pith. machine review for the scientific record. sign in

arxiv: 2604.21401 · v1 · submitted 2026-04-23 · ❄️ cond-mat.mtrl-sci

Recognition: unknown

GEWUM: General Exploration Workflow for the Utopia of Materials: A Unified Platform for Automated Structure Generation, Selection, and Validation

Jiexi Song , Aixian She , Changpeng Song , Diwei Shi , Fengyuan Xuan , Chongde Cao
Authors on Pith no claims yet

Pith reviewed 2026-05-09 21:32 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci
keywords materials discoverystructure predictionmachine learning interatomic potentialsworkflow automationstability assessmenthigh-throughput computationHPC integration
0
0 comments X

The pith

GEWUM unifies selective random structure search with universal machine learning potentials to automate materials exploration and validation.

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

The paper presents GEWUM as an open-source platform that integrates selective random structure search with universal machine learning interatomic potentials. This setup generates candidate structures, selects diverse ones, and performs stability checks plus property calculations in one automated sequence. A sympathetic reader would care because existing tools for materials prediction often require manual stitching across separate programs, which slows down work on large chemical spaces and high-performance computers. The platform is tested on nitride systems, uranium silicides, and high-pressure hydrides to show it can identify low-energy phases and new structures.

Core claim

GEWUM is a unified open-source platform that integrates the Selective Random Structure Search strategy with universal Machine Learning Interatomic Potentials. Its modular architecture supports SLURM-based HPC clusters and automates the workflow from random structure generation and diversity-preserving selection to thermodynamic and dynamic stability assessments as well as advanced property calculations such as elastic constants, thermal conductivity, and quasi-harmonic approximations. This is demonstrated through three case studies: prediction of low-energy polymorphs in the Al-Sc-N system, identification of a distinct P-62c phase of U3Si5, and high-pressure structure prediction of ThH10 at

What carries the argument

The Selective Random Structure Search (SRSS) strategy integrated with universal Machine Learning Interatomic Potentials (uMLIPs) inside a modular workflow architecture that natively supports SLURM-based HPC clusters.

If this is right

  • Enables efficient exploration of vast chemical spaces by combining structure generation and selection in one step.
  • Provides native support for running full workflows on SLURM-based high-performance computing clusters.
  • Allows unified assessment of thermodynamic stability, dynamic stability, and advanced properties like elastic constants and thermal conductivity.
  • Demonstrates concrete results such as new phases in complex nitrides, uranium silicides, and high-pressure hydrides.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The modular design could support addition of new property modules or alternative potentials beyond the current demonstrations.
  • Native HPC integration may lower the barrier for groups that lack custom scripting to run large-scale searches.
  • Success across chemically distinct systems suggests the workflow could extend to other classes of materials like alloys or oxides with minimal changes.
  • By handling initial screening with ML potentials, the platform could reduce the number of structures sent to more expensive higher-accuracy methods.

Load-bearing premise

Universal machine learning interatomic potentials remain accurate enough for reliable thermodynamic and dynamic stability assessments across the tested chemical systems without system-specific retraining.

What would settle it

Recalculation of one of the low-energy structures predicted by GEWUM in the Al-Sc-N case study with density functional theory yields a significantly higher energy or dynamic instability than reported.

Figures

Figures reproduced from arXiv: 2604.21401 by Aixian She, Changpeng Song, Chongde Cao, Diwei Shi, Fengyuan Xuan, Jiexi Song.

Figure 1
Figure 1. Figure 1: (a) The system architecture of GEWUM. (b) Home page snapshot; the inset shows the GEWUM logo. (c) The workflow of the RD module. 2.3. Core Functionality 2.3.1 Random Design Workflow (RD) A primary capability of GEWUM is the implementation of the Selective Random Structure Search (SRSS) framework for crystal structure generation and screening, see [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) The screening workflow of (Al13ScN16)x (x=1, 2). (b) The energy difference of predicted structures at the DFT-PBE level. (c) Schematic diagrams of some prominent metastable crystals. In principle, these target compositions allow the generation of 229 (space groups) ×100 (trial attempts) ×2 (compositions) =45,800 random structures. However, because a maximum limit of 60 atoms was imposed on the generate… view at source ↗
Figure 3
Figure 3. Figure 3: a [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) The Schematic diagrams of U3Si5 with USi2-type. (b) The crystal structure of P-62c-U3Si5 predicted in this work. (c) Simulated XRD patterns of U3Si5 with USi2-type and P-62c space group. (d) The AIMD simulation results of P-62c-U3Si5 [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Validation of thermophysical property calculations using [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
read the original abstract

