JPCL | Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training
A machine learning potential with low error in the potential energies does not guarantee good performance …
A machine learning potential with low error in the potential energies does not guarantee good performance …
MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more →
We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release …
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open …
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We …
The capabilities of MLatom 3 are described in the paper published in the J. Chem. Theory …
MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial Read more →
The new MLatom 3 release comes with the versatile Python API. We are happy to announce the release …
We are happy to announce that on the occasion of its ten-year anniversary, we have released …
MLatom 3: 10-year anniversary edition is released! Read more →
Molecular dynamics simulations are widely used to study molecules and materials and lots of effort is …
Energy-conserving molecular dynamics is not energy conserving! Read more →
We have introduced a concept of 4D-spacetime atomistic AI models that learn how the molecule changes …