Accelerating point defect simulations using data-driven and machine learning approaches

Arun Mannodi-Kanakkithodi; Menglin Huang; Prashun Gorai; Se\'an R. Kavanagh

Accelerating point defect simulations using data-driven and machine learning approaches

T0 review · 0 major / 2 minor · reviewed 2026-05-09 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Data-driven machine learning models trained on DFT calculations accelerate point defect simulations while retaining quantum-mechanical accuracy.

desk verdict This is a review summarizing a decade of ML work on point defect simulations, with no new methods or results of its own. read the letter →

arxiv 2604.21069 v1 pith:PDUNZKPR submitted 2026-04-22 cond-mat.mtrl-sci physics.chem-phphysics.comp-ph

Arun Mannodi-Kanakkithodi , Menglin Huang , Prashun Gorai , Seán R. Kavanagh This is my paper · ORCID

classification cond-mat.mtrl-sciphysics.chem-phphysics.comp-ph

keywords pointdefectsmachinelearningdensityfunctionaltheorysurrogatemodelsinteratomicpotentialsphononsdefectformationenergymaterialsscreening

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review outlines the growing use of machine learning to speed up simulations of point defects in solid materials. It covers descriptor-based screening models and especially surrogate models plus interatomic potentials fitted to density functional theory results. These tools deliver fast predictions of defect formation energies and structures in large supercells. They also compute phonon spectra and vibrational free energies to account for temperature effects. The goal is to make detailed defect modeling practical for materials screening and design.

What carries the argument

Surrogate models and interatomic potentials trained on DFT data for rapid defect energy and phonon calculations.

What would settle it

A new defect configuration or material where the ML-predicted formation energy or phonon spectrum differs substantially from a direct DFT computation.

Watch

Extended reading notes

Core claim

Surrogate models and interatomic potentials trained on density functional theory data can produce predictions of defect properties, including phonon modes and finite-temperature free energies, that match the accuracy of full quantum mechanical calculations but at a small fraction of the computational cost.

Load-bearing premise

Machine learning models trained on a limited set of DFT calculations will generalize reliably to new defect types and materials outside the training data.

Editorial extensions

If this is right

High-throughput screening of defect properties across many materials and configurations becomes feasible.
Defect energetics can be evaluated at finite temperatures through accurate vibrational contributions.
Computational resources are freed for exploring larger systems or more complex defect scenarios.
Links between theoretical predictions and experimental measurements of defects can be strengthened through faster data generation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Extending these models to defects in alloys or at interfaces could broaden their applicability in real materials.
Combining ML predictions with targeted experiments might create self-improving training loops for better generalization.
These accelerations might enable defect engineering in device-scale simulations that were previously too expensive.
Careful validation on diverse test sets remains essential to ensure reliability beyond the training data.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a review that summarizes efforts from the past decade on using data-driven and ML approaches to accelerate point defect simulations in solid-state materials. It doesn't introduce new methods or results. The strongest part is its description of how surrogate models and interatomic potentials trained on density functional theory data can deliver predictions with quantum-mechanical accuracies for things like defect formation energies and relaxations, but at a fraction of the usual cost. It also touches on using these for phonon modes and vibrational free energies to get defect energetics at finite temperatures, which is highlighted as an important direction. What stands out positively is the structure: starting with motivations, moving through descriptor-based models especially in oxides, then the case studies, and ending with links to experiment and future outlook. This makes it easy to follow for someone not deeply immersed in the subfield. On the downside, since no new data or validations are provided, the soundness rests on the quality of the referenced studies, which aren't re-evaluated here. The claims about accuracy are plausible but would benefit from more specific examples or error metrics in the text itself. There's also no deep dive into potential pitfalls like model generalization to unseen defects or materials. This paper is for computational materials scientists interested in defect modeling who want a concise overview of ML applications without reading dozens of individual papers. A reader already familiar with the cited works might not gain much, but newcomers or those in related areas could find it helpful for catching up. It deserves a serious referee. The work is timely given the growing interest in high-throughput materials discovery, and even as a review it organizes the literature in a way that could guide future research. I'd recommend sending it out for peer review rather than desk rejecting it.

Referee Report

0 major / 2 minor

Summary. The manuscript is an overview of prominent efforts to accelerate point defect simulations in solid-state materials using data-driven and machine learning approaches. It discusses motivations for these techniques, reviews descriptor-based models for rapid screening of defect properties especially in oxides, presents case studies of surrogate models and interatomic potentials trained on DFT data that achieve quantum-mechanical accuracies for formation energies, geometry relaxations, phonon modes, and vibrational free energies at finite temperatures, and covers connections to experimental data along with an outlook on the sub-field.

Significance. If the reviewed case studies hold as described, this overview is significant for highlighting how ML methods can deliver DFT-level accuracy for key defect properties at a fraction of the computational cost. This enables high-throughput screening and access to finite-temperature energetics via phonons and free energies, which the paper correctly identifies as a key frontier. The work provides a useful synthesis of progress over the past decade and gives appropriate credit to the underlying literature.

minor comments (2)

Consider adding a summary table of the key case studies reviewed, including reported error metrics (e.g., formation energy errors) and computational speedups relative to direct DFT, to enhance readability and facilitate quick comparisons for readers.
The abstract and introduction refer to 'quantum-mechanical accuracies' and 'a fraction of the cost' without providing even brief quantitative benchmarks or typical error ranges from the cited works; adding one or two concrete examples would strengthen the claims.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript, which accurately captures its scope as an overview of data-driven and machine learning approaches for accelerating point defect simulations. We appreciate the recognition of the significance of these methods for high-throughput screening and finite-temperature energetics, as well as the recommendation for minor revision.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: review summarizing external literature without new derivations

full rationale

This is a review/overview paper that discusses motivations for data-driven techniques, summarizes case studies from the past decade on descriptor-based models and interatomic potentials trained on DFT data, and reports on their reported capabilities for geometry relaxation, formation energies, phonons, and finite-temperature free energies. No original derivations, equations, or predictions are presented by the authors themselves; all quantitative claims about accuracies and cost reductions are explicitly attributed to cited external works. There are no self-definitional steps, fitted inputs called predictions, or load-bearing self-citations that reduce the central content to the paper's own inputs. The paper is self-contained as a descriptive summary and does not advance a new theorem or result that could be circular.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

As this is a review paper, there are no new free parameters, axioms, or invented entities introduced; all content draws from cited prior work.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Accelerating point defect simulations using data-driven and machine learning approaches." pith.science (2026). https://pith.science/paper/PDUNZKPR

@misc{pith2026260421069,
  author       = {Pith},
  title        = {Pith review of: Accelerating point defect simulations using data-driven and machine learning approaches},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PDUNZKPR}},
  note         = {Machine review of arXiv:2604.21069}
}

read the original abstract

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent efforts to accelerate defect simulations using these approaches. We begin by discussing the motivations for data-driven techniques in defect modeling, and describe efforts over the past decade to use descriptor-based models for rapid screening of defect properties -- most notably in oxides. In particular, we discuss case studies where surrogate models and interatomic potentials were trained on density functional theory (DFT) data, leading to predictions with quantum-mechanical accuracies at a fraction of the cost. In addition to geometry relaxation and formation energy predictions, these interatomic potentials are capable of predicting phonon modes and vibrational free energies to yield defect energetics at finite temperatures -- representing a key frontier for computational defect research. We finish with a discussion on how to connect these approaches and their outputs with experimental data, and provide an outlook on this burgeoning sub-field.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