The discovery of materials with tailored properties is increasingly reliant on computational methods. However, the fragmented landscape of existing software often hinders the seamless integration of large-scale structure prediction with rigorous stability validation, particularly in high-performance computing (HPC) environments. To address this gap, we present GEWUM (General Exploration Workflow for the Utopia of Materials), a unified, open-source platform designed to automate and accelerate materials discovery. GEWUM integrates the Selective Random Structure Search (SRSS) strategy with universal Machine Learning Interatomic Potentials (uMLIPs), enabling efficient exploration of vast chemical spaces. Its core architecture features a modular design with native support for SLURM-based HPC clusters. The platform unifies the entire workflow, from random structure generation and diversity-preserving selection to thermodynamic and dynamic stability assessments, as well as advanced property calculations (e.g., elastic constants, thermal conductivity, and quasi-harmonic approximations). We demonstrate GEWUM's capabilities through three distinct case studies: (1) the prediction of low-energy polymorphs in the complex Al-Sc-N nitride system; (2) the identification of a P-62c phase of U3Si5, distinct from the known AlB2 type; and (3) the high-pressure structure prediction of ThH10 at 150 GPa. Furthermore, benchmark tests show reasonable agreement in predicting thermophysical properties. By bridging the gap between uMLIPs and automated high-throughput workflows, GEWUM serves as a valuable framework to facilitate efficient and scalable materials exploration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces GEWUM, an open-source unified platform integrating Selective Random Structure Search (SRSS) with universal Machine Learning Interatomic Potentials (uMLIPs) for automated structure generation, diversity-preserving selection, thermodynamic/dynamic stability assessment, and property calculations (elastic constants, thermal conductivity, quasi-harmonic approximations). It features modular design with native SLURM HPC support and is demonstrated through three case studies (low-energy polymorphs in Al-Sc-N, a new P-62c phase of U3Si5, high-pressure ThH10 at 150 GPa) plus thermophysical benchmarks showing reasonable agreement.

Significance. A well-engineered, modular workflow platform with HPC integration and open-source release would be a useful contribution to computational materials science if the underlying uMLIP-based stability predictions are shown to be reliable. The emphasis on diversity-preserving selection and end-to-end automation addresses real practical bottlenecks in high-throughput exploration.

major comments (2)
  1. Abstract: the statements that 'benchmark tests show reasonable agreement' and that the three case studies 'succeeded' are presented without quantitative error bars, exact validation protocols against DFT, or details on post-hoc structure selection. These omissions are load-bearing because the central claim of reliable, unified exploration rests on the accuracy of uMLIP-derived formation energies and phonon spectra for stability assessments.
  2. Case studies section (Al-Sc-N, U3Si5, ThH10): no per-system DFT cross-validation or error quantification is reported for the predicted structures or stability metrics. Without this, inaccuracies in uMLIP energies or dynamics could invalidate the reported polymorph identifications and phase stability conclusions.
minor comments (1)
  1. The workflow architecture diagram would benefit from explicit labeling of data flow between SRSS, uMLIP evaluation, and stability modules to improve clarity for readers implementing the platform.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need for more quantitative validation details. We agree these strengthen the central claims and will revise the manuscript to address them directly.

read point-by-point responses

  1. Referee: Abstract: the statements that 'benchmark tests show reasonable agreement' and that the three case studies 'succeeded' are presented without quantitative error bars, exact validation protocols against DFT, or details on post-hoc structure selection. These omissions are load-bearing because the central claim of reliable, unified exploration rests on the accuracy of uMLIP-derived formation energies and phonon spectra for stability assessments.

    Authors: We agree that the abstract would benefit from explicit quantitative metrics. In the revised manuscript we will expand the abstract to report specific error statistics from the thermophysical benchmarks (MAE and RMSE for formation energies, phonon frequencies, and elastic constants relative to DFT reference data) along with the exact number of structures used in validation. We will also clarify the post-hoc selection protocol, including energy and phonon stability thresholds applied after uMLIP screening. These additions will be supported by new summary tables in the main text. revision: yes

  2. Referee: Case studies section (Al-Sc-N, U3Si5, ThH10): no per-system DFT cross-validation or error quantification is reported for the predicted structures or stability metrics. Without this, inaccuracies in uMLIP energies or dynamics could invalidate the reported polymorph identifications and phase stability conclusions.

