MLatom 1.0
MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.
Machine Learning in Chemistry
Nonadiabatic Dynamics with Deep Learning
We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics …
Machine Learning Accelerates Excited-State Dynamics
Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.
Self-Correcting Machine Learning and Structure-Based Sampling
Structure-based sampling and self-correcting machine learning is used for precise representation of molecular potential energy surfaces …
Self-Correcting Machine Learning and Structure-Based Sampling Read more →
Highlight about Δ-ML Approach Most Viewed in 2015
A highlight by Jan Jensen about the Δ-ML approach proposed by us [1] was the most …
Highlight about Δ-ML Approach Most Viewed in 2015 Read more →
Correcting Differences with Machine Learning
In our recent study, we propose using machine learning (ML) to correct differences in properties calculated …
Machine Learning of Semiempirical Parameters
We propose using machine learning (ML) for improving semiempirical Hamiltonian. Given sufficiently large training set ML …
Data Set with 134 Kilo Molecules
If you need really huge data set to test your methods, then our data set with …