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 →
MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide …
Training and using machine learning potentials with MLatom@XACS 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 →
Our editorial on the Special Topic “Modern semiempirical methods” was published in JCP. We overview contributions …
Editorial on the Special Topic “Modern semiempirical methods” published in JCP Read more →
Location: Xiamen University, China Duration: 2 years (with possible 1-year extension) Xiamen University is offering a …
Postdoc in Machine Learning-Based Simulations of Condensed Matter Read more →
The new MLatom 3 release comes with the versatile Python API. We are happy to announce the release …
The 2nd edition of the International Symposium on Machine Learning in Quantum Chemistry, SMLQC 2023, is …
SMLQC 2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry Read more →