ArticleJournal of the American Chemical Society2025
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
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Who cites it
140 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- On-the-Fly Machine-Learned Force Fields for High-Fidelity Polymer Glass Transition Simulations.Journal of chemical information and modeling · 2026Article
- Improving Reliability of Machine Learning Interatomic Potentials with Physics-Informed Pretraining.Journal of chemical information and modeling · 2026Article
- Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD.Science advances · 2026Article
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Accessible hybrid DFT-quality NMR crystallographyChemical science · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Article
- Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis.Journal of chemical theory and computation · 2026Article
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- Tripeptide dynamics from empirical and machine-learned energy functions.Biophysical journal · 2026Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Hierarchical Dynamics in Polymer NanocompositesInsights from Neutron and X‑ray Scattering.Macromolecules · 2026Review
- Dynamics of (Hetero)aryl Motifs: An Integrative Approach To Study the Conformational Landscape in Macrocycles.Journal of the American Chemical Society · 2026Article
80 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.