Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read Basis set incompleteness shifts fixed-node DMC binding energies of weakly bound dimers by up to about 1 kcal/mol, with hydrogen-bonded complexes worst.

desk verdict Solid A24 scan showing FN-DMC binding energies need augmented or counterpoise-corrected basis sets, but the abstract's protocol contradicts Section 4 and the BSSE/BSIE claim is logically shaky. read the letter →

arxiv 2412.00368 v1 pith:KVKNKRKD submitted 2024-11-30 physics.chem-ph physics.comp-ph

Kousuke Nakano , Benjamin X. Shi , Dario Alfè , Andrea Zen This is my paper · ORCID
classification physics.chem-phphysics.comp-ph
keywords fixed-nodediffusionMonteCarlobasissetincompletenesserrorsuperpositioncounterpoisecorrectionnon-covalentinteractionsA24benchmarknodalsurfacetrialwavefunction
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

Fixed-node diffusion Monte Carlo (FN-DMC) is often treated as nearly immune to basis set incompleteness because the fixed-node approximation depends only on the nodal surface of the trial wavefunction. This paper argues that this immunity fails for binding energies of weakly bound dimers: for the 24 complexes of the A24 set, a small localized basis such as cc-pVDZ introduces errors large enough to matter, and hydrogen-bonded systems suffer the most. The paper shows that adding diffuse functions, applying counterpoise (CP) correction, or both brings the binding energies to the complete-basis-set limit, so a modest basis set is enough if one of those measures is taken. If this is right, DMC benchmarks of non-covalent interactions need to state and control basis-set and counterpoise status, much as quantum chemistry already does.

What carries the argument

The central object is the nodal surface of the trial wavefunction, because fixed-node DMC inherits basis set incompleteness only through that surface. The determinant part of the trial wavefunction is expanded in correlation-consistent Gaussian bases of increasing cardinality, and the paper isolates the basis-set effect by computing lattice-regularized DMC (LRDMC), a lattice-discretized variant of DMC, at several lattice spacings and extrapolating to the continuum limit, with an augmented sextuple-zeta basis as the complete-basis reference. The second key piece is the counterpoise correction, defined by monomer calculations with ghost orbitals from the partner; the paper shows that ghost orbitals improve the monomer nodal surface and thereby reduce the BSIE. This is what makes the qualitative result believable: basis-set incompleteness affects DMC binding energies through the nodes, not through the projection step.

What would settle it

Compute the cc-pVDZ and aug-cc-pV6Z DMC binding energy of ammonia dimer, one of the largest-BSIE cases in the dataset, with a different pseudopotential or an all-electron treatment. If the gap between the two changes by more than the reported roughly 1.3 kcal/mol, the reference is not converged and every reported BSIE shifts.

Watch

Extended reading notes

Core claim

The central claim is that basis set incompleteness errors (BSIEs) in fixed-node DMC binding energies are real and can be comparable in size to the benchmark accuracy being sought, contrary to the common assumption that projection methods wash out basis set bias. Working through all 24 dimers of the A24 set, the paper shows that with cc-pVDZ the BSIE can reach about a kilocalorie per mole for hydrogen-bonded systems, while dispersion-dominated dimers are affected less. The complete-basis-set reference is the aug-cc-pV6Z DMC binding energy, whose convergence the paper checks by showing that basis set superposition errors vanish at that size and by cross-checking against large-cutoff plane-wave DMC. The paper concludes that cc-pVDZ with counterpoise correction, or aug-cc-pVTZ without it, is sufficient to reach the CBS limit within statistical errors, and that no cardinal-number extrapolation is needed once either augmentation or CP correction is used.

Load-bearing premise

The entire error budget is measured against the paper's own aug-cc-pV6Z DMC binding energies, so the conclusions stand only if those values really are the complete-basis-set limit.

Editorial extensions

If this is right

  • FN-DMC binding energies from small localized basis sets such as cc-pVDZ should not be treated as complete-basis values; for hydrogen-bonded dimers the basis-set bias is on the order of 1 kcal/mol, comparable to the accuracy being targeted.
  • Adding diffuse functions changes the picture: aug-cc-pVTZ without counterpoise correction reaches the CBS limit within statistical error, making it a safe default for non-covalent DMC benchmarks.
  • Counterpoise correction is an alternative cure: CP-corrected cc-pVDZ or aug-cc-pVDZ recovers CBS binding energies, so smaller basis sets remain usable when the correction is applied.
  • The success of CP correction shows that ghost orbitals improve monomer nodal surfaces, confirming that basis-set superposition and incompleteness act through the trial wavefunction's nodes.
  • Earlier DMC benchmarks of the A24 set made with un-augmented triple-zeta basis sets carry a small BSIE contamination, so published reference values should come from augmented-basis or CP-corrected calculations.

