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Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This study establishes that fixed-node diffusion Monte Carlo is reproducible across eleven community codes, provided the trial wave function's determinant is shared and the non-local pseudopotential is handled by the T-move, DLA, or DTM…

desk verdict A genuinely important cross-code FN-DMC benchmark whose core finding holds up, but the LA attribution is partly confounded by two codes using different orbitals. read the letter →

arxiv 2501.12950 v3 pith:3NVEN7IB submitted 2025-01-22 physics.comp-ph cond-mat.mtrl-sciphysics.chem-ph

Flaviano Della Pia , Benjamin X. Shi , Yasmine S. Al-Hamdani , Dario Alfè , Tyler A. Anderson , Matteo Barborini , Anouar Benali , Michele Casula
show 25 more authors
Neil D. Drummond Matúš Dubecký Claudia Filippi Paul R. C. Kent Jaron T. Krogel Pablo López Ríos Arne Lüchow Ye Luo Angelos Michaelides Lubos Mitas Kosuke Nakano Richard J. Needs Manolo C. Per Anthony Scemama Jil Schultze Ravindra Shinde Emiel Slootman Sandro Sorella Alexandre Tkatchenko Mike Towler Cyrus J. Umrigar Lucas K. Wagner William A. Wheeler Haihan Zhou Andrea Zen
This is my paper · ORCID
classification physics.comp-phcond-mat.mtrl-sciphysics.chem-ph
keywords fixed-nodediffusionMonteCarloreproducibilitypseudopotentiallocalizationT-movedeterminantlocalityapproximationwater-methanedimertime-stepextrapolationquantumcodes
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 community-wide study asks whether fixed-node diffusion Monte Carlo (FN-DMC), a widely used many-body electronic structure method, gives the same answer when eleven independently written codes compute the same molecule. The paper's answer, for the water-methane dimer test case, is yes, provided the trial wave function's determinantal part is the same and the non-local pseudopotential is treated with the T-move, determinant locality, or determinant T-move approximation. Under those schemes the eleven codes' interaction energies agree to within about 3 meV after extrapolation to zero time step, while the older locality approximation scatters over roughly 25 meV. The paper attributes the disagreement to the pseudopotential localization scheme, not to the codes themselves. If right, the result strengthens the case for FN-DMC as a portable reference for molecular energies.

What carries the argument

The central object is the treatment of non-local pseudopotentials in the DMC propagator. Non-local pseudopotentials introduce a sign-violating term into the imaginary-time Green's function, and four localization schemes replace it by an effective local operator: the locality approximation (LA) localizes on the full trial wave function; the T-move approximation (TM) localizes only the sign-violating part, restoring the upper-bound property; the determinant locality approximation (DLA) localizes on the determinantal part only; and the determinant T-move approximation (DTM) applies determinant localization to the sign-violating part while keeping the full wave function elsewhere. The quantitative machinery is a per-code probability distribution P($\alpha$)(E) = (1/N) sum of per-code Gaussians, whose standard deviation separates stochastic error from cross-code disagreement, plus polynomial extrapolation E(tau)=A+B tau+C $tau^{2}$+D $tau^{3}$ to the zero-time-step limit.

What would settle it

Run the LA protocol again with all codes importing one identical orbital file (or with two codes swapping orbital files) and compare the ~25 meV spread; if it persists, the localization scheme is the cause, and if it collapses, orbital or node differences were the cause.

Watch

Extended reading notes

Core claim

Using a single Slater determinant from LDA density functional theory as the fixed-node surface, shared ccECP pseudopotentials and basis, and only code-specific Jastrow factors, the authors find that the zero-time-step FN-DMC interaction energy of the water-methane dimer is reproducible across all eleven codes when the non-local pseudopotential is localized with the T-move (TM), determinant locality approximation (DLA), or determinant T-move (DTM) scheme. The spread across codes is about 3 meV for TM and DLA and about 1 meV for DTM (four codes), and the averaged values sit close to the CCSDT(Q) reference of about -27 meV. With the older locality approximation (LA), the same codes disagree by up to about 21 meV (standard deviation 7 meV), showing that the localization scheme, not the underlying DMC algorithm, is the dominant source of non-reproducibility. The authors also show that the time step must be small: at tau=0.04 a.u. DLA results scatter over more than 60 meV, narrowing onto the common value only near tau=0.0025 a.u.

Load-bearing premise

The load-bearing premise is that every code used the same determinantal component, so the fixed-node surfaces coincide; the supporting information records that two codes generated orbitals with different quantum-chemistry packages, and any orbital differences would shift the nodes and could absorb part of the LA scatter.

Editorial extensions

If this is right

  • Users of FN-DMC who employ TM, DLA, or DTM can expect published interaction energies from independent codes to be mutually consistent at the few-meV level, given identical determinants and converged time steps.
  • The older locality approximation should not be relied on for quantitative cross-code comparisons; its large spread is an artifact of the localization choice, not of the stochastic method.
  • Time-step convergence must be checked explicitly: at tau=0.04 a.u. the DLA interaction-energy spread exceeds 60 meV, while tau=0.0025 a.u. is adequate for this system.
  • Total energies, not just energy differences, are reproducible to sub-millihartree precision under TM, DLA, and DTM, with standard deviations below about 6 meV across codes.
  • The study supplies a benchmark protocol for future QMC comparisons: fix the determinant, share the pseudopotential and basis, and extrapolate to zero time step.

