ArticleNature machine intelligence2025
The design space of E(3)-equivariant atom-centred interatomic potentials.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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Who cites it
111 citing papers in PubMed.
- Modeling Phase Separation in Polymer-Derived Silicon Carbonitride Ceramics through Extended Machine Learning Molecular Dynamics.ACS nano · 2026Article
- GMUPC: a new package for global-minimum search of large uranyl peroxide clusters with machine learning potentials and DFT calculations.Journal of molecular modeling · 2026Article
- CoTAR: Topology and Atomic State Reconstruction in Condensed Phases.Journal of chemical information and modeling · 2026Article
- Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Conditional generative models enable targeted exploration of MAX phase design space.Journal of materials chemistry. C · 2026Article
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.Journal of chemical theory and computation · 2026Article
- Reversible Hydration Tuning of Polar Order and Large Nonlinear Optical Response in a 2D van der Waals Crystal, LiInPAdvanced materials (Deerfield Beach, Fla.) · 2026Article
- Experimental Prototyping and Atomistic Modeling of Graphene Quantum Dot-Sensitized Solar Cells.Materials (Basel, Switzerland) · 2026Article
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- Quantitative Prediction of Exchangeable Proton Chemical Shifts.Nature communications · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.Journal of chemical theory and computation · 2026Article
- Enhancing molecular property prediction of transformer models with dual graph representation.Nature communications · 2026Article
- Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells.ACS omega · 2026Article
- A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids.Journal of chemical theory and computation · 2026Article
- Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Steering Langevin Dynamics toward Transition States Using Collective-Variable-Free Resampling.Journal of chemical theory and computation · 2026Article
51 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.
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