Reading between the lines

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

  • The paper's hydrogen-bond versus dispersion trend suggests that interactions with long-range electrostatic tails, such as charged dimers or ionic clusters, will show larger BSIEs; a direct test would be to repeat the cc-pVDZ versus aug-cc-pV6Z comparison on a small set of charged or strongly dipolar dimers.
  • Because the nodal surface is the carrier, trial wavefunctions with better nodes, such as multideterminant or CASSCF expansions, may be less sensitive to basis size; this is testable by recomputing a few A24 dimers with correlated trial functions across cc-pVDZ and aug-cc-pVTZ.
  • A practical protocol for large systems follows implicitly: when aug-cc-pVTZ is unaffordable, cc-pVDZ with counterpoise correction is a validated route to CBS binding energies for non-covalent systems at a fraction of the cost.
  • The conclusions are tied to the specific ccECP pseudopotentials used, so transferring the protocol to other effective core potentials should be checked, since pseudopotentials alter the nodal surface and could change the BSIE magnitude.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript reports a systematic fixed-node diffusion Monte Carlo (FN-DMC, specifically LRDMC) study of basis set incompleteness errors (BSIEs) and basis set superposition errors (BSSEs) in the binding energies of the 24 non-covalently bound dimers in the A24 benchmark set. Using cc-pVnZ and aug-cc-pVnZ basis sets with ccECP pseudopotentials, the authors extrapolate the LRDMC lattice-space bias to the a→0 limit, compute BSSEs and counterpoise (CP)-corrected binding energies, and define BSIEs relative to aug-cc-pV6Z reference values that are cross-checked against plane-wave DMC calculations. They find that BSIEs are significant for small basis sets such as cc-pVDZ, especially for hydrogen-bonded dimers, and that augmentation with diffuse functions and/or CP correction reduces BSIEs below statistical error. The paper concludes with practical recommendations for choosing basis sets in DMC binding-energy calculations.

Significance. This work addresses a question of practical importance for the QMC community: whether FN-DMC binding energies of weakly bound dimers are significantly affected by basis set incompleteness and basis set superposition error. The study is careful in several respects: it uses the full A24 set, extrapolates the LRDMC lattice-space bias from four lattice spacings, reports statistical errors throughout, computes BSSE and CP corrections explicitly, and provides an independent plane-wave DMC cross-check. The central finding—that BSIEs can be significant for small localized basis sets and are mitigated by augmentation and/or CP correction—is likely to influence protocol choices in QMC calculations of non-covalent interactions, and the paper also offers a useful re-evaluation of earlier A24 benchmarks. No fitted parameters enter the physical claim; the BSIE estimates are simple differences against a converged reference, with lattice-space extrapolation coefficients serving as standard convergence parameters.

major comments (3)
  1. [Abstract and Section 1 vs Section 4 and Section 6] The paper's headline protocol is internally inconsistent. The abstract and Section 1 state that "cc-pVDZ is sufficient when CP correction is applied," but Section 4's analysis of Figure 3(a) states that for non-augmented cc basis sets "one needs the cc-pVQZ without the CP corrections or the cc-pVTZ with the CP correction," and Section 6 states "with the CP correction, one can use a smaller basis, such as cc-pVTZ or aug-cc-pVDZ basis sets." These statements cannot all be true: the data do not show that cc-pVDZ with CP reaches the CBS limit within the stated 3σ ≈ 0.25 kcal/mol threshold. This contradiction directly affects the practical recommendation that is a central claim of the paper and must be resolved, for example by correcting what appears to be a typo in the abstract and introduction.
  2. [Section 3] The statement "Since zero BSSE implies zero BSIE in binding energy calculation" is not generally correct. Zero BSSE only tells one that the ghost functions of the partner monomer do not lower the monomer energies; it does not by itself establish that the monomer or dimer basis is at the CBS limit. A basis could be complete for each isolated monomer but still lack functions needed for the dimer, in which case BSSE would vanish while BSIE in E_b would not. This logical step is used to justify choosing aug-cc-pV6Z as the CBS reference in Eq. (2). The paper has independent support for that reference from the plane-wave QMCPACK cross-check in SI Table S1 and Figure S1, so the central estimates are not solely resting on the false implication, but the statement should be corrected or explicitly qualified and the reference validation presented as an empirical check rather than a logical consequence.
  3. [Section 3 and SI Section 1] The main-text BSIE estimates use TurboRVB LRDMC energies with the DTM localization, while the independent plane-wave cross-check is performed with QMCPACK using the T-move scheme, and the paper notes these are not equivalent. For some dimers the two codes give differences of order 0.1–0.2 kcal/mol (e.g., ammonia dimer: –3.10(6) in Table 1 vs –3.17(5) and –3.30(7) in SI Table S1). Since the BSIEs in Figures 2 and 3(a) are differences within TurboRVB, a DTM-specific offset would partly cancel, but the external validation of the aug-cc-pV6Z reference does not directly validate the DTM reference used in Eq. (2). The authors should either provide a DTM-based plane-wave check or explicitly discuss the transferability and show that the protocol conclusions are robust to the DTM/T-move difference, for example by reporting CP-corrected QMCPACK results that would confirm the cc-pVTZ and aug-cc-pVDZ recommendations.
minor comments (5)
  1. [Table 1 and SI Tables SII/SIII] Several dimer labels are duplicated or misspelled (e.g., "methane--ethane" appears twice, "ethene dimer" and "ethyne dimer" appear twice, and the SI has "ammoniam--ethane"); because the A24 set contains 24 distinct systems, these labels should be corrected.
  2. [Section 2] The claim that Jastrow-factor optimization does not affect the extrapolated LRDMC energies under DTM would benefit from a one-sentence justification or citation, since DTM still uses the trial wavefunction in the nonlocal pseudopotential localization.
  3. [Section 4] The representative threshold "3σ ≈ 0.25 kcal/mol" should be defined more precisely; the statistical error differs from dimer to dimer, so the authors should state how this single value is obtained.
  4. [Figure 3(a)] The caption should clearly state that the PW BSIEs in the right panel are referenced to the QMCPACK plane-wave value, not to the TurboRVB aug-cc-pV6Z value used in the left and middle panels; as written this is ambiguous.
  5. [Section 4] The sentence "the extrapolation is no longer needed when the CP correction is applied" is too strong; the results show that CP correction reduces BSIEs to below the statistical threshold for sufficiently large basis sets, not that cardinal-number extrapolation is never useful.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: BSIE estimates are measured against an aug-cc-pV6Z reference that is independently cross-checked with plane-wave DMC, so the central claim does not reduce to its inputs.