Reading between the lines

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

  • The mechanistic attribution would be sharper if every code used bitwise identical orbitals; the supporting information records that two codes generated orbitals with different quantum-chemistry packages, so a fraction of the LA scatter could in principle originate from slightly different fixed-node surfaces rather than localization error alone.
  • A direct testable extension is to run the same protocol on a heavier-element dimer with a stronger non-local channel; the prediction would be that TM, DLA, and DTM still collapse the code-to-code spread while LA widens it.
  • For periodic solids, the same localization-scheme contrast is likely to control reproducibility, but finite-size corrections and Brillouin-zone sampling add error sources that this molecular benchmark cannot constrain.
  • The per-code probability-distribution formalism could serve as a general reproducibility metric for future QMC benchmark studies, cleanly separating statistical noise from systematic code disagreement.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This community benchmark assesses the reproducibility of fixed-node diffusion Monte Carlo (FN-DMC) across eleven independently developed codes, using the water–methane dimer as a test case. All codes were asked to use the same ccECP pseudopotential, the same ccECP-ccpVTZ basis set, and a single Slater determinant of LDA DFT orbitals, with four pseudopotential localization schemes compared: locality approximation (LA), T-move (TM), determinant locality approximation (DLA), and determinant T-move (DTM). Interaction energies and total energies are extrapolated to the zero-time-step limit using polynomial fits, and the results are compared with a CCSDT(Q) reference of -26.6 meV. The main finding is that the LA shows a large cross-code spread (standard deviation about 7 meV), while TM, DLA, and DTM give much narrower distributions (standard deviations of about 2, 1.2, and 0.8 meV, respectively), leading the authors to conclude that FN-DMC is reproducible when TM, DLA, or DTM is used and when time-step convergence is carefully controlled.

Significance. If the result holds, this is an important and timely contribution: it is, to my knowledge, the first systematic multi-code reproducibility study of FN-DMC with a fixed pseudopotential and target system, and it provides a concrete recommendation (prefer TM/DLA/DTM over LA, and extrapolate carefully in time step) that could become standard practice. The paper's strengths include the participation of eleven independent code teams, the use of an external CCSDT(Q) reference, the publication of raw data and analysis notebooks on GitHub, and the detailed per-code supplementary notes that document algorithmic choices. The significance is moderated by two issues discussed in the major comments: the incomplete control of the determinantal component for two of the eleven codes, and the fact that no single localization scheme covers all eleven codes. These do not invalidate the core observation that TM/DLA/DTM reduce cross-code scatter, but they do require the authors either to strengthen the controlled comparison or to qualify the central mechanistic attribution.

major comments (3)
  1. [Sec. 3 (Methods) and SI S8.9, S8.10, Table S5] The controlled comparison that underpins the mechanistic conclusion is incomplete for two of the eleven codes. The Methods section states that all codes used the same pseudopotential, basis set, and a single Slater determinant from LDA DFT (with TREXIO exchange for some codes), but SI S8.10 reports that QWalk used GAMESS orbitals and SI S8.9 reports that QMeCha used ORCA orbitals. Table S5 shows that these two codes are exactly the most extreme LA outliers (QWalk -38.8 meV, QMeCha -35.0 meV against an LA mean of -27.5 meV). The LA distribution therefore mixes the effect of the localization scheme with possible differences in the fixed-node surfaces. Please rerun QWalk and QMeCha with the shared PySCF orbitals for all the schemes they report, or at minimum present the LA statistics with and without these two codes and discuss whether the remaining spread still supports the attribution of the ~25 meV LA variability to the localization algorithm. In addition, please discuss explicitly why TM, DLA, and DTM are expected to be less sensitive to small nodal differences, since the current text only asserts this indirectly.
  2. [Abstract, Sec. 4 (Conclusions), Table S5] The claim that agreement is achieved 'across all eleven codes' when employing TM, DLA, and DTM is stronger than the data support. Table S5 reports the number of codes contributing to each scheme: LA 8, TM 9, DLA 9, and DTM 4; no single scheme covers all eleven codes. The statement in the Conclusions that 'agreement in the interaction energy across all eleven codes is achieved in the limit of zero time step when employing the TM, DLA, and DTM approximations' should be rephrased to specify which codes are included in each scheme and to clarify whether the eleven-code reproducibility is meant collectively (i.e., each code is represented in at least one reproducible scheme) or within each individual scheme. The Abstract's phrase 'for the same choice of determinantal component' is likewise conditional on the shared-orbital protocol that is incomplete for QWalk and QMeCha (see Major Comment 1).
  3. [SI S5 and Table S1, main-text Eq. 11] The zero-time-step extrapolation uses a polynomial of degree 2 or 3, chosen per code and per quantity, but the selection criterion is not stated. Because the central comparison between the four localization schemes is made on the extrapolated energies, the degree choice is load-bearing. Please state the rule used to select between quadratic and cubic fits (e.g., a reduced-chi-squared threshold, an F-test, or an information criterion), and provide a stability analysis showing that the reported extrapolated interaction energies and the resulting cross-code conclusions are insensitive to the degree choice (for example, by comparing quadratic and cubic extrapolations for all fits where both are feasible). The currently reported reduced chi-squared and RMSR values are useful, but they do not by themselves justify the model selection.
minor comments (6)
  1. [SI Table S5 caption] The 'Mean' column reports the standard deviation of the probability distribution defined in Eq. 4 of the main text, not the standard error of the mean; please relabel this column or revise the caption to avoid ambiguity.
  2. [Main text, Eqs. 2-4] The construction of Pα(E) uses the single-point estimates Eα,i and σα,i without accounting for the uncertainty in these estimates. This is a reasonable practical approximation, but it likely underestimates the spread of the true distribution; a bootstrap or a t-like distribution would be a more conservative alternative. This does not affect the qualitative conclusion.
  3. [Sec. 2, total energies discussion] The statement that the standard deviations of the TM, DLA, and DTM total energy distributions are 'close to the theoretical minimum allowed by the precision of the performed FN-DMC simulations' would be easier to verify if the two contributions to Eq. 4 were reported separately (the statistical-error term and the deviation-from-mean term). Please add a breakdown for at least one representative case.
  4. [Abstract and Sec. 4] The broad claim 'Yes, FN-DMC is reproducible (when handled with care)' would be better qualified as 'for the water-methane dimer benchmark with single-determinant trial wave functions and the tested ccECP pseudopotential,' since the study is a single-system, single-node-surface test.
  5. [SI S8.9 and S8.10] For QMeCha and QWalk, please confirm explicitly that the ccECP and ccECP-ccpVTZ basis set use exactly the same parameters and normalization conventions as the other codes, and whether the GAMESS/ORCA orbital coefficients have been made available in TREXIO format for reproducibility.
  6. [General] There are several typographical and formatting issues, including ligature artifacts in the abstract ('affirming'), and the phrase 'the error bar associated the the Mean' in the Table S5 caption. Please proofread the final version.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the cross-code benchmark is externally anchored to CCSDT(Q) and to eleven independently developed codes; the DLA/DTM agreement is an empirical measurement, not a result forced by definition. One control caveat about orbital sources is a validity concern, not a circular step.

