MLatom

A Package for Atomistic Simulations with Machine Learning

Developed for practical and efficient application of machine learning in computational chemistry.

Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way

MLatom 2: Introducing a Platform for Atomistic Machine Learning

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

MLatom 1.2: ML absorption spectra

Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310.

Interfaces to popular machine learning potentials (sGDML, GAP-SOAP, ANI, PhysNet, DPMD)
Converting XYZ coordinates to molecular descriptors (RE descriptor, Coulomb matrix)
Sampling (structure-based, farthest-point, random, user-defined)
Δ-ML
Kernel ridge regression (Gaussian, Laplacian, exponential, Matérn kernels; optimization of hyperparameters)
Creating and re-using ML models
Estimating accuracy of ML models (cross-validation etc.)
First derivatives
ML-nuclear ensemble approach for absorption spectrum calculation
learning curves
hyperparameter optimization