full rationale

The derivation chain is not circular. The BSIE definition (Eq. 2) is a difference between a finite-basis DMC binding energy and a CBS reference; the reference is the authors' own aug-cc-pV6Z LRDMC value (Section 3), but its use as E_CBS_b is justified by two independent checks: the BSSEs of aug-cc-pV6Z vanish within 3σ and, more decisively, QMCPACK binding energies with aug-cc-pV6Z and with large-cutoff plane-wave trial wavefunctions agree within 3σ (Section 3 and SI Figure S1). The small-basis BSIEs are therefore not forced by construction; they are measured against an externally corroborated reference. No parameter is fitted to the target quantities: the lattice-space extrapolations (a→0) are standard convergence extrapolations and do not inject the BSIE conclusions. Self-citations (e.g., Nakano et al. 60 in Section 5, TurboRVB and DTM method citations) are used for method attribution and benchmark comparison, not as the load-bearing justification for the central claim. Two flagged weaknesses are correctness issues rather than circularity: (i) Section 3 asserts 'zero BSSE implies zero BSIE in binding energy calculation', which is not generally true and overstates the evidential value of the vanishing BSSE; the plane-wave cross-check supplies independent support, so this does not make the derivation circular. (ii) The abstract and Introduction state 'cc-pVDZ is sufficient when CP correction is applied,' which is inconsistent with Section 4's own threshold-based conclusion that the smallest non-augmented CP-corrected basis reaching the CBS limit within 3σ ≈ 0.25 kcal/mol is cc-pVTZ. This internal inconsistency affects the practical recommendation but does not make the derivation circular. Overall, the central claim has independent computational content and is benchmarked against external references, so the circularity score is low.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The only fitted numbers are the lattice-space extrapolation coefficients. The key input is an internally generated aug-cc-pV6Z reference, cross-checked by plane-wave DMC.

free parameters (2)
  • LRDMC lattice-space extrapolation coefficients (k2, k4) = not reported; fitted per dimer and basis set
    Binding energies and BSSEs are extrapolated to zero lattice space using polynomial fits Eb(a^2)=k4 a^4 + k2 a^2 + Eb and BSSE(a^2)=k2 a^2 + BSSE. The reported BSIEs inherit these fitted values; the paper does not provide the coefficients or fit residuals.
  • Lattice spacings a = 0.30, 0.25, 0.20, 0.10 Bohr = hand-chosen, not fitted
    The extrapolation assumes these points are in the asymptotic a^2 regime; results may shift if the fit is not stable.
assumptions (5)
  • domain assumption FN-DMC energy is exact for the fixed nodal surface; the trial wavefunction's nodal surface is the channel through which basis set incompleteness enters.
    Used throughout; basis set effects on the amplitude are projected out, so only nodes matter. This is the standard QMC framework.
  • domain assumption LRDMC with lattice space extrapolated to zero reproduces standard FN-DMC at zero time step; the lattice error follows a polynomial in a^2.
    Section 2; the extrapolation formulas for Eb and BSSE assume the leading lattice-space error is polynomial in a^2.
  • domain assumption ccECP pseudopotentials with DTM treatment of nonlocal terms give binding energies accurate enough that ECP error does not distort BSIE comparisons.
    Section 2; no all-electron or alternative pseudopotential comparison is made within the study, so errors from the ECP are absorbed into the reference and BSIEs.
  • domain assumption aug-cc-pV6Z DMC binding energies equal the complete-basis limit for all A24 dimers.
    Section 3; the paper's stated 'zero BSSE implies zero BSIE' logic is not generally valid, so this rests on the PW cross-check rather than on BSSE alone.
  • domain assumption LDA-PZ single-determinant trial wavefunctions with a Jastrow factor are representative of FN-DMC practice for non-covalent interactions.
    Generalization from one trial-wavefunction flavor to 'FN-DMC' claims; other ansatze may show different basis-set sensitivity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions." pith.science (2026). https://pith.science/paper/KVKNKRKD