143 extracted references · 143 canonical work pages

[1]

Defects in semiconductors,

L. Vines, E. Monakhov, and A. Kuznetsov, “Defects in semiconductors,” Journal of Applied Physics, vol. 132, p. 150401, 10 2022

work page 2022
[2]

Perspective on defect control in semiconductors for photovoltaics,

X. Cai and S.-H. Wei, “Perspective on defect control in semiconductors for photovoltaics,”Journal of Applied Physics, vol. 134, no. 22, p. 220901, 2023

work page 2023
[3]

The defect challenge of wide-bandgap semiconductors for photovoltaics and beyond,

A. M. Ganose, D. O. Scanlon, A. Walsh, and R. L. Z. Hoye, “The defect challenge of wide-bandgap semiconductors for photovoltaics and beyond,”Nature Communications, vol. 13, no. 1, p. 4715, 2022

work page 2022
[4]

Pycdt: A python toolkit for modeling point defects in semiconductors and insulators,

D. Broberg, B. Medasani, N. E. Zimmermann, G. Yu, A. Canning, M. Haranczyk, M. Asta, and G. Hautier, “Pycdt: A python toolkit for modeling point defects in semiconductors and insulators,”Computer Physics Communications, vol. 226, pp. 165–179, 2018. 25

work page 2018
[5]

Point defect engineering in thin-film solar cells,

J. S. Park, S. Kim, Z. Xie, and A. Walsh, “Point defect engineering in thin-film solar cells,”Nature Reviews Materials, vol. 3, pp. 194–210, 2018

work page 2018
[6]

Beginner’s guide to interpreting defect and defect level diagrams,

P. Gorai, “Beginner’s guide to interpreting defect and defect level diagrams,”PRX Energy, vol. 4, p. 032001, Aug 2025

work page 2025
[7]

Quantum Monte Carlo assessment of embedding for for strongly correlated defects: interplay between mean-field starting point and interactions

K. G. Kleiner, S. Joshi, W. Lee, A. Hampel, M. R¨ osner, C. E. Dreyer, and L. K. Wagner, “Quantum Monte Carlo assessment of embed- ding for a strongly-correlated defect: interplay between mean-field and interactions,” May 2025. arXiv:2505.00845 [cond-mat]

work page Pith review arXiv 2025
[8]

Accuracy of$GW$for calculating defect energy levels in solids,

W. Chen and A. Pasquarello, “Accuracy of$GW$for calculating defect energy levels in solids,”Physical Review B, vol. 96, p. 020101, July 2017

work page 2017

Show all 143 references

[9]

Real-space representation of the quasiparticle self-consistent G W self- energy and its application to defect calculations,

O. Dernek, D. Skachkov, W. R. L. Lambrecht, and M. van Schilfgaarde, “Real-space representation of the quasiparticle self-consistent G W self- energy and its application to defect calculations,”Physical Review B, vol. 105, p. 205136, May 2022

2022
[10]

On the dopability of semiconductors and governing material properties,

A. Goyal, P. Gorai, S. Anand, E. S. Toberer, G. J. Snyder, and V. Ste- vanovi´ c, “On the dopability of semiconductors and governing material properties,”Chemistry of Materials, vol. 32, no. 11, pp. 4467–4480, 2020

2020
[11]

Doping limits of phosphorus, arsenic, and antimony in cdte,

I. Chatratin, B. Dou, S.-H. Wei, and A. Janotti, “Doping limits of phosphorus, arsenic, and antimony in cdte,”The Journal of Physical Chemistry Letters, vol. 14, no. 1, pp. 273–278, 2023

2023
[12]

Dopants and defects in ultra-wide bandgap semiconductors,

J. L. Lyons, D. Wickramaratne, and A. Janotti, “Dopants and defects in ultra-wide bandgap semiconductors,”Current Opinion in Solid State and Materials Science, vol. 30, p. 101148, 2024

2024
[13]

Data-driven machine learn- ing model for the prediction of oxygen vacancy formation energy of metal oxide materials,

Z. Wan, Q.-D. Wang, D. Liu, and J. Liang, “Data-driven machine learn- ing model for the prediction of oxygen vacancy formation energy of metal oxide materials,”Phys. Chem. Chem. Phys., vol. 23, pp. 15675–15684, 2021

2021
[14]

Oxide enthalpy of formation and band gap energy as accurate descriptors of oxygen vacancy formation energetics,

A. M. Deml, V. Stevanovi´ c, C. L. Muhich, C. B. Musgrave, and R. O’Hayre, “Oxide enthalpy of formation and band gap energy as accurate descriptors of oxygen vacancy formation energetics,”Energy Environ. Sci., vol. 7, pp. 1996–2004, 2014

1996
[15]

Data-driven design of novel halide perovskite alloys,

A. Mannodi-Kanakkithodi and M. K. Y. Chan, “Data-driven design of novel halide perovskite alloys,”Energy Environ. Sci., vol. 15, pp. 1930– 1949, 2022. 26

1930
[16]

First-principles calculations for point defects in solids,

C. Freysoldt, B. Grabowski, T. Hickel, J. Neugebauer, G. Kresse, A. Jan- otti, and C. G. Van de Walle, “First-principles calculations for point defects in solids,”Reviews of Modern Physics, vol. 86, pp. 253–305, Mar. 2014

2014
[17]

Guidelines for robust and reproducible point defect simulations in crystals,

A. G. Squires, S. R. Kavanagh, A. Walsh, and D. O. Scanlon, “Guidelines for robust and reproducible point defect simulations in crystals,”Nature Reviews Materials, pp. 1–18, Jan. 2026

2026
[18]

Quantitative modeling of point defects inβ-Ga2O3 combining hybrid functional energetics with semicon- ductor and processes thermodynamics,

K. A. Arnab, M. Stephens, I. Maxfield, C. Lee, E. Ertekin, Y. K. Froda- son, J. B. Varley, and M. A. Scarpulla, “Quantitative modeling of point defects inβ-Ga2O3 combining hybrid functional energetics with semicon- ductor and processes thermodynamics,”Physical Chemistry Chemic...

