MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

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 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 pre-trained 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.

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Cited by in corpus (14)

• DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
• MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
• Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
• FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
• Physics-informed active learning for accelerating quantum chemical simulations
• A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
• Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
• Interoperable Workflows by Exchanging Grid-Based Data between Quantum-Chemical Program Packages
• All-in-one foundational models learning across quantum chemical levels
• Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure
• Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
• Systematically improved potential energy surfaces via sinNN models and sparse grid sampling
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• Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces