ArticleJournal of chemical theory and computation2023
Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.Journal of chemical theory and computation · 2025Article
- Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050.ACS physical chemistry Au · 2024Review
- Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model's applicability and limitations, we developed FFAST (force field analysis software and tools): a cross-platform software package designed to gain detailed insights into a model's performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of any molecular force field,─such as popular state-of-the-art MLFF models, ─ on various popular data set types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large data set. In this paper, the example of the MACE and NequIP models is used on two data sets of interest [stachyose and docosahexaenoic acid (DHA)]─to illustrate the use cases of the software. With this, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to the study of dynamics of molecules and materials.
Identifiers
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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.