2025
[19]

S. R. Kavanagh,Accurately Modelling Point Defects in Semiconduc- tors: The Case of CdTe. Doctoral, UCL (University College London), Jan. 2024. Publication Title: Doctoral thesis, UCL (University College London)

2024
[20]

Imperfections are not 0 K: free energy of point defects in crystals,

I. Mosquera-Lois, S. R. Kavanagh, J. Klarbring, K. Tolborg, and A. Walsh, “Imperfections are not 0 K: free energy of point defects in crystals,”Chemical Society Reviews, vol. 52, pp. 5812–5826, Aug. 2023

2023
[21]

Energetics of quaternary iii-v alloys described by incorporation and clustering of impurities,

K. Biswas and S. Lany, “Energetics of quaternary iii-v alloys described by incorporation and clustering of impurities,”Phys. Rev. B, vol. 80, p. 115206, Sep 2009

2009
[22]

Defect interactions and the role of complexes in the CdTe solar cell absorber,

D. Krasikov and I. Sankin, “Defect interactions and the role of complexes in the CdTe solar cell absorber,”Journal of Materials Chemistry A, vol. 5, pp. 3503–3513, Feb. 2017

2017
[23]

Cation disorder dominates the defect chemistry of high-voltage LiMn 1.5 Ni 0.5 O 4 (LMNO) spinel cathodes,

J. Cen, B. Zhu, S. R. Kavanagh, A. G. Squires, and D. O. Scanlon, “Cation disorder dominates the defect chemistry of high-voltage LiMn 1.5 Ni 0.5 O 4 (LMNO) spinel cathodes,”Journal of Materials Chemistry A, vol. 11, no. 25, pp. 13353–13370, 2023

2023
[24]

Beyond thermodynamic defect models: A kinetic simulation of arsenic activation in CdTe,

D. Krasikov and I. Sankin, “Beyond thermodynamic defect models: A kinetic simulation of arsenic activation in CdTe,”Physical Review Materials, vol. 2, p. 103803, Oct. 2018

2018
[25]

Universal machine learn- ing framework for defect predictions in zinc blende semiconductors,

A. Mannodi-Kanakkithodi, X. Xiang, L. Jacoby, R. Biegaj, S. T. Dunham, D. R. Gamelin, and M. K. Chan, “Universal machine learn- ing framework for defect predictions in zinc blende semiconductors,” Patterns, vol. 3, no. 3, 2022. 27

2022
[26]

High-throughput calculations of charged point defect properties with semi-local density functional the- ory—performance benchmarks for materials screening applications,

D. Broberg, K. Bystrom, S. Srivastava, D. Dahliah, B. A. D. Williamson, L. Weston, D. O. Scanlon, G.-M. Rignanese, S. Dwaraknath, J. Varley, K. A. Persson, M. Asta, and G. Hautier, “High-throughput calculations of charged point defect properties with semi-local density functio...

2023
[27]

Computationally predicted energies and properties of defects in gan,

J. L. Lyons and C. G. Van de Walle, “Computationally predicted energies and properties of defects in gan,”npj Computational Materials, vol. 3, no. 1, p. 12, 2017

2017
[28]

Iden- tifying the ground state structures of point defects in solids,

I. Mosquera-Lois, S. R. Kavanagh, A. Walsh, and D. O. Scanlon, “Iden- tifying the ground state structures of point defects in solids,”npj Computational Materials, vol. 9, no. 1, p. 25, 2023

2023
[29]

Stability and molecu- lar pathways to the formation of spin defects in silicon carbide,

E. M. Y. Lee, A. Yu, J. J. de Pablo, and G. Galli, “Stability and molecu- lar pathways to the formation of spin defects in silicon carbide,”Nature Communications, vol. 12, no. 1, p. 6325, 2021

2021
[30]

One-defect one-potential strategy for accurate machine learning prediction of phonons in defect- containing supercells,

J. Zhou, X. Li, M. Huang, and S. Chen, “One-defect one-potential strategy for accurate machine learning prediction of phonons in defect- containing supercells,”Phys. Rev. B, vol. 112, p. 235205, Dec 2025

2025
[31]

Dft+u studies of cu dop- ing and p-type compensation in crystalline and amorphous zns,

H. H. Pham, G. T. Barkema, and L.-W. Wang, “Dft+u studies of cu dop- ing and p-type compensation in crystalline and amorphous zns,”Phys. Chem. Chem. Phys., vol. 17, pp. 26270–26276, 2015

2015
[32]

Self- compensation in phosphorus-doped cdte,

M. A. Flores, W. Orellana, and E. Men´ endez-Proupin, “Self- compensation in phosphorus-doped cdte,”Phys. Rev. B, vol. 96, p. 134115, Oct 2017

2017
[33]

Theoretical study of intrinsic defects in cubic silicon carbide 3c-sic,

P. A. Schultz, R. M. Van Ginhoven, and A. H. Edwards, “Theoretical study of intrinsic defects in cubic silicon carbide 3c-sic,”Phys. Rev. B, vol. 103, p. 195202, May 2021

2021
[34]

Caution! static supercell calculations of defect migration in higher symmetry abx3 perovskite halides may be unreliable: A case study of methylammonium lead iodide,

R. A. De Souza, D. Kemp, M. J. Wolf, and A. H. H. Ramadan, “Caution! static supercell calculations of defect migration in higher symmetry abx3 perovskite halides may be unreliable: A case study of methylammonium lead iodide,”The Journal of Physical Chemistry Letters, vol. 13, ...

2022
[35]

Evolutionary computing and machine learning for discovering of low-energy defect configurations,

M. Arrigoni and G. K. H. Madsen, “Evolutionary computing and machine learning for discovering of low-energy defect configurations,” npj Computational Materials, vol. 7, pp. 1–13, May 2021. Number: 1

2021
[36]

Anion vacancies as a source of persistent photo- conductivity in II-VI and chalcopyrite semiconductors,

S. Lany and A. Zunger, “Anion vacancies as a source of persistent photo- conductivity in II-VI and chalcopyrite semiconductors,”Physical Review 28 B, vol. 72, p. 035215, July 2005

2005
[37]

doped: Python toolkit for robust and repeatable charged defect supercell calculations,

S. R. Kavanagh, A. G. Squires, A. Nicolson, I. Mosquera-Lois, A. M. Ganose, B. Zhu, K. Brlec, A. Walsh, and D. O. Scanlon, “doped: Python toolkit for robust and repeatable charged defect supercell calculations,” Journal of Open Source Software, vol. 9, p. 6433, Apr. 2024

2024
[38]

Shak- eNBreak: Navigating the defect configurational landscape,

I. Mosquera-Lois, S. R. Kavanagh, A. Walsh, and D. O. Scanlon, “Shak- eNBreak: Navigating the defect configurational landscape,”Journal of Open Source Software, vol. 7, p. 4817, Dec. 2022

2022
[39]

Rapid Recombination by Cadmium Vacancies in CdTe,

S. R. Kavanagh, A. Walsh, and D. O. Scanlon, “Rapid Recombination by Cadmium Vacancies in CdTe,”ACS Energy Letters, vol. 6, pp. 1392– 1398, Apr. 2021

2021
[40]

Machine-learning structural reconstructions for accelerated point defect calculations,

I. Mosquera-Lois, S. R. Kavanagh, A. M. Ganose, and A. Walsh, “Machine-learning structural reconstructions for accelerated point defect calculations,”npj Computational Materials, vol. 10, pp. 1–9, June 2024

2024
[41]

Identifying split vacancy defects with machine-learned foundation models and electrostatics,

S. R. Kavanagh, “Identifying split vacancy defects with machine-learned foundation models and electrostatics,”Journal of Physics: Energy, vol. 7, p. 045002, July 2025

2025
[43]

Point defects in ga2o3,

M. D. McCluskey, “Point defects in ga2o3,”Journal of Applied Physics, vol. 127, p. 101101, 03 2020

2020
[44]

Comparative study of as and cu doping stability in cdsete absorbers,

D. Krasikov, D. Guo, S. Demtsu, and I. Sankin, “Comparative study of as and cu doping stability in cdsete absorbers,”Solar Energy Materials and Solar Cells, vol. 224, p. 111012, 2021

