We are very happy to announce that MLatom joins Xiamen Atomistic Computing Suite (XACS) which allows us to provide much better service to the theoretical and computational chemistry community. We marked the inception of XACS by holding a local workshop …
In our work published in the Journal of Physical Chemistry Letters, we investigate the performance of the general-purpose data-driven methods ANI-1ccx and AIQM1 in the calculation of enthalpies of formation. Extensive benchmark tests show that these two methods can achieve …
Our AIQM1 paper is one of the 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021! #NCOMTop25 The full list: https://www.nature.com/collections/gagdjjgcgj AIQM1 paper: https://www.nature.com/articles/s41467-021-27340-2 How to use AIQM1 method with MLatom: http://MLatom.com/AIQM1
In the work published in New Journal of Physics, we combine machine learning (ML) with the numerically exact hierarchical equations of motion (HEOM) approach, propagating quantum dynamics of a two-state system (spin-boson model) with only ca. 10% of the HEOM …
Speeding up quantum dissipative dynamics of open systems with kernel methods Read more »
We have developed artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), which can be used out of the box for very fast quantum chemical calculations with the accuracy of the gold-standard coupled-cluster method. Read more »
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …
We are happy to introduce MLatom 2: a major release of our integrative platform for user-friendly atomistic machine learning. It includes many more features and is further optimized for efficiency. Detailed overview of MLatom 2 is given in our contribution …
MLatom 2: Introducing a Platform for Atomistic Machine Learning Read more »
Paper 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.Preprint on ChemRxiv, DOI: 10.26434/chemrxiv.12594191. Short overview of the method in a form of LiveSlides: In brief ML-NEA can boost the calculation speed and increase …
My book chapter shows in a tutorial way how to use machine learning to assist quantum chemistry research.
We introduced hierarchical machine learning (hML) approach for building highly accurate potential energy surfaces from multiple Δ-ML models, each trained on semi-automatically defined training points.