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20162025
most citedPushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals

2 citations · 2 across the 3 of their papers we have counts for

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physics.chem-ph2025★ 2 cited

Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals

Anouar Benali, Thomas Plé, Olivier Adjoua +18

We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learnin…

physics.chem-ph2020

Towards a Systematic Improvement of the Fixed-Node Approximation in Diffusion Monte Carlo for Solids -- A Case Study In Diamond

Anouar Benali, Kevin Gasperich, Kenneth D. Jordan +9

While Diffusion Monte Carlo (DMC) is in principle an exact stochastic method for \textit{ab initio} electronic structure calculations, in practice the fermionic sign problem necess…

physics.chem-ph2018

Spin-adapted selected configuration interaction in a determinant basis

Vijay Gopal Chilkuri, Thomas Applencourt, Kevin Gasperich +2

Selected configuration interaction (SCI) methods, when complemented with a second-order perturbative correction, provide near full configuration interaction (FCI) quality energies…

physics.chem-ph2016

Using CIPSI nodes in diffusion Monte Carlo

Michel Caffarel, Thomas Applencourt, Emmanuel Giner +1

Several aspects of the recently proposed DMC-CIPSI approach consisting in using selected Configuration Interaction (SCI) approaches such as CIPSI (Configuration Interaction using a…

physics.chem-ph2016

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

Michel Caffarel, Thomas Applencourt, Emmanuel Giner +1

All-electron Fixed-node Diffusion Monte Carlo (FN-DMC) calculations for the nonrelativistic ground-state energy of the water molecule at equilibrium geometry are presented. The det…