2021
[45]

Point defect formation at finite temperatures with machine learning force fields,

I. Mosquera-Lois, J. Klarbring, and A. Walsh, “Point defect formation at finite temperatures with machine learning force fields,”Chemical Science, Apr. 2025

2025
[46]

Machine learning in materials informatics: recent applications and prospects,

R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, “Machine learning in materials informatics: recent applications and prospects,”npj Computational Materials, vol. 3, no. 1, p. 54, 2017

2017
[47]

Recent advances and applications of machine learning in solid-state materials science,

J. Schmidt, M. R. G. Marques, S. Botti, and M. A. L. Marques, “Recent advances and applications of machine learning in solid-state materials science,”npj Computational Materials, vol. 5, no. 1, p. 83, 2019. 29

2019
[48]

Intrinsic material properties dictating oxygen vacancy formation energetics in metal oxides,

A. M. Deml, A. M. Holder, R. P. O’Hayre, C. B. Musgrave, and V. Stevanovi´ c, “Intrinsic material properties dictating oxygen vacancy formation energetics in metal oxides,”The Journal of Physical Chemistry Letters, vol. 6, no. 10, pp. 1948–1953, 2015

1948
[49]

Factors governing oxygen vacancy formation in oxide perovskites,

R. B. Wexler, G. S. Gautam, E. B. Stechel, and E. A. Carter, “Factors governing oxygen vacancy formation in oxide perovskites,”Journal of the American Chemical Society, vol. 143, no. 33, pp. 13212–13227, 2021

2021
[50]

Simple structural descriptor obtained from symbolic classification for predicting the oxygen vacancy defect formation of perovskites,

S. Liu, J. Wang, Z. Duan, K. Wang, W. Zhang, R. Guo, and F. Xie, “Simple structural descriptor obtained from symbolic classification for predicting the oxygen vacancy defect formation of perovskites,”ACS Applied Materials & Interfaces, vol. 14, no. 9, pp. 11758–11767, 2022

2022
[51]

A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide,

D. Wang, R. Zan, X. Zhu, Y. Zhang, Y. Wang, Y. Gu, and Y. Li, “A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide,”RSC Adv., vol. 14, pp. 33198–33205, 2024

2024
[52]

Machine learning-assisted dft reveals key descriptors governing the vacancy formation energy in pd-substituted multicomponent ceria,

P. Pentyala, V. Singhania, V. K. Duggineni, and P. A. Deshpande, “Machine learning-assisted dft reveals key descriptors governing the vacancy formation energy in pd-substituted multicomponent ceria,” Molecular Catalysis, vol. 522, p. 112190, 2022

2022
[53]

Insights into oxygen vacancies from high-throughput first-principles calculations,

Y. Kumagai, N. Tsunoda, A. Takahashi, and F. Oba, “Insights into oxygen vacancies from high-throughput first-principles calculations,” Physical Review Materials, vol. 5, p. 123803, Dec. 2021

2021
[54]

Machine Learning Pre- diction of Charged Defect Formation Energies from Crystal Structures,

S. Kiyohara, C. Shibui, S. Bae, and Y. Kumagai, “Machine Learning Pre- diction of Charged Defect Formation Energies from Crystal Structures,” Oct. 2025. arXiv:2510.00513 [cond-mat]

2025
[55]

Descriptor-based approach for the prediction of cation vacancy formation energies and transi- tion levels,

J. B. Varley, A. Samanta, and V. Lordi, “Descriptor-based approach for the prediction of cation vacancy formation energies and transi- tion levels,”The Journal of Physical Chemistry Letters, vol. 8, no. 20, pp. 5059–5063, 2017

2017
[56]

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets,

M. P. Polak, R. Jacobs, A. Mannodi-Kanakkithodi, M. K. Y. Chan, and D. Morgan, “Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets,”The Journal of Chemical Physics, vol. 156, p. 114110, 03 2022

2022
[57]

Defect graph neural networks for materials discovery in high- temperature clean-energy applications,

M. D. Witman, A. Goyal, T. Ogitsu, A. H. McDaniel, and S. Lany, “Defect graph neural networks for materials discovery in high- temperature clean-energy applications,”Nature Computational Science, vol. 3, pp. 675–686, Aug. 2023. 30

2023
[58]

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties,

T. Xie and J. C. Grossman, “Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties,”Physical Review Letters, vol. 120, p. 145301, Apr. 2018

2018
[59]

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications,

L. Way, C. D. Spataru, R. E. Jones, D. R. Trinkle, A. J. E. Rowberg, J. B. Varley, R. B. Wexler, C. M. Smyth, T. C. Douglas, S. R. Bishop, E. J. Fuller, A. H. McDaniel, S. Lany, and M. D. Witman, “Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperatu...

2025
[60]

Machine-learned impurity level pre- diction for semiconductors: the example of cd-based chalcogenides,

A. Mannodi-Kanakkithodi, M. Y. Toriyama, F. G. Sen, M. J. Davis, R. F. Klie, and M. K. Y. Chan, “Machine-learned impurity level pre- diction for semiconductors: the example of cd-based chalcogenides,”npj Computational Materials, vol. 6, no. 1, p. 39, 2020

2020
[61]

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning,

A. Mannodi-Kanakkithodi and M. K. Chan, “Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning,”Journal of Materials Science, vol. 57, no. 23, pp. 10736–10754, 2022

2022
[62]

Modified band alignment method to obtain hybrid functional accuracy from standard dft: Application to defects in highly mismatched iii-v:bi alloys,

M. P. Polak, R. Kudrawiec, R. Jacobs, I. Szlufarska, and D. Morgan, “Modified band alignment method to obtain hybrid functional accuracy from standard dft: Application to defects in highly mismatched iii-v:bi alloys,”Phys. Rev. Mater., vol. 5, p. 124601, Dec 2021

2021
[63]

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects,

X. Wang, I. Mosquera-Lois, and A. Walsh, “Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects,” Mar. 2026. arXiv:2603.05238 [cond-mat]

2026
[64]

Machine learning accelerated study of defect energy levels in perovskites,

X. Wu, H. Chen, J. Wang, and X. Niu, “Machine learning accelerated study of defect energy levels in perovskites,”The Journal of Physical Chemistry C, vol. 127, no. 23, pp. 11387–11395, 2023

2023
[65]

Defect formation in cssni3 from density functional theory and machine learning,

C. Khamdang and M. Wang, “Defect formation in cssni3 from density functional theory and machine learning,”J. Mater. Chem. C, vol. 13, pp. 7550–7557, 2025

2025
[66]

Generalized Neural-Network Representa- tion of High-Dimensional Potential-Energy Surfaces,

J. Behler and M. Parrinello, “Generalized Neural-Network Representa- tion of High-Dimensional Potential-Energy Surfaces,”Physical Review Letters, vol. 98, p. 146401, Apr. 2007

2007
[67]

Cross- disciplinary perspectives on the potential for artificial intelligence across 31 chemistry,

A. M. Mroz, A. R. Basford, F. Hastedt, I. S. Jayasekera, I. Mosquera- Lois, R. Sedgwick, P. J. Ballester, J. D. Bocarsly, E. A. d. R. Chanona, M. L. Evans, J. M. Frost, A. M. Ganose, R. L. Greenaway, K. K. M. Hii, Y. Li, R. Misener, A. Walsh, D. Zhang, and K. E. Jelfs, “Cross-...