full rationale

The central claim ('Yes, FN-DMC is reproducible (when handled with care)', with the LA spread attributed to the pseudopotential localization scheme while TM/DLA/DTM agree) is supported by more than 300 FN-DMC calculations across eleven independently developed community codes, compared against an external CCSDT(Q) reference value of -26.6 meV for the water-methane dimer interaction energy. No fitted parameter is renamed as a prediction: the zero-time-step extrapolation E(tau) = A + B*tau + C*tau^2 + D*tau^3 is a standard convergence procedure whose fit quality is reported (reduced chi-squared and RMSR in Table S1), and the CCSDT(Q) value enters only as a comparison line in Fig. 1, not as an input to any code's energy evaluation. The schemes DLA and DTM were introduced in co-authored prior work (Zen et al. 2019, ref. [17]; also Casula 2006 and Anderson & Umrigar 2021), so the paper partly validates algorithms from its own lineage, and a mild self-citation burden exists. However, the reproducibility statements rest on newly computed cross-code data that were free to disagree: Table S5 reports an LA spread of 21.3 meV (max-dist) across 8 codes versus DLA 3.8 meV across 9 codes and DTM 2.1 meV across 4 codes, and the total energy distributions are close to the stochastic floor (main text, Fig. 4 discussion). The definitions of DLA/DTM (localizing the non-local pseudopotential on the determinant part of the wave function, which is shared across codes) make partial agreement plausible by construction, but they do not force independent implementations to produce matching total energies after different Jastrow optimization and time-step extrapolation; the measured agreement at tau -> 0 is therefore a nontrivial consistency check rather than a reduction of the conclusion to its inputs. The main text statement in Section 3 that this 'choice ensures that any observed variation is due to implementation-level or algorithmic factors rather than differences in the choice of geometry, pseudopotential, basis set, or single-particle orbitals' is, however, contradicted by the paper's own Supplementary Information: SI S8.9 states that QMeCha used ORCA Kohn-Sham orbitals and SI S8.10 states that QWalk used GAMESS orbitals, while the other codes used PySCF orbitals. Table S1 shows that these are exactly the two most extreme LA outliers (QMeCha -35.0 meV, QWalk -38.8 meV versus the LA mean of -27.5 meV).

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

The central claim rests on standard FN-DMC theory, on the empirical polynomial time-step extrapolation, and on the assumption that all codes share the same determinant. Two fitted extrapolation models are the only free parameters; no new physical entities are postulated.

free parameters (2)
  • Time-step extrapolation polynomial coefficients (A, B, C, D) = Per code and scheme, see Tables S1 to S4
    The zero-time-step limits that define the consensus are obtained by least-squares fitting E(tau) = A + B*tau + C*tau^2 + D*tau^3 to each code's energies at finite tau. The coefficients are not derived from the short-time Green's function, and the central claim depends on the fit.
  • LRDMC lattice-spacing extrapolation coefficients (A, B, C) = Per TurboR VB-LRDMC run, see Eq. 12 and Table S1
    Used to map lattice-regularized DMC results to the zero time-step limit through E(a^2) = A + B*a^2 + C*a^4; this is the same role as the polynomial fit for standard DMC.
assumptions (5)
  • domain assumption Fixed-node approximation: the FN-DMC energy is the exact ground-state energy of the Hamiltonian constrained to the nodal surface of the trial wave function
    This is the standard theory of FN-DMC, invoked throughout the paper as the basis for treating the computed total energies as comparable across codes.
  • domain assumption The common Slater determinant gives the same fixed-node surface across all codes regardless of the Jastrow factor
    The paper relies on this to attribute residual differences within DLA and DTM to algorithmic details. The Methods section states that all codes used the same determinant, but the SI shows that some codes generated orbitals with different DFT packages, so this assumption may not be fully satisfied.
  • ad hoc to paper Polynomial form of the time-step bias, Eq. 11: E(tau) = A + B*tau + C*tau^2 + D*tau^3
    Used to extrapolate all energies to tau = 0. The form is empirical and not derived from the DMC short-time propagator. The central consensus depends on this extrapolation.
  • domain assumption The zero-lattice-spacing limit of LRDMC is equivalent to the zero-time-step limit of DMC
    This is used to include TurboR VB-LRDMC results in the DMC comparison, as stated in SI S5.
  • domain assumption The all-electron CCSDT(Q) complete-basis limit is an accurate reference for the water-methane interaction energy
    The CCSDT(Q) value of -26.6 meV is used as the external benchmark; the extrapolation procedure is described in SI S3 and follows established coupled-cluster methodology.