@misc{pith2026241200368,
  author       = {Pith},
  title        = {Pith review of: Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KVKNKRKD}},
  note         = {Machine review of arXiv:2412.00368}
}
abstract

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection method, is often considered minimally affected by basis set biases. Here, we show that this assumption is not always valid. While the relative error introduced by a small basis set in the total FN-DMC energy is minor, it can become significant in binding energy ($E_{\rm b}$) evaluations of weakly interacting systems. We systematically investigated BSIEs in FN-DMC-based binding energy ($E_{\rm b}$) evaluations using the A24 dataset, a well-known benchmark set of 24 non-covalently bound dimers. Contrary to common expectations, we found that BSIEs in FN-DMC evaluations of $E_{\rm b}$ are indeed significant when small localized basis sets, such as cc-pVDZ, are employed. We observed that BSIEs are larger in dimers with hydrogen-bonding interactions and smaller in dispersion-dominated interactions. We also found that augmenting the basis sets with diffuse orbitals, using counterpoise (CP) correction, or both, effectively mitigates BSIEs.

Figures

Figures reproduced from arXiv: 2412.00368 by the authors.

Figure 1
Figure 1. (a) The BSSEs in the binding energies of the A24 set computed by LRDMC with [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. BSIEs in the binding energies of the A24 set, estimated from LRDMC calculations [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. The violin plots of BSIEs in the binding energy calculations of the A24 data [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Systematic discrepancies between reference methods for non-covalent interactions within the S66 dataset

    physics.chem-ph 2024-12 conditional novelty 7.0 of 10

    Diffusion Monte Carlo interaction energies for the full S66 dataset reveal systematic deviations from CCSD(T) that correlate with the ratio of electrostatic to dispersion contributions.

Reference graph

Works this paper leans on

66 extracted references · 64 canonical work pages · cited by 1 Pith paper

  1. [1]

    The statistical error of green's function Monte Carlo

    Ceperley, D. The statistical error of green's function Monte Carlo. J. Stat. Phys. 1986, 43, 815--826

    work page 1986

  2. [2]

    Foulkes, W. M. C.; Mitas, L.; Needs, R. J.; Rajagopal, G. Quantum Monte Carlo simulations of solids. Rev. Mod. Phys. 2001, 73, 33

    work page 2001

  3. [3]

    D.; Monserrat, B.; Lloyd-Williams, J

    Drummond, N. D.; Monserrat, B.; Lloyd-Williams, J. H.; R \' os, P. L.; Pickard, C. J.; Needs, R. J. Quantum Monte Carlo study of the phase diagram of solid molecular hydrogen at extreme pressures. Nat. Commun. 2015, 6, 1--6

    work page 2015

  4. [4]

    Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations

    Mazzola, G.; Helled, R.; Sorella, S. Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations. Phys. Rev. Lett. 2018, 120, 025701

    work page 2018

  5. [5]

    High-pressure hydrogen by machine learning and quantum Monte Carlo

    Tirelli, A.; Tenti, G.; Nakano, K.; Sorella, S. High-pressure hydrogen by machine learning and quantum Monte Carlo. Phys. Rev. B 2022, 106, L041105

    work page 2022

  6. [6]

    K.; Ceperley, D

    Ly, K. K.; Ceperley, D. M. Phonons of metallic hydrogen with quantum Monte Carlo . J. Chem. Phys. 2022, 156, 044108

    work page 2022

  7. [7]

    Niu, H.; Yang, Y.; Jensen, S.; Holzmann, M.; Pierleoni, C.; Ceperley, D. M. Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo. Phys. Rev. Lett. 2023, 130, 076102

    work page 2023

  8. [8]

    Quantum phase diagram of high-pressure hydrogen

    Monacelli, L.; Casula, M.; Nakano, K.; Sorella, S.; Mauri, F. Quantum phase diagram of high-pressure hydrogen. Nat. Phys. 2023, 19, 845--850

    work page 2023

Show all 66 references
  1. [9]

