ArticleJournal of chemical theory and computation2024
MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- 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
- The Newton-X platform for mixed quantum-classical dynamics.Physical chemistry chemical physics : PCCP · 2026Article
- MAPLE: a machine-learning force-field-native platform for automated reaction modeling and enzyme design.Chemical science · 2026Article
- Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- ASH: A Multi-Scale, Multi-Theory Modeling Program.Journal of computational chemistry · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- Ultrafast Correlation Energy Estimator.The journal of physical chemistry. A · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Large language model guided automated reaction pathway exploration.Communications chemistry · 2025Article
- Benchmark of Approximate Quantum Chemical and Machine Learning Potentials for Biochemical Proton Transfer Reactions.Journal of chemical theory and computation · 2025Article
- DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.Journal of chemical theory and computation · 2025Article
- DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.Journal of chemical information and modeling · 2025Article
- Legion: A Platform for Gaussian Wavepacket Nonadiabatic Dynamics.Journal of chemical theory and computation · 2025Article
- An efficient and flexible approach for local distortion: distortion distribution analysis enabled by fragmentation.Chemical science · 2025Article
- Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.npj computational materials · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- LASP to the Future of Atomic Simulation: Intelligence and Automation.Precision chemistry · 2024Review
- The Best DFT Functional Is the Ensemble of Functionals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
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.