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Articles Citing

High-pressure hydrogen by machine learning and quantum Monte Carlo

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations

Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark

Training models using forces computed by stochastic electronic structure methods

Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules

No title available

No title available

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

ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training

Deep Variational Free Energy Approach to Dense Hydrogen

Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo

Thermal dependence of the hydrated proton and optimal proton transfer in the protonated water hexamer

Thermodynamic and transport properties of plasmas: Low‐density benchmarks

No title available

A structural optimization algorithm with stochastic forces and stresses

Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials

Extending the reach of quantum computing for materials science with machine learning potentials

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

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