    Principal deuterium Hugoniot via quantum Monte Carlo and -learning

    Tenti, G.; Nakano, K.; Tirelli, A.; Sorella, S.; Casula, M. Principal deuterium Hugoniot via quantum Monte Carlo and -learning. Phys. Rev. B 2024, 110, L041107

    2024

  2. [10]

    T.; Yuk, S

    Krogel, J. T.; Yuk, S. F.; Kent, P. R. C.; Cooper, V. R. Perspectives on van der Waals Density Functionals: The Case of TiS _2 . J. Phys. Chem. A 2020, 124, 9867--9876

    2020

  3. [11]

    L.; Krogel, J

    Ichibha, T.; Dzubak, A. L.; Krogel, J. T.; Cooper, V. R.; Reboredo, F. A. CrI _ 3 revisited with a many-body ab initio theoretical approach. Phys. Rev. Mater. 2021, 5, 064006

    2021

  4. [12]

    A.; Kumar, K

    Nikaido, Y.; Ichibha, T.; Hongo, K.; Reboredo, F. A.; Kumar, K. C. H.; Mahadevan, P.; Maezono, R.; Nakano, K. Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs. J. Phys. Chem. C 2022, 126, 6000--6007

    2022

  5. [13]

    Systematic DFT+U and Quantum Monte Carlo Benchmark of Magnetic Two-Dimensional (2D) CrX _3 (X = I, Br, Cl, F)

    Wines, D.; Choudhary, K.; Tavazza, F. Systematic DFT+U and Quantum Monte Carlo Benchmark of Magnetic Two-Dimensional (2D) CrX _3 (X = I, Br, Cl, F). J. Phys. Chem. C 2023, 127, 1176--1188

    2023

  6. [14]

    T.; Ataca, C

    Wines, D.; Tiihonen, J.; Saritas, K.; Krogel, J. T.; Ataca, C. A Quantum Monte Carlo Study of the Structural, Energetic, and Magnetic Properties of Two-Dimensional H and T Phase VSe _2 . J. Phys. Chem. Lett. 2023, 14, 3553--3560

    2023

  7. [15]

    G.; Klime s , J.; Tkatchenko, A.; Alf \`e , D.; Michaelides, A

    Zen, A.; Brandenburg, J. G.; Klime s , J.; Tkatchenko, A.; Alf \`e , D.; Michaelides, A. Fast and accurate quantum Monte Carlo for molecular crystals. Proc. Natl. Acad. Sci. U.S.A. 2018, 115, 1724--1729

    2018

  8. [16]

    How Accurate Are Simulations and Experiments for the Lattice Energies of Molecular Crystals? Phys

    Della Pia, F.; Zen, A.; Alf\`e, D.; Michaelides, A. How Accurate Are Simulations and Experiments for the Lattice Energies of Molecular Crystals? Phys. Rev. Lett. 2024, 133, 046401

    2024

  9. [17]

    D.; Casula, M.; Kim, J.; Sorella, S.; Martin, R

    Beaudet, T. D.; Casula, M.; Kim, J.; Sorella, S.; Martin, R. M. Molecular hydrogen adsorbed on benzene: Insights from a quantum Monte Carlo study. J. Chem. Phys. 2008, 129, 164711

    2008

  10. [18]

    M.; Cox, S

    Zen, A.; Roch, L. M.; Cox, S. J.; Hu, X. L.; Sorella, S.; Alf\` e , D.; Michaelides, A. Toward accurate adsorption energetics on clay surfaces. J. Phys. Chem. C 2016, 120, 26402--26413

    2016

  11. [19]

    S.; Rossi, M.; Alfè, D.; Tsatsoulis, T.; Ramberger, B.; Brandenburg, J

    Al-Hamdani, Y. S.; Rossi, M.; Alfè, D.; Tsatsoulis, T.; Ramberger, B.; Brandenburg, J. G.; Zen, A.; Kresse, G.; Grüneis, A.; Tkatchenko, A.; Michaelides, A. Properties of the water to boron nitride interaction: From zero to two dimensions with benchmark accuracy . J. Chem. Phy...

    2017

  12. [20]

    Quantum Monte Carlo Studies of CO Adsorption on Transition Metal Surfaces

    Hsing, C.-R.; Chang, C.-M.; Cheng, C.; Wei, C.-M. Quantum Monte Carlo Studies of CO Adsorption on Transition Metal Surfaces. J. Phys. Chem. C 2019, 123, 15659--15664

    2019

  13. [21]

    X.; Zen, A.; Kapil, V.; Nagy, P

    Shi, B. X.; Zen, A.; Kapil, V.; Nagy, P. R.; Grüneis, A.; Michaelides, A. Many-Body Methods for Surface Chemistry Come of Age: Achieving Consensus with Experiments. J. Am. Chem. Soc. 2023, 145, 25372--25381

    2023

  14. [22]

    Korth, M.; Grimme, S.; Towler, M. D. The Lithium--Thiophene Riddle Revisited. J. Phys. Chem. A 2011, 115, 11734--11739