    Authors: We acknowledge the value of system-specific cross-validation. The revised case-studies section will include per-system DFT comparisons for the lowest-energy candidates: formation-energy differences, phonon spectra at the Gamma point, and (where computationally tractable) full dispersion relations. For the high-pressure ThH10 case we will note the subset of structures for which full DFT phonons were performed and report the corresponding error metrics. These data will be presented in supplementary tables with explicit error bars. revision: yes

Circularity Check

0 steps flagged

No circularity: software platform with demonstrations, not derived quantities

full rationale

The paper describes the design and implementation of the GEWUM software platform that integrates SRSS with uMLIPs for structure exploration and validation. No equations, fitted parameters, or physical predictions are presented whose outputs reduce by construction to the inputs. The three case studies (Al-Sc-N, U3Si5, ThH10) and thermophysical benchmarks are reported as applications of the workflow rather than self-referential derivations. Any self-citations to prior SRSS or uMLIP work are not load-bearing for a central mathematical claim, as the contribution is the unified automation layer itself. This is a standard non-finding for a methods/software paper whose results are externally verifiable via the released code and independent DFT checks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The platform builds directly on existing SRSS and uMLIP methods from prior literature, adding only an automation and integration layer. No new physical constants, fitted parameters, or postulated entities are introduced in the abstract.

axioms (2)
  • domain assumption Universal machine learning interatomic potentials can be applied across chemically diverse systems for stability screening without substantial loss of predictive accuracy.
    Invoked when the workflow uses uMLIPs for thermodynamic and dynamic stability assessments in the three case studies.
  • standard math SLURM-based HPC environments provide the scheduling and resource management needed for the modular workflow.
    Stated as native support in the core architecture description.

pith-pipeline@v0.9.0 · 5602 in / 1502 out tokens · 50204 ms · 2026-05-09T21:32:07.023996+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

43 extracted references · 3 canonical work pages · 2 internal anchors

  1. [1]

    Designing a New Material World

    Olson, G.B. Designing a New Material World. Science 28 8 (2000) 993 - 998

    2000

  2. [2]

    The high - Throughput Highway to Computational Materials Design

    Curtarolo, S., Hart, G.L.W., Nardelli, M.B., Mingo, N., Sanvito, S., Levy, O. The high - Throughput Highway to Computational Materials Design. Nature Materials 12 (2013) 191 - 201

    2013

  3. [3]

    The path towards sustainabl e energy

    Chu, S., Cui, Y ., Liu, N. The path towards sustainabl e energy. Nature Materials 16 (2017) 16 - 22. [5] Yu, H., Giantomassi, M., Materzanini, G., Wang, J., Rignanese, G. Systematic assessment of various universal machine - learning interatomic potentials. Materials Genome Engineering Advances 2 (2024) e58

    2017

  4. [4]

    Performance assessment of universal machine learning interatomic potentials: Challenges and directions for materials' surfaces

    Fo cassio, B., Freitas, L.P.M., Schleder, G.R. Performance assessment of universal machine learning interatomic potentials: Challenges and directions for materials' surfaces. Physical Review Materials 8 (2024) 043801

    2024

  5. [5]

    A universal graph deep learning interatomic potential for the periodic table

    Chen, C., Ong, S.P. A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science 2 (2022) 718 - 728

    2022

  6. [6]

    A foundation model for atomistic materials chemistry

    Batatia, I., Benner, P., Chiang, Y ., Eldridge, J., Kovacs, D.P., Riebesell, J., Advincula, X.R., Asta, M., Avaylon, M., Baldwin, W.J., Batzner, S., Benedi, E., Bhowmik, A., Blau, S.M., Bokarev, S., Botev, I., Brown, D., Brualla, A., Cangi, A., Chatzidakis, S., Chen, Y ., Cui, Z., Darby, J.P., De, S., Dicks, A., DiTolla, A., Doerr, S., Dubizky, Z., Ebert,...

