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MLatom: A program package for quantum chemical research assisted by machine learning.

¹Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany.

Journal of Computational Chemistry

|June 21, 2019

Summary

This summary is machine-generated.

MLatom is a new software package for efficient atomistic simulations using machine learning (ML) algorithms. It simplifies complex ML tasks for users without requiring extensive programming knowledge.

Keywords:

Fortran Python kernel ridge regression machine learning molecular descriptor quantum chemistry sampling

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Atomistic simulations are crucial for understanding materials and chemical systems.
Implementing machine learning (ML) in these simulations can significantly enhance computational efficiency.
Existing tools may require extensive expertise in ML and programming.

Purpose of the Study:

To introduce MLatom, a user-friendly program package for efficient atomistic simulations.
To enable researchers to leverage ML algorithms without deep technical knowledge.
To provide a versatile tool for various simulation needs.

Main Methods:

MLatom implements kernel ridge regression with support for Gaussian, Laplacian, and Matérn kernels.
It accepts user-defined input vectors and converts molecular geometries into descriptors.
The package supports saving and reusing trained ML models and estimating generalization error.

Main Results:

MLatom offers a stand-alone program with an intuitive online manual.
It simplifies the application of ML algorithms for atomistic simulations.
The software supports gradient calculations and various sampling procedures.

Conclusions:

MLatom provides an accessible and efficient platform for machine learning-driven atomistic simulations.
It lowers the barrier to entry for utilizing advanced computational techniques.
The package is optimized for performance using Fortran and parallel computations.