    2011

  15. [23]

    D.; Needs, R

    Nemec, N.; Towler, M. D.; Needs, R. J. Benchmark all-electron ab initio quantum Monte Carlo calculations for small molecules . J. Chem. Phys. 2010, 132, 034111

    2010

  16. [24]

    R.; Toulouse, J.; Umrigar, C

    Petruzielo, F. R.; Toulouse, J.; Umrigar, C. J. Approaching chemical accuracy with quantum Monte Carlo. J. Chem. Phys. 2012, 136, 124116

    2012

  17. [25]

    Molecular Properties by Quantum Monte Carlo: An Investigation on the Role of the Wave Function Ansatz and the Basis Set in the Water Molecule

    Zen, A.; Luo, Y.; Sorella, S.; Guidoni, L. Molecular Properties by Quantum Monte Carlo: An Investigation on the Role of the Wave Function Ansatz and the Basis Set in the Water Molecule. J. Chem. Theory Comput. 2013, 9, 4332--4350

    2013

  18. [26]

    Quantum Monte Carlo with very large multideterminant wavefunctions

    Scemama, A.; Applencourt, T.; Giner, E.; Caffarel, M. Quantum Monte Carlo with very large multideterminant wavefunctions . J. Comput. Chem. 2016, 37, 1866--1875

    2016

  19. [27]

    Communication: Toward an improved control of the fixed-node error in quantum Monte Carlo: The case of the water molecule

    Caffarel, M.; Applencourt, T.; Giner, E.; Scemama, A. Communication: Toward an improved control of the fixed-node error in quantum Monte Carlo: The case of the water molecule. J. Chem. Phys. 2016, 144, 151103

    2016

  20. [28]

    Taming the fixed-node error in diffusion Monte Carlo via range separation

    Scemama, A.; Giner, E.; Benali, A.; Loos, P.-F. Taming the fixed-node error in diffusion Monte Carlo via range separation. J. Chem. Phys. 2020, 153, 174107

    2020

  21. [29]

    u chow, A.; Grimme, S. Toward the exact solution of the electronic Schr \

    Korth, M.; L \"u chow, A.; Grimme, S. Toward the exact solution of the electronic Schr \"o dinger equation for noncovalent molecular interactions: worldwide distributed quantum Monte Carlo calculations. J. Phys. Chem. A 2008, 112, 2104--2109

    2008

  22. [30]

    Quantum Monte Carlo methods describe noncovalent interactions with subchemical accuracy

    Dubecky, M.; Jurecka, P.; Derian, R.; Hobza, P.; Otyepka, M.; Mitas, L. Quantum Monte Carlo methods describe noncovalent interactions with subchemical accuracy. J. Chem. Theory Comput. 2013, 9, 4287--4292

    2013

  23. [31]

    Quantum Monte Carlo for noncovalent interactions: an efficient protocol attaining benchmark accuracy

    Dubeck \`y , M.; Derian, R.; Jure c ka, P.; Mitas, L.; Hobza, P.; Otyepka, M. Quantum Monte Carlo for noncovalent interactions: an efficient protocol attaining benchmark accuracy. Phys. Chem. Chem. Phys. 2014, 16, 20915--20923

    2014

  24. [32]

    D.; Fal'ko, V

    Mostaani, E.; Drummond, N. D.; Fal'ko, V. I. Quantum Monte Carlo Calculation of the Binding Energy of Bilayer Graphene. Phys. Rev. Lett. 2015, 115, 115501

    2015

  25. [33]

    All-Electron Quantum Monte Carlo with Jastrow Single Determinant Ansatz: Application to the Sodium Dimer

    Nakano, K.; Maezono, R.; Sorella, S. All-Electron Quantum Monte Carlo with Jastrow Single Determinant Ansatz: Application to the Sodium Dimer . J. Chem. Theory Comput. 2019, 15, 4044--4055

    2019

  26. [34]

    G.; Klime s , J.; Tkatchenko, A.; Alf \` e , D.; Michaelides, A

    Zen, A.; Brandenburg, J. G.; Klime s , J.; Tkatchenko, A.; Alf \` e , D.; Michaelides, A. Fast and accurate quantum Monte Carlo for molecular crystals . Proc. Natl. Acad. Sci. U.S.A. 2018, 115, 1724--1729

    2018

  27. [35]

    Quantum Monte Carlo benchmarking of large noncovalent complexes in the L7 benchmark set

    Benali, A.; Shin, H.; Heinonen, O. Quantum Monte Carlo benchmarking of large noncovalent complexes in the L7 benchmark set . J. Chem. Phys. 2020, 153, 194113

    2020

  28. [36]