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Cite this review

Pith. "Pith review of Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer." pith.science (2026). https://pith.science/paper/3NVEN7IB

@misc{pith2026250112950,
  author       = {Pith},
  title        = {Pith review of: Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3NVEN7IB}},
  note         = {Machine review of arXiv:2501.12950}
}
read the original abstract

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to assess the reproducibility of the method, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes-highlighting the maturity and robustness of the method as a tool for open and reliable computational science.

Figures

Figures reproduced from arXiv: 2501.12950 by the authors.

Figure 1
Figure 1. FN-DMC interaction energy of the methane-water dimer with four different [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Probability distribution Pα(E) (Eq. 2) of the FN-DMC interaction energy of the methane-water dimer for four different schemes for treating NLPPs. The probability distribution for the LA method is spread across a large energy range of ca. 25 meV, showing the disagreement among different codes. The probability distribution is instead much narrower when the TM, DLA, and DTM algorithms are employed, implying the agreeme… view at source ↗
Figure 3
Figure 3. Convergence with respect to the simulation time step of the probability dis [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Probability distribution Pα(E) (Eq. 2) of the FN-DMC total energy (Hartree) of the methane-water dimer (left), methane (middle), and water (right), for four different schemes to treat NLPPs. The bars under the distributions indicate the standard devia￾tion. this was su…

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Reference graph

Works this paper leans on

73 extracted references · 73 canonical work pages · cited by 1 Pith paper

  1. [1]

    A new generation of effective core potentials for correlated calculations,

    M. C. Bennett, C. A. Melton, A. Annaberdiyev, G. Wang, L. Shulenburger, and L. Mitas, “A new generation of effective core potentials for correlated calculations,” The Journal of Chemical Physics, vol. 147, p. 224106, 12 2017

    work page 2017

  2. [2]

    A new generation of effective core potentials from correlated calculations: 3d transition metal series,

    A. Annaberdiyev, G. Wang, C. A. Melton, M. C. Bennett, L. Shulenburger, and L. Mitas, “A new generation of effective core potentials from correlated calculations: 3d transition metal series,” The Journal of Chemical Physics, vol. 149, p. 134108, 10 2018

    work page 2018

  3. [3]

    PySCF: the Python-based simulations of chemistry framework,

    Q. Sun, T. C. Berkelbach, N. S. Blunt, G. H. Booth, S. Guo, Z. Li, J. Liu, J. D. Mc- Clain, E. R. Sayfutyarova, S. Sharma, S. Wouters, and G. K.-L. Chan, “PySCF: the Python-based simulations of chemistry framework,” Wiley Interdiscip. Rev. Comput. Mol. Sci., vol. 8, no. 1, p. e1340, 2018

    work page 2018

  4. [4]

    Recent developments in the PySCF program package,

    Q. Sun, X. Zhang, S. Banerjee, P. Bao, M. Barbry, N. S. Blunt, N. A. Bogdanov, G. H. Booth, J. Chen, Z.-H. Cui, J. J. Eriksen, Y. Gao, S. Guo, J. Hermann, M. R. Hermes, K. Koh, P. Koval, S. Lehtola, Z. Li, J. Liu, N. Mardirossian, J. D. McClain, M. Motta, B. Mussard, H. Q. Pham, A. Pulkin, W. Purwanto, P. J. Robinson, E. Ronca, E. R. Sayfutyarova, M. Sche...

    work page 2020

  5. [5]

    Self-interaction correction to density-functional ap- proximations for many-electron systems,

    J. P. Perdew and A. Zunger, “Self-interaction correction to density-functional ap- proximations for many-electron systems,” Phys. Rev. B, vol. 23, pp. 5048–5079, May 1981

    work page 1981

  6. [6]

    Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step,

    A. Zen, S. Sorella, M. J. Gillan, A. Michaelides, and D. Alfè, “Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step,” Phys. Rev. B, vol. 93, p. 241118, Jun 2016

    work page 2016

  7. [7]

    Revisiting the Atomic Natural Orbital Approach for Basis Sets: Robust Systematic Basis Sets for Explicitly Correlated and Conventional Correlated ab initio Methods?,

    F. Neese and E. F. Valeev, “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., vol. 7, pp. 33–43, Jan. 2011

    work page 2011

  8. [8]

    Size-consistent variational ap- proaches to nonlocal pseudopotentials: Standard and lattice regularized diffu- sion Monte Carlo methods revisited,

    M. Casula, S. Moroni, S. Sorella, and C. Filippi, “Size-consistent variational ap- proaches to nonlocal pseudopotentials: Standard and lattice regularized diffu- sion Monte Carlo methods revisited,” The Journal of Chemical Physics, vol. 132, p. 154113, 04 2010

    work page 2010

Show all 73 references
  1. [9]

