Citing Articles For: High-pressure hydrogen by machine learning and quantum Monte Carlo

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No title available

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Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials

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Liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential

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Deep variational free energy prediction of dense hydrogen solid at 1200 K

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Basis Set Incompleteness Errors in Fixed-Node Diffusion Monte Carlo Calculations on Noncovalent Interactions

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Self-Consistency Error Correction for Accurate Machine Learning Potentials from Variational Monte Carlo

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The Journal of Chemical Physics 161 234303 (2024)

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Principal deuterium Hugoniot via quantum Monte Carlo and Δ -learning

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Phys. Rev. B 110 L041107 (2024)

Efficient calculation of unbiased atomic forces in ab initio variational Monte Carlo

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Phys. Rev. B 109 205151 (2024)

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npj Comput Mater 10 56 (2024)

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Articles Citing

High-pressure hydrogen by machine learning and quantum Monte Carlo

Fast evaluation of unbiased atomic forces in ab initio variational Monte Carlo via the Lagrangian technique

Denser hydrogen inferred from first-principles simulations challenges Jupiter’s interior models

Neural wave functions for high-pressure atomic hydrogen

Quantum melting behavior of dense solid hydrogen from large-scale machine-learned potential simulations

Nonadiabatic Excited-State Dynamics with Quantum Monte Carlo-Trained Machine Learning: Azomethane as a Stringent Test

Nuclear Gradients from Auxiliary-Field Quantum Monte Carlo and Their Applications in ML-Driven Geometry Optimization and Transition State Search

H2 Vibrations in a Spherical Potential Well

Approaches for strong electron correlation beyond DFT: Conventional and neural network quantum Monte Carlo

Geometry‐Based Neural‐Network Prediction of Electron Localization Function Topology in Dense Hydrogen

No title available

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

Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials

Liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential

Hydrogen liquid-liquid transition from first principles and machine learning

Deep variational free energy prediction of dense hydrogen solid at 1200 K

Basis Set Incompleteness Errors in Fixed-Node Diffusion Monte Carlo Calculations on Noncovalent Interactions

Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems

Self-Consistency Error Correction for Accurate Machine Learning Potentials from Variational Monte Carlo

Nonmetal-to-metal transition in liquid hydrogen using density functional theory and the Heyd–Scuseria–Ernzerhof exchange-correlation functional

Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials

Toward first principles-based simulations of dense hydrogen

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

Principal deuterium Hugoniot via quantum Monte Carlo and Δ -learning

Efficient calculation of unbiased atomic forces in ab initio variational Monte Carlo

Quantum symmetrization transition in superconducting sulfur hydride from quantum Monte Carlo and path integral molecular dynamics

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