2025
[68]

Data Generation for Machine Learning Interatomic Poten- tials and Beyond,

M. Kulichenko, B. Nebgen, N. Lubbers, J. S. Smith, K. Barros, A. E. A. Allen, A. Habib, E. Shinkle, N. Fedik, Y. W. Li, R. A. Messerly, and S. Tretiak, “Data Generation for Machine Learning Interatomic Poten- tials and Beyond,”Chemical Reviews, vol. 124, pp. 13681–13714, Dec. 2024

2024
[69]

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science,

V. L. Deringer, M. A. Caro, and G. Cs´ anyi, “Machine Learning Interatomic Potentials as Emerging Tools for Materials Science,” Advanced Materials, vol. 31, no. 46, p. 1902765, 2019. eprint: https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201902765

2019 doi
[70]

How to vali- date machine-learned interatomic potentials,

J. D. Morrow, J. L. A. Gardner, and V. L. Deringer, “How to vali- date machine-learned interatomic potentials,”The Journal of Chemical Physics, vol. 158, p. 121501, Mar. 2023

2023
[71]

A practical guide to machine learning interatomic potentials – Status and future,

R. Jacobs, D. Morgan, S. Attarian, J. Meng, C. Shen, Z. Wu, C. Y. Xie, J. H. Yang, N. Artrith, B. Blaiszik, G. Ceder, K. Choudhary, G. Csanyi, E. D. Cubuk, B. Deng, R. Drautz, X. Fu, J. Godwin, V. Honavar, O. Isayev, A. Johansson, B. Kozinsky, S. Martiniani, S. P. Ong, I. Polt...

2025
[72]

Machine Learning Force Fields,

O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Sch¨ utt, A. Tkatchenko, and K.-R. M¨ uller, “Machine Learning Force Fields,”Chemical Reviews, vol. 121, pp. 10142–10186, Aug. 2021

2021
[73]

Explicit Mul- tielement Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems,

M. A. Cusentino, M. A. Wood, and A. P. Thompson, “Explicit Mul- tielement Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems,”The Journal of Physical Chemistry A, vol. 124, pp. 5456–5464, July 2020

2020
[74]

Systematic softening in universal machine learn- ing interatomic potentials,

B. Deng, Y. Choi, P. Zhong, J. Riebesell, S. Anand, Z. Li, K. Jun, K. A. Persson, and G. Ceder, “Systematic softening in universal machine learn- ing interatomic potentials,”npj Computational Materials, vol. 11, p. 9, Jan. 2025

2025
[75]

Universal machine learning interatomic potentials poised to supplant DFT in modeling gen- eral defects in metals and random alloys,

F. Shuang, Z. Wei, K. Liu, W. Gao, and P. Dey, “Universal machine learning interatomic potentials poised to supplant DFT in modeling gen- eral defects in metals and random alloys,”Machine Learning: Science and Technology, vol. 6, p. 030501, July 2025

2025
[76]

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties,

D. Wines and K. Choudhary, “CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties,”ACS Materials Letters, 32 vol. 7, pp. 2105–2114, June 2025

2025
[77]

Non-collinear magnetic atomic cluster expansion for iron,

M. Rinaldi, M. Mrovec, A. Bochkarev, Y. Lysogorskiy, and R. Drautz, “Non-collinear magnetic atomic cluster expansion for iron,”npj Compu- tational Materials, vol. 10, p. 12, Jan. 2024

2024
[78]

A framework to eval- uate machine learning crystal stability predictions,

J. Riebesell, R. E. A. Goodall, P. Benner, Y. Chiang, B. Deng, G. Ceder, M. Asta, A. A. Lee, A. Jain, and K. A. Persson, “A framework to eval- uate machine learning crystal stability predictions,”Nature Machine Intelligence, vol. 7, pp. 836–847, June 2025

2025
[79]

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,

I. Batatia, D. P. Kovacs, G. Simm, C. Ortner, and G. Csanyi, “MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,”Advances in Neural Information Processing Systems, vol. 35, pp. 11423–11436, Dec. 2022

2022
[80]

The Materials Project: A materials genome approach to accelerating materials innovation,

A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. a. Persson, “The Materials Project: A materials genome approach to accelerating materials innovation,”APL Materials, vol. 1, no. 1, p. 011002, 2013

2013
[81]

Thermal Stabilization of Defect Charge States and Finite-Temperature Charge Transition Levels,

T. Hainer, E. Berger, E. Berger, O. Hildeberg, P. Erhart, and J. Wiktor, “Thermal Stabilization of Defect Charge States and Finite-Temperature Charge Transition Levels,” Dec. 2025. arXiv:2512.15463 [cond-mat]

2025
[82]

Machine-learning driven global optimization of surface adsorbate geometries,

H. Jung, L. Sauerland, S. Stocker, K. Reuter, and J. T. Margraf, “Machine-learning driven global optimization of surface adsorbate geometries,”npj Computational Materials, vol. 9, p. 114, June 2023

2023
[83]

Accelerating CO2 direct air cap- ture screening for metal-organic frameworks with a transferable machine learning force field,

Y. Lim, H. Park, A. Walsh, and J. Kim, “Accelerating CO2 direct air cap- ture screening for metal-organic frameworks with a transferable machine learning force field,”Matter, vol. 8, July 2025

2025
[84]

Iden- tifying the ground state structures of point defects in solids,

I. Mosquera-Lois, S. R. Kavanagh, A. Walsh, and D. O. Scanlon, “Iden- tifying the ground state structures of point defects in solids,”npj Computational Materials, vol. 9, pp. 1–11, Feb. 2023. Number: 1

2023
[85]

Gaussian Process Regression for Materials and Molecules,

V. L. Deringer, A. P. Bart´ ok, N. Bernstein, D. M. Wilkins, M. Ceri- otti, and G. Cs´ anyi, “Gaussian Process Regression for Materials and Molecules,”Chemical Reviews, vol. 121, pp. 10073–10141, Aug. 2021

2021
[86]

Origins of structural and electronic transitions in disordered silicon,

V. L. Deringer, N. Bernstein, G. Cs´ anyi, C. Ben Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, and S. R. Elliott, “Origins of structural and electronic transitions in disordered silicon,”Nature, vol. 589, pp. 59–64, Jan. 2021. 33

2021
[87]

Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning,

J. D. Morrow, C. Ugwumadu, D. A. Drabold, S. R. Elliott, A. L. Goodwin, and V. L. Deringer, “Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning,”Ange- wandte Chemie, vol. 136, no. 22, p. e202403842, 2024. eprint: https://onlinelibrary...