    Nonlocal pseudopotentials and time-step errors in diffusion monte carlo,

    T. A. Anderson and C. J. Umrigar, “Nonlocal pseudopotentials and time-step errors in diffusion monte carlo,” J. Chem. Phys., vol. 154, p. 214110, 2021

    2021

  2. [10]

    Nonlocal pseudopotentials and diffusion Monte Carlo,

    L. Mitáš, E. L. Shirley, and D. M. Ceperley, “Nonlocal pseudopotentials and diffusion Monte Carlo,” The Journal of Chemical Physics, vol. 95, pp. 3467–3475, 09 1991

    1991

  3. [11]

    Beyond the Locality Approximation in the Standard Diffusion Monte Carlo Method,

    M. Casula, “Beyond the Locality Approximation in the Standard Diffusion Monte Carlo Method,” Phys. Rev. B, vol. 74, p. 161102, Oct. 2006. 47

    2006

  4. [12]

    Proof for an upper bound in fixed-node Monte Carlo for lattice fermions,

    D. F. B. ten Haaf, H. J. M. van Bemmel, J. M. J. van Leeuwen, W. van Saarloos, and D. M. Ceperley, “Proof for an upper bound in fixed-node Monte Carlo for lattice fermions,” Phys. Rev. B, vol. 51, pp. 13039–13045, May 1995

    1995

  5. [13]

    Relativistic effective potentials in quantum Monte-Carlo calculations,

    M. M. Hurley and P. A. Christiansen, “Relativistic effective potentials in quantum Monte-Carlo calculations,” J. Chem. Phys., vol. 86, p. 1069, 1987

    1987

  6. [14]

    Valence quantum Monte Carlo with ab initio effective core potentials,

    B. Hammond, P. Reynolds, and W. Lester, “Valence quantum Monte Carlo with ab initio effective core potentials,” The Journal of Chemical Physics, vol. 87, no. 2, pp. 1130–1136, 1987

    1987

  7. [15]

    Reduction of the computational effort in quantum Monte Carlo calculations with pseudopotentials through a change of the projection operators,

    H. Flad, A. Savin, and H. Preuss, “Reduction of the computational effort in quantum Monte Carlo calculations with pseudopotentials through a change of the projection operators,” The Journal of Chemical Physics, vol. 97, pp. 459–463, 07 1992

    1992

  8. [16]

    Caffarel, T

    M. Caffarel, T. Applencourt, E. Giner, and A. Scemama, Using CIPSI Nodes in Diffusion Monte Carlo, ch. 2, pp. 15–46. American Chemical Society, 2016

    2016

  9. [17]

    A New Scheme for Fixed Node Diffusion Quantum Monte Carlo with Pseudopotentials: Improving Repro- ducibility and Reducing the Trial-Wave-Function Bias,

    A. Zen, J. G. Brandenburg, A. Michaelides, and D. Alfè, “A New Scheme for Fixed Node Diffusion Quantum Monte Carlo with Pseudopotentials: Improving Repro- ducibility and Reducing the Trial-Wave-Function Bias,” J. Chem. Phys., vol. 151, p. 134105, 10 2019

    2019

  10. [18]

    Speeding up ab initio diffusion Monte Carlo simulations by a smart lattice regularization,

    K. Nakano, R. Maezono, and S. Sorella, “Speeding up ab initio diffusion Monte Carlo simulations by a smart lattice regularization,” Phys. Rev. B, vol. 101, p. 155106, Apr 2020

    2020

  11. [19]

    A Diffusion Monte-Carlo al- gorithm with very small time-step errors,

    C. J. Umrigar, M. P. Nightingale, and K. J. Runge, “A Diffusion Monte-Carlo al- gorithm with very small time-step errors,” J. Chem. Phys., vol. 99, pp. 2865–2890, Aug 15 1993

    1993

  12. [20]

    Generic expansion of the Jastrow correlation factor in polynomials satisfying symmetry and cusp conditions,

    A. Lüchow, A. Sturm, C. Schulte, and K. H. Mood, “Generic expansion of the Jastrow correlation factor in polynomials satisfying symmetry and cusp conditions,” J. Chem. Phys., vol. 142, p. 084111, 2 2015

    2015

  13. [21]

    Quantum Monte Carlo study of composite fermions in quantum dots: The effect of Landau-level mixing,

    A. D. Güçlü, G. S. Jeon, C. J. Umrigar, and J. K. Jain, “Quantum Monte Carlo study of composite fermions in quantum dots: The effect of Landau-level mixing,” Phys. Rev. B, vol. 72, p. 205327, Nov. 2005

    2005

  14. [22]

    R. J. Needs, M. D. Towler, N. D. Drummond, and P. López Ríos, CASINO User’s Guide. Cambridge University. Version 2.13, https://casinoqmc. net/casino_manual_dir/casino_manual.pdf

  15. [23]

    Vari- ational and diffusion quantum Monte Carlo calculations with the CASINO code,

    R. J. Needs, M. D. Towler, N. D. Drummond, P. López Ríos, and J. R. Trail, “Vari- ational and diffusion quantum Monte Carlo calculations with the CASINO code,” The Journal of Chemical Physics, vol. 152, p. 154106, 04 2020

    2020

  16. [24]

    J.; Shepard, S.; Slootman, E.; Cuzzocrea, A.; Azizi, V.; Lopez-Tarifa, P.; Renaud, N.; Umrigar, C

