ArticleScientific data2025
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches.ACS physical chemistry Au · 2026Article
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.Journal of chemical theory and computation · 2026Article
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- A universal machine learning model for the electronic density of states.Digital discovery · 2026Article
- MC3D: the Materials Cloud computational database of experimentally known stoichiometric inorganics.Digital discovery · 2026Article
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 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
- Enhanced climbing image nudged elastic band method with Hessian eigenmode alignment.Frontiers in chemistry · 2026Article
- Massive Atomic Diversity: a compact universal dataset for atomistic machine learning.Scientific data · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of machine-learning models for atomic-scale simulations has greatly benefited from the large databases of materials and molecular properties, computed using electronic-structure calculations. Recently, these databases enabled the training of "universal" models that aim to make accurate predictions for arbitrary atomic geometries and compositions. However, many of these databases were originally designed for materials discovery, focusing primarily on equilibrium structures. Here, we introduce a dataset designed to train machine-learning models to make reasonable predictions for arbitrary structures. Starting with relatively small sets of stable structures, we built the dataset aiming to achieve "massive atomic diversity" (MAD) by aggressively modifying these structures and utilizing highly consistent electronic-structure settings for property calculations. Despite containing fewer than 100,000 entries, the MAD dataset has already enabled the training of universal interatomic potentials that rival those trained on datasets containing two to three orders of magnitude more data. We detail the design philosophy of the dataset and introduce low-dimensional structural latent space descriptors that can be used as a general-purpose materials cartography tool.
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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.