2024 doi
[88]

Advancing molecular simu- lation with equivariant interatomic potentials,

S. Batzner, A. Musaelian, and B. Kozinsky, “Advancing molecular simu- lation with equivariant interatomic potentials,”Nature Reviews Physics, vol. 5, pp. 437–438, July 2023

2023
[89]

The design space of E(3)-equivariant atom-centred interatomic potentials,

I. Batatia, S. Batzner, D. P. Kov´ acs, A. Musaelian, G. N. C. Simm, R. Drautz, C. Ortner, B. Kozinsky, and G. Cs´ anyi, “The design space of E(3)-equivariant atom-centred interatomic potentials,”Nature Machine Intelligence, vol. 7, pp. 56–67, Jan. 2025

2025
[90]

The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models,

D. S. Levine, M. Shuaibi, E. W. C. Spotte-Smith, M. G. Taylor, M. R. Hasyim, K. Michel, I. Batatia, G. Cs´ anyi, M. Dzamba, P. Eastman, N. C. Frey, X. Fu, V. Gharakhanyan, A. S. Krishnapriyan, J. A. Rackers, S. Raja, A. Rizvi, A. S. Rosen, Z. Ulissi, S. Vargas, C. L. Zitnick, ...

2025
[91]

A universal graph deep learning interatomic potential for the periodic table,

C. Chen and S. P. Ong, “A universal graph deep learning interatomic potential for the periodic table,”Nature Computational Science, vol. 2, pp. 718–728, Nov. 2022

2022
[92]

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,

I. Batatia, D. P. Kov´ acs, G. N. C. Simm, C. Ortner, and G. Cs´ anyi, “MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,” Jan. 2023. arXiv:2206.07697 [cond-mat, physics:physics, stat]

2023
[93]

High-performance training and inference for deep equivariant interatomic potentials,

C. W. Tan, M. L. Descoteaux, M. Kotak, G. d. M. Nascimento, S. R. Kavanagh, L. Zichi, M. Wang, A. Saluja, Y. R. Hu, T. Smidt, A. Johans- son, W. C. Witt, B. Kozinsky, and A. Musaelian, “High-performance training and inference for deep equivariant interatomic potentials,” Apr
[94]

arXiv:2504.16068 [physics]
[95]

Metastable struc- tures of cation vacancies in semiconducting oxides,

W. B. Fowler, M. Stavola, A. Venzie, and A. Portoff, “Metastable struc- tures of cation vacancies in semiconducting oxides,”Journal of Applied Physics, vol. 135, p. 170901, May 2024

2024
[96]

Machine learning potential assisted exploration of complex defect potential energy surfaces,

C. Jiang, C. A. Marianetti, M. Khafizov, and D. H. Hurley, “Machine learning potential assisted exploration of complex defect potential energy surfaces,”npj Computational Materials, vol. 10, p. 21, Jan. 2024

2024
[97]

DeFecT-FF: Accel- erated Modeling of Defects in Cd-Zn–Te-Se-S Compounds Combining 34 High-Throughput DFT and Machine Learning Force Fields,

M. H. Rahman and A. Mannodi-Kanakkithodi, “DeFecT-FF: Accel- erated Modeling of Defects in Cd-Zn–Te-Se-S Compounds Combining 34 High-Throughput DFT and Machine Learning Force Fields,” Oct. 2025. arXiv:2510.23514 [cond-mat]

2025
[98]

Under- standing Defect-Mediated Ion Migration in Semiconductors using Atom- istic Simulations and Machine Learning,

M. H. Rahman, M. Biswas, and A. Mannodi-Kanakkithodi, “Under- standing Defect-Mediated Ion Migration in Semiconductors using Atom- istic Simulations and Machine Learning,”ACS Materials Au, vol. 4, pp. 557–573, Nov. 2024

2024
[99]

How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields,

M. Pols, V. Brouwers, S. Calero, and S. Tao, “How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields,”Chemical Communications, vol. 59, pp. 4660–4663, Apr. 2023

2023
[100]

Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W,

A. M. Goryaeva, J. D´ er` es, C. Lapointe, P. Grigorev, T. D. Swinburne, J. R. Kermode, L. Ventelon, J. Baima, and M.-C. Marinica, “Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W,”Physical Review Materials, vol. 5, ...

2021
[101]

Specialising neural network potentials for accurate properties and appli- cation to the mechanical response of titanium,

T. Wen, R. Wang, L. Zhu, L. Zhang, H. Wang, D. J. Srolovitz, and Z. Wu, “Specialising neural network potentials for accurate properties and appli- cation to the mechanical response of titanium,”npj Computational Materials, vol. 7, p. 206, Dec. 2021

2021
[102]

Machine learning interatomic potentials accelerate defect exploration in amorphous silica,

X. Li, X. Guo, M. Huang, X.-G. Gong, and S. Chen, “Machine learning interatomic potentials accelerate defect exploration in amorphous silica,” Physical Review Materials, vol. 10, p. 015003, Jan. 2026

2026
[103]

Screening of material defects using universal machine-learning interatomic potentials,

E. Berger, M. Bagheri, and H.-P. Komsa, “Screening of material defects using universal machine-learning interatomic potentials,” Apr. 2025. arXiv:2504.06993 [cond-mat]

2025
[104]

Thermal Con- ductivity Predictions with Foundation Atomistic Models,

B. P´ ota, P. Ahlawat, G. Cs´ anyi, and M. Simoncelli, “Thermal Con- ductivity Predictions with Foundation Atomistic Models,” Sept. 2024. arXiv:2408.00755 [cond-mat] version: 4

2024
[105]

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts,

R. Tran, J. Lan, M. Shuaibi, B. M. Wood, S. Goyal, A. Das, J. Heras- Domingo, A. Kolluru, A. Rizvi, N. Shoghi, A. Sriram, F. Therrien, J. Abed, O. Voznyy, E. H. Sargent, Z. Ulissi, and C. L. Zitnick, “The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocataly...

2022
[106]

CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis,

J. Moon, U. Jeon, S. Choung, and J. W. Han, “CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis,”Cell Reports Physical Science, vol. 6, Dec. 2025. 35

2025
[107]

Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids,

B. Cheng and M. Ceriotti, “Computing the absolute Gibbs free energy in atomistic simulations: Applications to defects in solids,”Physical Review B, vol. 97, p. 054102, Feb. 2018

2018
[108]

Impact of metastable defect structures on carrier recombination in solar cells,

S. R. Kavanagh, D. O. Scanlon, A. Walsh, and C. Freysoldt, “Impact of metastable defect structures on carrier recombination in solar cells,” Faraday Discussions, vol. 239, pp. 339–356, Oct. 2022

2022
[109]

Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}} {3}$Obey Equilibrium Defect Thermodynamics,

I. Mosquera-Lois and A. Walsh, “Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}} {3}$Obey Equilibrium Defect Thermodynamics,”PRX Energy, vol. 4, p. 043008, Nov. 2025

2025
[110]

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks,

X. Zhu, P. Rinke, and D. A. Egger, “Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks,” Nov. 2025. arXiv:2511.18398 [cond-mat]

2025
[111]

General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian,

X. Gong, H. Li, N. Zou, R. Xu, W. Duan, and Y. Xu, “General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian,”Nature Communications, vol. 14, p. 2848, May 2023

2023
[112]

ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs,

E. Dramko, Y. Xiong, Y. Zhu, G. Hautier, T. Reps, C. Jermaine, and A. Kyrillidis, “ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs,” Sept. 2025. arXiv:2509.24115 [cs]

2025
[113]

The Case for a Defect Genome Initiative,

Q. Yan, S. Kar, S. Chowdhury, and A. Bansil, “The Case for a Defect Genome Initiative,”Advanced Materials, vol. 36, p. 2303098, Mar. 2024

2024
[115]

Atomate2: modular work- flows for materials science,

A. M. Ganose, H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D. C. Chrzan, J. Clary, O. A. Cohen, C. Ertural, M. C. Gallant, J. George, S. Ger- its, R. E. A. Goodall, R. D. Guha, G. Hautier, M. Horton, T. J. Inizan, A. D. Kaplan...

