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 …
Category: Machine Learning in Chemistry
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 …
In our Review “Molecular excited states through a machine learning lens” in Nature Reviews Chemistry, we provide insights and highlight challenges of the rapidly growing field of machine learning for excited-states simulation and analysis.
Download the poster by Bao-Xin Xue about Machine Learning for Absorption Cross Sections:
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.
My perspective on the state-of-the-art of machine learning in quantum chemistry and outlook for future developments was published in J. Phys. Chem. Lett.
The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.
MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.