ReviewNature reviews. Chemistry2026
Foundation models for atomistic simulation of chemistry and materials.
Review in Nature reviews. Chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed.
- Automating Model Building for SPM Images of Biomolecules Using MISO.Journal of chemical information and modeling · 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
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.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
- Article
- Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.JACS Au · 2026Review
- Benchmarking Foundation Potentials against Quantum Chemistry Methods for Predicting Molecular Redox Potentials.Precision chemistry · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- Artificial intelligence-assisted smart hydrogel bioinks in 3D bioprinting: design, optimization, and construct validation for functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.Journal of chemical theory and computation · 2025Article
- Machine learning potentials accurately reproduce vibrational dynamics in complex environments.The Journal of chemical physics · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models - pre-trained on massive datasets using transformer architectures - have revolutionized many fields. It is thus interesting to ask whether a foundation model - subject to suitable data, parameter scaling and training - could enable learned simulations of chemistry and materials. Here, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.
Identifiers
41673335What OpenQuestion holds
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.