1944
[116]

AiiDA 1.0, a scalable computational infrastructure for auto- mated reproducible workflows and data provenance,

S. P. Huber, S. Zoupanos, M. Uhrin, L. Talirz, L. Kahle, R. H¨ auselmann, D. Gresch, T. M¨ uller, A. V. Yakutovich, C. W. Andersen, F. F. Ramirez, C. S. Adorf, F. Gargiulo, S. Kumbhar, E. Passaro, C. John- ston, A. Merkys, A. Cepellotti, N. Mounet, N. Marzari, B. Kozinsky, and...

2020
[117]

DASP: Defect and Dopant ab-initio Simulation Package,

M. Huang, Z. Zheng, Z. Dai, X. Guo, S. Wang, L. Jiang, J. Wei, and S. Chen, “DASP: Defect and Dopant ab-initio Simulation Package,” Journal of Semiconductors, vol. 43, p. 042101, Apr. 2022

2022
[118]

MLIP Arena: Advancing Fairness and Trans- parency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform,

Y. Chiang, T. Kreiman, C. Zhang, M. C. Kuner, E. Weaver, I. Amin, H. Park, Y. Lim, J. Kim, D. Chrzan, A. Walsh, S. M. Blau, M. Asta, and A. S. Krishnapriyan, “MLIP Arena: Advancing Fairness and Trans- parency in Machine Learning Interatomic Potentials via an Open, Accessible B...

2025
[119]

An automated framework for exploring and learning potential-energy surfaces,

Y. Liu, J. D. Morrow, C. Ertural, N. L. Fragapane, J. L. A. Gardner, A. A. Naik, Y. Zhou, J. George, and V. L. Deringer, “An automated framework for exploring and learning potential-energy surfaces,”Nature Communications, vol. 16, p. 7666, Aug. 2025

2025
[120]

Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets,

P. Huang, R. Lukin, M. Faleev, N. Kazeev, A. R. Al-Maeeni, D. V. Andreeva, A. Ustyuzhanin, A. Tormasov, A. H. Castro Neto, and K. S. Novoselov, “Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets,”npj 2D Materials ...

2023
[121]

Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures,

T. Angsten, T. Mayeshiba, H. Wu, and D. Morgan, “Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures,”New Journal of Physics, vol. 16, p. 015018, Jan. 2014

2014
[122]

Two-dimensional materials by large-scale computations and chemical exfoliation of layered solids,

J. Bj{\”o}rk, J. Zhou, P. O.\. Persson, and J. Rosen, “Two-dimensional materials by large-scale computations and chemical exfoliation of layered solids,”Science, vol. 383, pp. 1210–1215, Mar. 2024

2024
[123]

Using neural network potentials to study defect formation and phonon properties of nitrogen vacancies with multiple charge states in gan,

K. Shimizu, Y. Dou, E. F. Arguelles, T. Moriya, E. Minamitani, and S. Watanabe, “Using neural network potentials to study defect formation and phonon properties of nitrogen vacancies with multiple charge states in gan,”Phys. Rev. B, vol. 106, p. 054108, Aug 2022

2022
[124]

Machine- learning potential for phonon transport in aln with defects in multiple charge states,

Y. Dou, K. Shimizu, J. Carrete, H. Fujioka, and S. Watanabe, “Machine- learning potential for phonon transport in aln with defects in multiple charge states,”Phys. Rev. Mater., vol. 9, p. 034601, Mar 2025. 37

2025
[125]

Pre- diction of phonon properties of cubic boron nitride with vacancy defects and isotopic disorders by using a neural network potential,

J. Zhang, J. Zhang, G. Bao, Z. Li, X. Li, T.-H. Liu, and R. Yang, “Pre- diction of phonon properties of cubic boron nitride with vacancy defects and isotopic disorders by using a neural network potential,”Applied Physics Letters, vol. 124, p. 152206, 04 2024

2024
[126]

Optical line shapes of color centers in solids from classical autocorrelation functions,

C. Linder¨ alv, N. ¨Osterbacka, J. Wiktor, and P. Erhart, “Optical line shapes of color centers in solids from classical autocorrelation functions,” npj Computational Materials, vol. 11, no. 1, p. 101, 2025

2025
[127]

Machine learning phonon spectra for fast and accurate optical lineshapes of defects,

M. E. Turiansky, J. L. Lyons, and N. Bernstein, “Machine learning phonon spectra for fast and accurate optical lineshapes of defects,” 2025

2025
[128]

First-principles theory of the luminescence lineshape for the triplet tran- sition in diamond nv centres,

A. Alkauskas, B. B. Buckley, D. D. Awschalom, and C. G. Van de Walle, “First-principles theory of the luminescence lineshape for the triplet tran- sition in diamond nv centres,”New Journal of Physics, vol. 16, p. 073026, jul 2014

2014
[129]

First-principles theory of nonradiative carrier capture via multiphonon emission,

A. Alkauskas, Q. Yan, and C. G. Van de Walle, “First-principles theory of nonradiative carrier capture via multiphonon emission,”Phys. Rev. B, vol. 90, p. 075202, Aug 2014

2014
[130]

Defect phonon renormalization during nonradiative multiphonon transitions in semiconductors,

J. Zhou, S. Wang, M. Huang, X.-G. Gong, and S. Chen, “Defect phonon renormalization during nonradiative multiphonon transitions in semiconductors,”Phys. Rev. B, vol. 111, p. 115202, Mar 2025

2025
[131]

Accelerating point defect photo-emission calcula- tions with machine learning interatomic potentials,

K. Sharma, A. Loew, H. Wang, F. A. Nilsson, M. Jain, M. A. Marques, and K. S. Thygesen, “Accelerating point defect photo-emission calcula- tions with machine learning interatomic potentials,”npj Computational Materials, vol. 11, no. 1, p. 334, 2025

2025
[132]

Accurate gap levels and their role in the reliability of other calculated defect properties,

P. De´ ak, A. Gali, B. Aradi, and T. Frauenheim, “Accurate gap levels and their role in the reliability of other calculated defect properties,” physica status solidi (b), vol. 248, no. 4, pp. 790–798, 2011. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pssb.201046210

2011 doi
[133]

Photoluminescence spectra of point defects in semiconductors: Validation of first-principles calculations,

Y. Jin, M. Govoni, G. Wolfowicz, S. E. Sullivan, F. J. Heremans, D. D. Awschalom, and G. Galli, “Photoluminescence spectra of point defects in semiconductors: Validation of first-principles calculations,”Physical Review Materials, vol. 5, p. 084603, Aug. 2021

2021
[134]

First- Principles Calculations of Luminescence Spectrum Line Shapes for Defects in Semiconductors: The Example of GaN and ZnO,

A. Alkauskas, J. L. Lyons, D. Steiauf, and C. G. Van de Walle, “First- Principles Calculations of Luminescence Spectrum Line Shapes for Defects in Semiconductors: The Example of GaN and ZnO,”Physical Review Letters, vol. 109, p. 267401, Dec. 2012