    Shinde, R.; Landinez Borda, E. J.; Shepard, S.; Slootman, E.; Cuzzocrea, A.; Azizi, V.; Lopez-Tarifa, P.; Renaud, N.; Umrigar, C. J.; Moroni, S.; Filippi, C. Cornell-Holland Ab-Initio Materials Package (CHAMP-EU) , 2024; https://github.com/filippi-claudia/champ (accessed 2024-7-31). 48

    2024

  17. [25]

    TREXIO: A file format and library for quantum chemistry,

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

    2023

  18. [26]

    Reducing the time-step errors in diffusion Monte Carlo,

    T. A. Anderson, M. C. Per, and C. J. Umrigar, “Reducing the time-step errors in diffusion Monte Carlo,” J. Chem. Phys., vol. 160, p. 104110, 03 2024

    2024

  19. [27]

    A.; Filippi, C.; Petruziello, F

    Anderson, T. A.; Filippi, C.; Petruziello, F. R.; Güclu A. D.; Toulouse J.; Um- rigar, C. J. Cornell-Holland Ab-Initio Materials Package (CHAMP-US) , 2024; https://github.com/QMC-Cornell/CHAMP (accessed 2024-10-22)

    2024

  20. [28]

    Diffusion monte carlo study of circular quantum dots,

    F. Pederiva, C. J. Umrigar, and E. Lipparini, “Diffusion monte carlo study of circular quantum dots,” Phys. Rev. B, vol. 62, pp. 8120–8125, Sep 15 2000

    2000

  21. [29]

    Investigation of excita- tion energies and hund’s rule in open shell quantum dots by diffusion monte carlo,

    L. Colletti, F. Pederiva, E. Lipparini, and C. J. Umrigar, “Investigation of excita- tion energies and hund’s rule in open shell quantum dots by diffusion monte carlo,” EUROPEAN PHYSICAL JOURNAL B, vol. 27, pp. 385–392, Jun 2002

    2002

  22. [30]

    Ghosal, C

    A. Ghosal, C. J. Umrigar, H. Jiang, D. Ullmo, and H. Baranger, “Interaction ef- fects in the mesoscopic regime: A quantum monte carlo study of irregular quantum dotsinteraction effects in the mesoscopic regime: A quantum monte carlo study of irregular quantum dots,” Phys. Rev. ...

    2005

  23. [31]

    Maximum-density droplet to lower-density droplet transition in quantum dots,

    A. Güclu and C. J. Umrigar, “Maximum-density droplet to lower-density droplet transition in quantum dots,” Phys. Rev. B, vol. 72, p. 045309, Jul 2005

    2005

  24. [32]

    Quantum monte carlo study of composite fermions in quantum dots: The effect of landau-level mixing,

    A. D. Güclu, G. S. Jeon, C. J. Umrigar, and J. K. Jain, “Quantum monte carlo study of composite fermions in quantum dots: The effect of landau-level mixing,” Phys. Rev. B, vol. 72, p. 205327, Nov 2005

    2005

  25. [33]

    Composite-fermion antipar- ticle description of the hole excitation in a maximum-density droplet with a small number of electrons,

    G. S. Jeon, A. D. Güclu, C. J. Umrigar, and J. K. Jain, “Composite-fermion antipar- ticle description of the hole excitation in a maximum-density droplet with a small number of electrons,” Phys. Rev. B, vol. 72, p. 245312, Dec 2005

    2005

  26. [34]

    Correlation- induced inhomogeneity in circular quantumdots,

    A. Ghosal, A. D. Güclu, C. J. Umrigar, D. Ullmo, and H. U. Baranger, “Correlation- induced inhomogeneity in circular quantumdots,” NATURE PHYSICS, vol. 2, pp. 336–340, May 2006

    2006

  27. [35]

    Interaction-induced strong localization in quantum dots,

    A. D. Güclu, A. Ghosal, C. J. Umrigar, and H. U. Baranger, “Interaction-induced strong localization in quantum dots,” Phys. Rev. B, vol. 77, p. 041301, Jan 2008

    2008

  28. [36]

    Path to wigner localization in circular quantum dots,

    L. Zeng, W. Geist, W. Y. Ruan, C. J. Umrigar, and M. Y. Chou, “Path to wigner localization in circular quantum dots,” Phys. Rev. B, vol. 79, p. 235334, Jun 2009

    2009

  29. [37]

    Localization in an inhomogeneous quantum wire,

    A. D. Güclu, C. J. Umrigar, H. Jiang, and H. U. Baranger, “Localization in an inhomogeneous quantum wire,” Phys. Rev. B, vol. 80, p. 201302, Nov 2009

    2009

  30. [38]

    Zigzag phase transi- tion in quantum wires,

    A. C. Mehta, C. J. Umrigar, J. S. Meyer, and H. U. Baranger, “Zigzag phase transi- tion in quantum wires,” Phys. Rev. Lett., vol. 110, p. 246802, Jun 11 2013. 49

    2013

  31. [39]

    Excitation energies from density functional perturbation theory,

    C. Filippi, C. J. Umrigar, and X. Gonze, “Excitation energies from density functional perturbation theory,” J. Chem. Phys., vol. 107, pp. 9994–10002, Dec 15 1997

    1997

  32. [40]

    Are unoccupied kohn-sham eigenval- ues related to excitation energies?,

    C. J. Umrigar, A. Savin, and X. Gonze, “Are unoccupied kohn-sham eigenval- ues related to excitation energies?,” in ELECTRONIC DENSITY FUNCTIONAL THEORY: RECENT PROGRESS AND NEW DIRECTIONS (J. Dobson, G. Vig- nale, and M. Das, eds.), pp. 167–176, 1998. International Workshop o...

