ArticleNature communications2025
PET-MAD as a lightweight universal interatomic potential for advanced materials modeling.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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Who cites it
28 citing papers in PubMed.
- 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
- Accessible hybrid DFT-quality NMR crystallographyChemical science · 2026Article
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- A unified machine-learning framework for ab initio multiscale modeling of liquids.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches.ACS physical chemistry Au · 2026Article
- Bias in Universal Machine-Learned Interatomic Potentials and Its Effects on Fine-Tuning.Journal of chemical theory and computation · 2026Article
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.JACS Au · 2026Review
- Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band.MethodsX · 2026Article
- Article
- Atomic-Scale Molecular Dynamics Modeling of Iron Oxides: Surface Properties and Methodologies.Molecules (Basel, Switzerland) · 2026Review
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- KIMMDY: a biomolecular reaction emulator.Nature communications · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- Giant photorefractive and photoexpansion effects in a van der Waals semiconductor.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- MC3D: the Materials Cloud computational database of experimentally known stoichiometric inorganics.Digital discovery · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Molecular Surface Engineering of Sulfide Electrolytes with Enhanced Humidity Tolerance for Robust Lithium Metal All-Solid-State Batteries.Advanced materials (Deerfield Beach, Fla.) · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Machine-learning interatomic potentials have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent universal models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable interatomic potential trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but thoroughly consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with the state-of-the-art machine-learned interatomic potentials for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.
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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.