ArticleJournal of chemical theory and computation2021
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Unprecedented robustness of physics-informed atomic energy models at and beyond room temperature.Communications chemistry · 2026Article
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Platonic representation of foundation machine learning interatomic potentials.Nature machine intelligence · 2026Article
- Quantum mechanics in drug design: Progress, challenges, and future frontiers.Communicative & integrative biology · 2026Review
- Engineering rhodium encapsulated indium doped fullerene for NHScientific reports · 2025Article
- Convergence of Body-Orders in Linear Atomic Cluster Expansions.The journal of physical chemistry. A · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.Journal of chemical information and modeling · 2025Article
- Targeted Transferable Machine-Learned Potential for Linear Alkanes Trained on CJournal of chemical theory and computation · 2025Article
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Modelling ligand exchange in metal complexes with machine learning potentials.Faraday discussions · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- The design space of E(3)-equivariant atom-centred interatomic potentials.Nature machine intelligence · 2025Article
- A machine-learning framework for accelerating spin-lattice relaxation simulations.npj computational materials · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule.
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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.