    1998

  33. [41]

    Evidence of physical reality in the kohn- sham potential: The case of atomic ne,

    A. Al-Sharif, R. Resta, and C. J. Umrigar, “Evidence of physical reality in the kohn- sham potential: The case of atomic ne,” Phys. Rev. A, vol. 57, pp. 2466–2469, Apr 1998

    1998

  34. [42]

    Relationship of kohn-sham eigenvalues to excitation energies,

    A. Savin, C. J. Umrigar, and X. Gonze, “Relationship of kohn-sham eigenvalues to excitation energies,” CHEMICAL PHYSICS Lett., vol. 288, pp. 391–395, May 22 1998

    1998

  35. [43]

    Compact and flexible basis func- tions for quantum monte carlo calculations,

    F. R. Petruzielo, J. Toulouse, and C. J. Umrigar, “Compact and flexible basis func- tions for quantum monte carlo calculations,” J. Chem. Phys., vol. 132, p. 094109, Mar 7 2010

    2010

  36. [44]

    Basis set construction for molec- ular electronic structure theory: Natural orbital and gauss-slater basis for smooth pseudopotentials,

    F. R. Petruzielo, J. Toulouse, and C. J. Umrigar, “Basis set construction for molec- ular electronic structure theory: Natural orbital and gauss-slater basis for smooth pseudopotentials,” J. Chem. Phys., vol. 134, p. 064104, Feb 14 2011

    2011

  37. [45]

    Optimized trial wave functions for quan- tum monte carlo calculations,

    C. J. Umrigar, K. Wilson, and J. Wilkins, “Optimized trial wave functions for quan- tum monte carlo calculations,” Phys. Rev. Lett., vol. 60, pp. 1719–1722, Apr 25 1988

    1988

  38. [46]

    Energy and variance optimization of many-body wave functions,

    C. J. Umrigar and C. Filippi, “Energy and variance optimization of many-body wave functions,” Phys. Rev. Lett., vol. 94, p. 150201, Apr 22 2005

    2005

  39. [47]

    Optimization of quantum monte carlo wave functions by energy minimization,

    J. Toulouse and C. J. Umrigar, “Optimization of quantum monte carlo wave functions by energy minimization,” J. Chem. Phys., vol. 126, p. 084102, Feb 28 2007

    2007

  40. [48]

    Alleviation of the fermion-sign problem by optimization of many-body wave functions,

    C. J. Umrigar, J. Toulouse, C. Filippi, S. Sorella, and R. G. Hennig, “Alleviation of the fermion-sign problem by optimization of many-body wave functions,” Phys. Rev. Lett., vol. 98, p. 110201, Mar 16 2007

    2007

  41. [49]

    Full optimization of jastrow-slater wave functions with application to the first-row atoms and homonuclear diatomic molecules,

    J. Toulouse and C. J. Umrigar, “Full optimization of jastrow-slater wave functions with application to the first-row atoms and homonuclear diatomic molecules,” J. Chem. Phys., vol. 128, p. 174101, May 7 2008

    2008

  42. [50]

    Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo,

    C. Filippi, R. Assaraf, and S. Moroni, “Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo,” J. Chem. Phys., vol. 144, p. 194105, 2016

    2016

  43. [51]

    Optimizing the Energy with Quantum Monte Carlo: A Lower Numerical Scaling for Jastrow Slater Expansions,

    R. Assaraf, S. Moroni, and C. Filippi, “Optimizing the Energy with Quantum Monte Carlo: A Lower Numerical Scaling for Jastrow Slater Expansions,” J. Chem. Theory Comput., vol. 13, p. 5273, 2017. 50

    2017

  44. [52]

    Accelerated Metropolis Method,

    C. J. Umrigar, “Accelerated Metropolis Method,” Phys. Rev. Lett., vol. 71, pp. 408– 411, Jul 19 1993

    1993

  45. [53]

    Efficient protocol for quantum Monte Carlo calculations of hydrogen abstraction barriers: Application to methanol,

    E. T. Swann, M. L. Coote, A. S. Barnard, and M. C. Per, “Efficient protocol for quantum Monte Carlo calculations of hydrogen abstraction barriers: Application to methanol,” International Journal of Quantum Chemistry, vol. 117, no. 9, p. e25361, 2017

    2017

  46. [54]

    Green function Monte Carlo with stochastic reconfiguration,

    S. Sorella, “Green function Monte Carlo with stochastic reconfiguration,” Phys. Rev. Lett., vol. 80, no. 20, p. 4558, 1998

    1998

  47. [55]

    Diffusion Monte Carlo methods with a fixed number of walkers,

    R. Assaraf, M. Caffarel, and A. Khelif, “Diffusion Monte Carlo methods with a fixed number of walkers,” Phys. Rev. E, vol. 61, pp. 4566–4575, Apr 2000

    2000

  48. [56]

    Quantum Mecha (QMeCha) package β.1.3 (private repository),

    M. Barborini, “Quantum Mecha (QMeCha) package β.1.3 (private repository),” 2023

    2023

  49. [57]

    The ORCA Quantum Chem- istry Program Package,

    F. Neese, F. Wennmohs, U. Becker, and C. Riplinger, “The ORCA Quantum Chem- istry Program Package,” J. Chem. Phys., vol. 152, p. 224108, June 2020

    2020

  50. [58]

    Generalized Lanczos algorithm for variational quantum Monte Carlo,

    S. Sorella, “Generalized Lanczos algorithm for variational quantum Monte Carlo,” Phys. Rev. B, vol. 64, p. 024512, Jun 2001

    2001

  51. [59]

    TurboR VB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo,

    K. Nakano, C. Attaccalite, M. Barborini, L. Capriotti, M. Casula, E. Coccia, M. Dagrada, C. Genovese, Y. Luo, G. Mazzola, A. Zen, and S. Sorella, “TurboR VB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo,” The Journal of Chemical Physics, vol....