    2025

  7. [7]

    Generalized neural - network representation of high - dimensional potential - energy surfaces

    Behler, J., Parrinello, M. Generalized neural - network representation of high - dimensional potential - energy surfaces. Physical Review Letters 98 (2007) 146 401

    2007

  8. [8]

    Machine learning force fields

    Unke, O.T., Chmiela, S., Sauceda, H.E., Gastegger, M., Poltavsky, I., Schütt, K.T., Tkatchenko, A., Müller, K.R. Machine learning force fields. Chemical Reviews 121 (2021) 10142 - 10186

    2021

  9. [9]

    Machine learning interatomic potentials as emerging tools for materials science

    Deringer, V .L., Caro, M.A., Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Advanced Materials 31 (2019) 1902765

    2019

  10. [10]

    Crystal structure prediction using ab initio evolutionary techniques: Principles and applications

    Oganov, A.R., Glass, C.W. Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. Journal of Chemical Phys ics 124 (2006) 244704

    2006

  11. [11]

    CALYPSO: A method for crystal structure prediction

    Wang, Y ., Lv, J., Zhu, L., Ma, Y . CALYPSO: A method for crystal structure prediction. Computer Physics Communications 183 (2012) 2063 - 2070

    2012

  12. [12]

    XtalOpt: An open - source evolutionary algorithm for crystal s tructure prediction

    Lonie, D.C., Zurek, E. XtalOpt: An open - source evolutionary algorithm for crystal s tructure prediction. Computer Physics Communications 182 (2011) 372 - 387

    2011

  13. [13]

    Ab initio random structure searching

    Pickard, C.J., Needs, R.J. Ab initio random structure searching. Journal of Physics: Condensed Matter 23 (2011) 053201. [17] Wang, J., Gao, H., Han, Y ., Ding, C., Pan, S., Wang, Y ., Jia, Q., Wang, H., Xing, D., Sun, J. MAGUS: Machine learning and graph theory assisted universal structure searcher. National Science Review 10 (2023) nwad128

    2011

  14. [14]

    Structure Prediction Drives Materials Discovery

    Oganov, A.R., Pickard, C.J., Zhu, Q., Needs, R.J. Structure Prediction Drives Materials Discovery. Nature Reviews Materials 4 (2019) 331 - 348. [19] Day, G.M., Cooper, A.I. Energy - Structure - Function Maps: Cartography for Materials Discovery. Advanced Materials 30 (2018) 1704944

    2019

  15. [15]

    SISSO: A compressed - sensing method for systematically identifying efficient physical descriptors for materials properties

    Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., Ghiringhelli, L.M. SISSO: A compressed - sensing method for systematically identifying efficient physical descriptors for materials properties. Physical Review Materials 2 (2018) 083802

    2018

  16. [16]

    MACE: Higher order equivariant message passing neural networks for fast and accurate force fields

    Batatia, I., Kovács, D.P., Simm, G.N.C., Ortner, C., Csányi, G. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems 35 (2022) 11423 - 11436

    2022

  17. [17]

    E(3) - Equivariant Graph Neural Networks for Data - Efficient and Accurate Interatomic Potentials

    Batzner, S., Smidt, T.E., Sun, L., Geiger, M.J., Mailoa, J.P., Kornbluth, M., M olinari, N., Kozinsky, B. E(3) - Equivariant Graph Neural Networks for Data - Efficient and Accurate Interatomic Potentials. Nature Communications 13 (2022) 2453

    2022

  18. [18]

    Learning Local Equivariant Representations for Large - Scale Atomistic Dynamics

    Musaelian, A., Batzner, S., Johansson, A., Sun, L., Owen, C.J., Kornbluth, M., Kozinsky, B. Learning Local Equivariant Representations for Large - Scale Atomistic Dynamics. Nature Communications 14 (2023) 579

    2023

  19. [19]

    Data - Driven Materials Science: Status, Challenges, and Perspectives

    Himanen, L., Geurts, A., Foster, A.S., Rinke, P. Data - Driven Materials Science: Status, Challenges, and Perspectives. Advanced Science 6 (2019) 1900808

    2019

  20. [20]

    Integrating computational and experimental workflows for accelerated organic materials discovery

    Greenaway, R.L., Jelfs, K.E. Integrating computational and experimental workflows for accelerated organic materials discovery. Advanced Materials 33 (2021) 2004831

    2021

  21. [21]

