19 citations · 21 across the 7 of their papers we have counts for
9 papers
Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%
Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin +1
General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of million structures, assuming transferability requires such scale. We test…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density
Dahvyd Wing, Mihail Bogojeski, Szabolcs Goger +2
Machine-learned force fields (MLFFs) contain many learnable parameters and therefore require large training datasets. This poses a challenge for developing highly accurate, general…
Quantum-Accurate Vibrational and Conformational Dynamics from Small Molecules to Proteins with Machine-Learned Force Fields
Sergio Suárez-Dou, Miguel Gallegos, Kyunghoon Han +3
Biomolecular thermodynamics and spectroscopy depend on relative conformer energies, local curvatures, and collective dipole fluctuations on the potential-energy surface. Convention…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
A Transferable Model of Molecular Exchange-Repulsion Interaction from Anisotropic Valence Density Overlap
Dahvyd Wing, Alexandre Tkatchenko
Pauli exchange-repulsion is the dominant short-range intermolecular interaction and it is an essential component of molecular force fields. Current approaches to modeling Pauli rep…
QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
Adil Kabylda, Sergio Suárez-Dou, Nils Davoine +2
Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computationa…
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko +2
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable referenc…
Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
Emiel Slootman, Igor Poltavsky, Ravindra Shinde +4
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 f…