2012
[135]

Distinguishing atomic vibrations near point defects,

X. Yan, “Distinguishing atomic vibrations near point defects,”Nature Materials, vol. 22, pp. 540–541, May 2023. Number: 5. 38

2023
[136]

Photoionization of negatively charged NV centers in dia- mond: Theory and ab initio calculations,

L. Razinkovas, M. Maciaszek, F. Reinhard, M. W. Doherty, and A. Alkauskas, “Photoionization of negatively charged NV centers in dia- mond: Theory and ab initio calculations,”Physical Review B, vol. 104, p. 235301, Dec. 2021

2021
[137]

Small hole polaron in CdTe: Cd-vacancy revisited,

A. Shepidchenko, B. Sanyal, M. Klintenberg, and S. Mirbt, “Small hole polaron in CdTe: Cd-vacancy revisited,”Scientific Reports, vol. 5, pp. 1– 6, Sept. 2015. Number: 1

2015
[138]

An x-ray diffraction technique for analyzing structural defects including microstrain in nitride materials,

Q. Paduano, D. Weyburne, and A. Drehman, “An x-ray diffraction technique for analyzing structural defects including microstrain in nitride materials,”Journal of Crystal Growth, vol. 318, no. 1, pp. 418– 422, 2011. The 16th International Conference on Crystal Growth (ICCG16)/Th...

2011
[139]

Dynam- ical x-ray diffraction theory: Characterization of defects and strains in as-grown and ion-implanted garnet structures,

S. I. Olikhovskii, V. B. Molodkin, O. S. Skakunova, E. G. Len, Y. M. Kyslovskyy, T. P. Vladimirova, O. V. Reshetnyk, E. V. Kochelab, S. V. Lizunova, V. M. Pylypiv, B. K. Ostafiychuk, and O. Z. Garpul, “Dynam- ical x-ray diffraction theory: Characterization of defects and strai...

2017
[140]

Precessed electron diffraction study of defects and strain in gan nanowires fabricated by top-down etching,

G. Mart´ ın, L. L´ opez-Conesa, D. d. Pozo, Q. Portillo, G. Doundoulakis, A. Georgakilas, S. Estrad´ e, and F. Peir´ o, “Precessed electron diffraction study of defects and strain in gan nanowires fabricated by top-down etching,”Applied Physics Letters, vol. 121, p. 082104, 08 2022

2022
[141]

Modeling crystal defects using defect informed neural networks,

Z. Yang, X. Liu, X. Zhang, P. Huang, K. S. Novoselov, and L. Shen, “Modeling crystal defects using defect informed neural networks,”npj Computational Materials, vol. 11, p. 229, July 2025

2025
[142]

Quantitative analysis of cu xanes spectra using linear combination fitting of binary mixtures simulated by feff9,

S. Rojsatien, A. Mannodi-Kanakkithodi, T. Walker, T. Nietzold, E. Cole- grove, B. Lai, Z. Cai, M. Holt, M. K. Chan, and M. I. Bertoni, “Quantitative analysis of cu xanes spectra using linear combination fitting of binary mixtures simulated by feff9,”Radiation Physics and Chemi...

2023
[143]

Distribution of copper states, phases, and defects across the depth of a cu-doped cdte solar cell,

S. Rojsatien, A. Mannodi-Kanakkithodi, T. Walker, N. Mohan Kumar, T. Nietzold, E. Colegrove, D. Mao, M. E. Stuckelberger, B. Lai, Z. Cai, M. K. Y. Chan, and M. I. Bertoni, “Distribution of copper states, phases, and defects across the depth of a cu-doped cdte solar cell,”Chemi...

2023
[144]

First-principles simulations correlating x-ray absorption spectroscopy features to point defects in h-bn,

W. Sun, M. Seo, L. B. Bayu Aji, G. V. Taylor, A. A. Baker, S. O. Kucheyev, and L. F. Wan, “First-principles simulations correlating x-ray absorption spectroscopy features to point defects in h-bn,”The Journal 39 of Physical Chemistry Letters, vol. 16, no. 16, pp. 3926–3931, 2025

2025
[145]

Deep level transient spectroscopy: Tracing interface and bulk trap-induced degradation in algan/gan-heterostructure based devices,

K. Deng, S. Huang, X. Wang, Y. Yao, Y. Yang, L. Yu, Y. Pei, J. An, Q. Jiang, X. Liu, S. Yang, and K. J. Chen, “Deep level transient spectroscopy: Tracing interface and bulk trap-induced degradation in algan/gan-heterostructure based devices,”Information & Functional Materials,...

2024

Pith tools

Reviewed May 9, 2026 · model on record in the stance chip above.

[1] [1]

Defects in semiconductors,

L. Vines, E. Monakhov, and A. Kuznetsov, “Defects in semiconductors,” Journal of Applied Physics, vol. 132, p. 150401, 10 2022

work page 2022

[2] [2]

Perspective on defect control in semiconductors for photovoltaics,

X. Cai and S.-H. Wei, “Perspective on defect control in semiconductors for photovoltaics,”Journal of Applied Physics, vol. 134, no. 22, p. 220901, 2023

work page 2023

[3] [3]

The defect challenge of wide-bandgap semiconductors for photovoltaics and beyond,

A. M. Ganose, D. O. Scanlon, A. Walsh, and R. L. Z. Hoye, “The defect challenge of wide-bandgap semiconductors for photovoltaics and beyond,”Nature Communications, vol. 13, no. 1, p. 4715, 2022

work page 2022

[4] [4]

Pycdt: A python toolkit for modeling point defects in semiconductors and insulators,

D. Broberg, B. Medasani, N. E. Zimmermann, G. Yu, A. Canning, M. Haranczyk, M. Asta, and G. Hautier, “Pycdt: A python toolkit for modeling point defects in semiconductors and insulators,”Computer Physics Communications, vol. 226, pp. 165–179, 2018. 25

work page 2018

[5] [5]

Point defect engineering in thin-film solar cells,

J. S. Park, S. Kim, Z. Xie, and A. Walsh, “Point defect engineering in thin-film solar cells,”Nature Reviews Materials, vol. 3, pp. 194–210, 2018

work page 2018

[6] [6]

Beginner’s guide to interpreting defect and defect level diagrams,

P. Gorai, “Beginner’s guide to interpreting defect and defect level diagrams,”PRX Energy, vol. 4, p. 032001, Aug 2025

work page 2025

[7] [7]

Quantum Monte Carlo assessment of embedding for for strongly correlated defects: interplay between mean-field starting point and interactions

K. G. Kleiner, S. Joshi, W. Lee, A. Hampel, M. R¨ osner, C. E. Dreyer, and L. K. Wagner, “Quantum Monte Carlo assessment of embed- ding for a strongly-correlated defect: interplay between mean-field and interactions,” May 2025. arXiv:2505.00845 [cond-mat]

work page Pith review arXiv 2025

[8] [8]

Accuracy of$GW$for calculating defect energy levels in solids,

W. Chen and A. Pasquarello, “Accuracy of$GW$for calculating defect energy levels in solids,”Physical Review B, vol. 96, p. 020101, July 2017

work page 2017