    2020

  52. [60]

    Geminal wave functions with Jastrow correlation: A first application to atoms,

    M. Casula and S. Sorella, “Geminal wave functions with Jastrow correlation: A first application to atoms,” J. Chem. Phys., vol. 119, no. 13, pp. 6500–6511, 2003

    2003

  53. [61]

    Nonlocal pseudopotentials and time-step errors in diffusion Monte Carlo,

    T. A. Anderson and C. J. Umrigar, “Nonlocal pseudopotentials and time-step errors in diffusion Monte Carlo,” J. Chem. Phys., vol. 154, p. 214110, 06 2021

    2021

  54. [62]

    QWalk: A quantum Monte Carlo program for electronic structure,

    L. K. Wagner, M. Bajdich, and L. Mitas, “QWalk: A quantum Monte Carlo program for electronic structure,” Journal of Computational Physics, vol. 228, no. 9, pp. 3390– 3404, 2009

    2009

  55. [63]

    Toward Accurate Hydro- gen Bonds by Scalable Quantum Monte Carlo,

    M. Dubecky, P. Jurecka, L. Mitas, M. Ditte, and R. Fanta, “Toward Accurate Hydro- gen Bonds by Scalable Quantum Monte Carlo,” J. Chem. Theor. Computat., vol. 15, no. 6, pp. 3552–3557, 2019

    2019

  56. [64]

    Recent developments in the general atomic and molecular electronic structure system,

    G. M. J. Barca, C. Bertoni, L. Carrington, D. Datta, N. De Silva, J. E. Deustua, D. G. Fedorov, J. R. Gour, A. O. Gunina, E. Guidez, T. Harville, S. Irle, J. Ivanic, K. Kowalski, S. S. Leang, H. Li, W. Li, J. J. Lutz, I. Magoulas, J. Mato, V. Mironov, H. Nakata, B. Q. Pham, P....

    2020

  57. [65]

    Correlated Monte Carlo wave functions for some cations and anions of the first row atoms,

    J. W. Moskowitz and K. E. Schmidt, “Correlated Monte Carlo wave functions for some cations and anions of the first row atoms,” J. Chem. Phys., vol. 97, pp. 3382– 3385, 09 1992

    1992

  58. [66]

    Electronic structure quantum Monte Carlo,

    M. Bajdich and L. Mitas, “Electronic structure quantum Monte Carlo,” Acta Phys. Slovaca, vol. 59, no. 2, pp. 81–162, 2009

    2009

  59. [67]

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

    M. Dubecky, R. Derian, P. Jurecka, L. Mitas, P. Hobza, and M. Otyepka, “Quantum Monte Carlo for noncovalent interactions: an efficient protocol attaining benchmark accuracy,” Phys. Chem. Chem. Phys., vol. 16, pp. 20915–20923, 2014

    2014

  60. [68]

    Quantum Monte Carlo Methods Describe Noncovalent Interactions with Subchemical Accu- racy,

    M. Dubecký, P. Jurečka, R. Derian, P. Hobza, M. Otyepka, and L. Mitas, “Quantum Monte Carlo Methods Describe Noncovalent Interactions with Subchemical Accu- racy,” Journal of Chemical Theory and Computation, vol. 9, no. 10, pp. 4287–4292, 2013

    2013

  61. [69]

    General Correlated Geminal Ansatz for Electronic Structure Calculations: Exploiting Pfaffians in Place of De- terminants,

    C. Genovese, T. Shirakawa, K. Nakano, and S. Sorella, “General Correlated Geminal Ansatz for Electronic Structure Calculations: Exploiting Pfaffians in Place of De- terminants,” Journal of Chemical Theory and Computation, vol. 16, pp. 6114–6131, oct 2020

    2020

  62. [70]

    Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods,

    S. Sorella, M. Casula, and D. Rocca, “Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods,” J. Chem. Phys., vol. 127, no. 1, p. 014105, 2007

    2007

  63. [71]

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

    K. Nakano, O. Kohulák, A. Raghav, M. Casula, and S. Sorella, “TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods,” The Journal of Chemical Physics, vol. 159, p. 224801, 12 2023

    2023

  64. [72]

    Numerical study of the two-dimensional Heisenberg model using a Green function Monte Carlo technique with a fixed number of walkers,

    M. Calandra Buonaura and S. Sorella, “Numerical study of the two-dimensional Heisenberg model using a Green function Monte Carlo technique with a fixed number of walkers,” Phys. Rev. B, vol. 57, pp. 11446–11456, May 1998

    1998

  65. [73]

    Becca and S

    F. Becca and S. Sorella, Quantum Monte Carlo approaches for correlated systems. Cambridge University Press, 2017. 52

    2017

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.