ReviewTopics in current chemistry (Cham)2021
MLatom 2: An Integrative Platform for Atomistic Machine Learning.
Review in Topics in current chemistry (Cham), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin-Orbit Couplings and Dynamics.The journal of physical chemistry. A · 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
- LASP to the Future of Atomic Simulation: Intelligence and Automation.Precision chemistry · 2024Review
- Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.Nature communications · 2024Article
- MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.Journal of chemical theory and computation · 2024Article
- Next-Generation Vitrimers Design through Theoretical Understanding and Computational Simulations.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.International journal of molecular sciences · 2024Article
- The OpenMolcasJournal of chemical theory and computation · 2023Article
- Universal machine learning for the response of atomistic systems to external fields.Nature communications · 2023Article
- Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results.Journal of chemical theory and computation · 2023Article
- DeePMD-kit v2: A software package for deep potential models.The Journal of chemical physics · 2023Article
- Interpretable Machine Learning of Two-Photon Absorption.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets.Scientific data · 2023Article
- Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles.Journal of chemical theory and computation · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.
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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.