    S.; Nagy, P

    Al-Hamdani, Y. S.; Nagy, P. R.; Zen, A.; Barton, D.; Kállay, M.; Brandenburg, J. G.; Tkatchenko, A. Interactions between large molecules pose a puzzle for reference quantum mechanical methods. Nat. Commun. 2021, 12, 3927

    2021

  29. [37]

    Raghav, A.; Maezono, R.; Hongo, K.; Sorella, S.; Nakano, K. J. Chem. Theory Comput. 2023, 19, 2222--2229

    2023

  30. [38]

    Noncovalent Interactions by Quantum Monte Carlo

    Dubeck \' y , M.; Mitas, L.; Jure c ka, P. Noncovalent Interactions by Quantum Monte Carlo . Chem. Rev. 2016, 116, 5188--5215

    2016

  31. [39]

    Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons

    Nakano, K.; Sorella, S.; Alfè, D.; Zen, A. Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons. J. Chem. Theory Comput. 2024, 20, 4591--4604

    2024

  32. [40]

    a fer, T.; Irmler, A.; Gallo, A.; Gr \

    Sch \"a fer, T.; Irmler, A.; Gallo, A.; Gr \"u neis, A. Understanding discrepancies of wavefunction theories for large molecules. arXiv preprint arXiv:2407.01442 2024,

    2024

  33. [41]

    B.; van Duijneveldt-van de Rijdt, J

    Van Duijneveldt, F. B.; van Duijneveldt-van de Rijdt, J. G.; van Lenthe, J. H. State of the art in counterpoise theory. Chem. Rev. 1994, 94, 1873--1885

    1994

  34. [42]

    Dunning, T. H. A road map for the calculation of molecular binding energies. J. Phys. Chem. A 2000, 104, 9062--9080

    2000

  35. [43]

    Dunning Jr, T. H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 1989, 90, 1007--1023

    1989

  36. [44]

    The calculation of small molecular interactions by the differences of separate total energies

    Boys, S.; Bernardi, F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Mol. Phys. 1970, 19, 553--566

    1970

  37. [45]

    Diffusion Monte Carlo method for barrier heights of multiple proton exchanges and complexation energies in small water, ammonia, and hydrogen fluoride clusters

    Zhou, X.; Huang, Z.; He, X. Diffusion Monte Carlo method for barrier heights of multiple proton exchanges and complexation energies in small water, ammonia, and hydrogen fluoride clusters. J. Chem. Phys. 2024, 160, 054103

    2024

  38. [46]

    gold standard,

    R ez\'a c , J.; Hobza, P. Describing noncovalent interactions beyond the common approximations: how accurate is the “gold standard,” CCSD (T) at the complete basis set limit? J. Chem. Theory Comput. 2013, 9, 2151--2155

    2013

  39. [47]

    C.; Melton, C

    Bennett, M. C.; Melton, C. A.; Annaberdiyev, A.; Wang, G.; Shulenburger, L.; Mitas, L. A new generation of effective core potentials for correlated calculations . J. Chem. Phys 2017, 147, 224106

    2017

  40. [48]

    C.; Wang, G.; Annaberdiyev, A.; Melton, C

    Bennett, M. C.; Wang, G.; Annaberdiyev, A.; Melton, C. A.; Shulenburger, L.; Mitas, L. A new generation of effective core potentials from correlated calculations: 2nd row elements . J. Chem. Phys. 2018, 149, 104108

    2018

  41. [49]

    TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo

    Nakano, K.; Attaccalite, C.; Barborini, M.; Capriotti, L.; Casula, M.; Coccia, E.; Dagrada, M.; Genovese, C.; Luo, Y.; Mazzola, G.; Zen, A.; Sorella, S. TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo . J. Chem. Phys. 2020, 152, 204121

    2020

  42. [50]

    Diffusion Monte Carlo method with lattice regularization

    Casula, M.; Filippi, C.; Sorella, S. Diffusion Monte Carlo method with lattice regularization. Phys. Rev. Lett. 2005, 95, 100201

    2005

  43. [51]

    G.; Michaelides, A.; Alfè, D

    Zen, A.; Brandenburg, J. G.; Michaelides, A.; Alfè, D. A new scheme for fixed node diffusion quantum Monte Carlo with pseudopotentials: Improving reproducibility and reducing the trial-wave-function bias. J. Chem. Phys. 2019, 151, 134105

    2019

  44. [52]

    G.; Ammar, A.; Hapka, M.; Pernal, K.; Shinde, R.; Landinez Borda, E

    Posenitskiy, E.; Chilkuri, V. G.; Ammar, A.; Hapka, M.; Pernal, K.; Shinde, R.; Landinez Borda, E. J.; Filippi, C.; Nakano, K.; Kohulák, O.; Sorella, S.; de Oliveira Castro, P.; Jalby, W.; Ríos, P. L.; Alavi, A.; Scemama, A. TREXIO: A file format and library for quantum chemis...