    Spglib: a software library for crystal sym metry search

    Togo, A., Tanaka, I. Spglib: a software library for crystal sym metry search. Computational Materials Science 181 (2020) 107731

    2020

  22. [22]

    Enhancing crystal structure prediction by combining computational and experimental data via graph networks

    Qin, C., Liu, J., Ma, S., Du, J., Jiang, G., Zhao, L. Enhancing crystal structure prediction by combining computational and experimental data via graph networks. arXiv:2311.11665 (2023)

    work page arXiv 2023

  23. [23]

    On - the - fly Closed - loop Materials Discovery via Bayesian Active Learning

    Kusne, A.G., Yu, P., Nussinov, R., Keller, D., Anderson, A., Schull, I.D., Takeuchi, I. On - the - fly Closed - loop Materials Discovery via Bayesian Active Learning. Nature Communications 11 (2020) 5966. [29] Jain, A., Montoya, J., Dwaraknath, S., Zimmerm ann, N.E.R., Dagdelen, J., Horton, M., Huck, P., Winston, D., Cholia, S., Ong, S.P., Persson, K. The...

    2020

  24. [24]

    Selective Random Structure Search (SRSS): Unbiased Exploration of Polymorphs in Crystals

    Song, J., Shi, D., She, A., Cao, C., X uan, F. Selective Random Structure Search (SRSS): Unbiased exploration of polymorphs in crystals. arXiv:2604.08972 (2026). [31] Fredericks, S., Parrish, K., Sayre, D., Zhu, Q . PyXtal: a Python library for crystal structure generation and symmetry analysis. Computer Physics Communications 261 (2021) 107810

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  25. [25]

    Implementation strategies in phonopy and phono3py

    Togo, A., Chaput, L., Tadano, T., Tanaka, I. Implementation strategies in phonopy and phono3py. Journal of Physics: Condensed Matter 35 (2023) 353001

    2023

  26. [26]

    The atomic simulation environment - a Python library for working with atoms

    Larsen, A.H., Jørgensen, J.J., Mortensen, J .J., Chen, S., Kaasbjerg, K., Falicov, M., Gjerding, M., Glavvad, D., Haikola, V ., Hansen, H.H., Kristoffersen, H.H., Kuisma, M., Leonhard, J., Melander, A., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Strange, M., Tritsaris, G.A., V arini, L., Walter, M., Zeng, Z., Jacobsen, K.W. The ato...

    2017

  27. [27]

    Some methods for classification and analysis of multivaria te observations

    MacQueen, J.B. Some methods for classification and analysis of multivaria te observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1 (1967) 281 - 297

    1967

  28. [28]

    Density - based clustering based on hierarchical density estimates

    Campello, R.J.G.B., Moulavi, D., Sander, J. Density - based clustering based on hierarchical density estimates. Lecture Notes in Co mputer Science 7819 (2013) 160 - 172

    2013

  29. [29]

    A critical examination of compound stability predictions from machine - learned formation energies

    Bartel, C.J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., Ceder, G. A critical examination of compound stability predictions from machine - learned formation energies. npj Computational Materials 6 (2020) 97

    2020

  30. [30]

    Parallel distributed computing using Python

    Da lcín, L.D., Paz, R.R., Kler, P.A., Cosimo, A. Parallel distributed computing using Python. Advances in Water Resources 34 (2011) 1124 - 1139

    2011

  31. [31]

    GNU Parallel 2018

    Tange, O. GNU Parallel 2018. The USENIX Magazine 43 (2018) 40 - 47

    2018

  32. [32]

    Crystal Structure Predictio n: Reflections on Present Status and Challenges

    Oganov, A.R. Crystal Structure Predictio n: Reflections on Present Status and Challenges. Faraday Discussions 211 (2018) 643 - 660. [40] Koval, P., Foerster, D., Coulaud, O. A Parallel Iterative Method for Computing Molecular Absorption Spectra. Journal of Chemical Theory and Computation 6 (2010) 2 654 - 2668

    2018

  33. [33]

    Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities

    Amdahl, G.M. Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities. AFIPS Conference Proceedings 30 (1967) 483 - 485

    1967

  34. [34]

    AiiDA: Automated Interactive Infrastructure and Database for Computational Science

    Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., Kozinsky, B. AiiDA: Automated Interactive Infrastructure and Database for Computational Science. Computational Materials Science 111 (2016) 218 - 230

    2016

  35. [35]

    The Crystallographic Information File (CIF): a New Standard Archive File for Crystallography

    Hall, S.R., Allen, F.H., Brown, I.D. The Crystallographic Information File (CIF): a New Standard Archive File for Crystallography. A cta Crystallographica Section A 47 (1991) 655 - 685

    1991

  36. [36]

    CIF: The Computer Language of Crystallography

    Brown, I.D., McMahon, B. CIF: The Computer Language of Crystallography. Acta Crystallographica Section B 58 (2002) 317 - 324. [45] Tipton, W.W., Hennig, R.G. A grand canonical genetic algorithm for the prediction of multi - component phase diagrams and testing of empirical potentials. Journal of Physics: Condensed Matter 25 (2013) 495401

    2002

  37. [37]

    Enhancement of piezoelectric respon se in scandium aluminum nitride alloy thin films prepared by dual reactive cosputtering

    Akiyama, M., Kamohara, T., Kano, K., Teshigahara, A., Kawahara, N., Kuwano, N. Enhancement of piezoelectric respon se in scandium aluminum nitride alloy thin films prepared by dual reactive cosputtering. Advanced Materials 21 (2009) 593 - 596

    2009

  38. [38]

    AlScN: A III - V semiconductor based ferroelectric

    Fichtner, S., Wolff, N., Lofink, F., Kohler, B., Wagner, B. AlScN: A III - V semiconductor based ferroelectric. Journal of App lied Physics 125 (2019) 114103. [48] Patidar, J., Thorwarth, K., Schmitz - Kempen, T., Kessels, R., Siol, S. Deposition of highly crystalline AlScN thin films using synchronized high - power impulse magnetron sputtering: From comb...

    2019

  39. [39]

    Preparation, identification and chemical properties of the thorium silicides

    Jacobson, E.L., Freeman, R.D., Searcy, A. Preparation, identification and chemical properties of the thorium silicides. Journal of the American Chemical Society 78 (1956) 4850 - 4854. [51] Chung, C.K., Guo, X.F., Wang, G., Wilson, T.L., White, J.T., Nelson, A.T., Shelyug, A., Boukhalfa, H., Yang, P., Batista, E.R., Migdisov, A.A., Roback, R.C., Navrotsky,...

    1956

  40. [40]

    VESTA3 for three - dimensional visualization of crystal, volumetric and morphology data

    Momma, K., Izumi, F. VESTA3 for three - dimensional visualization of crystal, volumetric and morphology data. Journal of Applied Crystallography 44 (2011) 1272 - 1276

    2011

  41. [41]

    Tri - arc growth and characterization of U 3 Si 2 and U 3 Si 5 single crystals

    Kardoulaki, E., Byler, D.D., Bárta, J., McClellan, K.J. Tri - arc growth and characterization of U 3 Si 2 and U 3 Si 5 single crystals. Journal of Crystal Growth 558 (2021) 126025. [54] Pöttgen, R., Hoffmann, R.D., Kussmann, D . The Binary Silicides Eu 5 Si 3 and Yb 3 Si 5 - ： Synthesis, Crystal Structure, and Chemical Bonding. Zeitschrift fü r anorganisc...

    2021

  42. [42]

    Babizhetskyy, V ., Jardin, R., Gautier, R., Fontaine, B., Halet, J. - F. Flux synthesis, crystal structure and electronic properties of the layered rare earth metal boride silicide Er 3 Si 5 - x B. An ex ample of a boron/silicon - ordered structure derived from the AlB 2 structure type. Zeitschrift für Naturforschung B 76 (2021) 869 - 879. [56] Semenok, D...

    2021

  43. [43]

    MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

    Van Daal, H.J., Knippenberg, W.F., Wasscher, J.D. On the electronic conduction of α - SiC crystals between 3 00 and 1500 K. Journal of Physics and Chemistry of Solids 24 (1963) 109 - 127. [58] Yang, H., Hu, C., Zhou, Y ., Liu, X., Shi, Y ., Li, J., Li, G., Chen, Z., Chen, S., Zeni, C., Horton, M., Pinsler, R., Fowler, A., Zügner, D., Xie, T., Smith, J., S...

    work page internal anchor Pith review arXiv 1963