ArticleJournal of chemical theory and computation2024
Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Use of π-Space CASSCF Trial Wave Functions in DMC Calculations of Unsaturated Organic Molecules.The journal of physical chemistry. A · 2026Article
- Nuclear Gradients from Auxiliary-Field Quantum Monte Carlo and Their Applications in ML-Driven Geometry Optimization and Transition State Search.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions.Nature communications · 2025Article
- Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br.Scientific data · 2025Article
- Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.Journal of the American Chemical Society · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multideterminant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in diffusion Monte Carlo. The excellent performance of our protocols is assessed against high-level coupled cluster calculations on a diverse set of representative configurations of the system. Finally, we train machine-learning force fields on the QMC forces and compare them to models trained on coupled cluster reference data, showing that a force field based on the diffusion Monte Carlo forces with a single determinant can faithfully reproduce coupled cluster power spectra in molecular dynamics simulations.
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Registered trials
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