    2023

  45. [53]

    C.; Blunt, N

    Sun, Q.; Berkelbach, T. C.; Blunt, N. S.; Booth, G. H.; Guo, S.; Li, Z.; Liu, J.; McClain, J. D.; Sayfutyarova, E. R.; Sharma, S.; others PySCF: the Python-based simulations of chemistry framework. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2018, 8, e1340

    2018

  46. [54]

    S.; Bogdanov, N

    Sun, Q.; Zhang, X.; Banerjee, S.; Bao, P.; Barbry, M.; Blunt, N. S.; Bogdanov, N. A.; Booth, G. H.; Chen, J.; Cui, Z. H.; Eriksen, J. J.; Gao, Y.; Guo, S.; Hermann, J.; Hermes, M. R.; Koh, K.; Koval, P.; Lehtola, S.; Li, Z.; Liu, J.; Mardirossian, N.; McClain, J. D.; Motta, M....

    2020

  47. [55]

    P.; Zunger, A

    Perdew, J. P.; Zunger, A. Self-interaction correction to density-functional approximations for many-electron systems. Phys. Rev. B 1981, 23, 5048

    1981

  48. [56]

    Green function Monte Carlo with stochastic reconfiguration

    Sorella, S. Green function Monte Carlo with stochastic reconfiguration. Phys. Rev. Lett. 1998, 80, 4558

    1998

  49. [57]

    a np \"a \

    Kim, J.; Baczewski, A. D.; Beaudet, T. D.; Benali, A.; Bennett, M. C.; Berrill, M. A.; Blunt, N. S.; Borda, E. J. L.; Casula, M.; Ceperley, D. M.; Chiesa, S.; Clark, B. K.; Clay, R. C.; Delaney, K. T.; Dewing, M.; Esler, K. P.; Hao, H.; Heinonen, O.; Kent, P. R. C.; Krogel, J....

    2018

  50. [58]

    R.; Annaberdiyev, A.; Benali, A.; Bennett, M

    Kent, P. R.; Annaberdiyev, A.; Benali, A.; Bennett, M. C.; Borda, E. J. L.; Doak, P.; Hao, H.; Jordan, K. D.; Krogel, J. T.; Kylänpaä, I.; Lee, J.; Luo, Y.; Malone, F. D.; Melton, C. A.; Mitas, L.; Morales, M. A.; Neuscamman, E.; Reboredo, F. A.; Rubenstein, B.; Saritas, K.; U...

    2020

  51. [59]

    Neese, F.; Valeev, E. F. Revisiting the Atomic Natural Orbital Approach for Basis Sets : Robust Systematic Basis Sets for Explicitly Correlated and Conventional Correlated Ab Initio Methods ? J. Chem. Theory Comput. 2011, 7, 33--43

    2011

  52. [60]

    TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods

    Nakano, K.; Kohulák, O.; Raghav, A.; Casula, M.; Sorella, S. TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods . J. Chem. Phys. 2023, 159, 224801

    2023

  53. [61]

    Energy-consistent pseudopotentials for quantum Monte Carlo calculations

    Burkatzki, M.; Filippi, C.; Dolg, M. Energy-consistent pseudopotentials for quantum Monte Carlo calculations. J. Chem. Phys. 2007, 126

    2007

  54. [62]

    """"""""

    R ez \'a c , J.; Jure c ka, P.; Riley, K. E.; C ern \`y , J.; Valdes, H.; Pluh \'a c kov \'a , K.; Berka, K.; R ez \'a c , T.; Pito n \'a k, M.; Vondr \'a s ek, J.; Hobza, P. Quantum chemical benchmark energy and geometry database for molecular clusters and complex molecular s...

    2008

  55. [63]

    L.; Cococcioni, M.; Dabo, I.; others QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

    Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G. L.; Cococcioni, M.; Dabo, I.; others QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter. 2009, 21, 395502

    2009

  56. [64]

    Alf\`e, D.; Gillan, M. J. Efficient localized basis set for quantum Monte Carlo calculations on condensed matter. Phys. Rev. B 2004, 70, 161101

    2004

  57. [65]

    Beyond the Locality Approximation in the Standard Diffusion Monte Carlo Method

    Casula, M. Beyond the Locality Approximation in the Standard Diffusion Monte Carlo Method. Phys. Rev. B 2006, 74, 161102

    2006

  58. [66]

    O"Dҟ 6 avؾ Z騮R+D`e J ɶcl cY .+ qL: @ @ @ @ @ |؜,E p #rΙg5V2:< 2U>+dVZJ[m p VaT I k ϰaᒘnQx[e dX + <Py _y p! @ @ @ @ p h

    Zen, A.; Sorella, S.; Gillan, M. J.; Michaelides, A.; Alf \` e , D. Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step . Phys. Rev. B 2016, 93, 241118(R) mcitethebibliography sup.tex0000664000000000000000000006174614722526240011131 0ustar ro...

    2016

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.