IN-PERSON EVENT - REGISTER HERE to attend

By Prof. Gábor Csányi - Professor of Molecular Modelling, University of Cambridge 

Abstract: A new computational task has been defined and solved over the past 15 years for extended material systems: the analytic fitting of the Born-Oppenheimer potential energy surface as a function of nuclear coordinates. The resulting potentials ("force fields") are reactive, many-body, with evaluation costs that are currently on the order of 0.1-10 ms/atom/cpu core (or about 1-10ms on a powerful GPU), and reach accuracies of a few meV/atom when trained specifically for a given system using iterative or active learning methods. The latest and most successful architectures leverage many-body symmetric descriptions of local geometry and equivariant message passing networks. Perhaps the most surprising recent result is the stability of models trained on very diverse training sets across the whole periodic table. Our recent discovery is that the MACE-MP-0 model that was trained on just ~150,000 real and hypothetical small inorganic crystals (90% of training set < 70 atoms), is capable of stable molecular dynamics at ambient conditions on any system tested so far - this includes crystals, liquids, surfaces, clusters, molecules, and combinations of all of these. The astounding generalisation performance of such foundation models open the possibility to creating a universally applicable interatomic potential with useful accuracy for materials (especially when fine-tuned with a little bit of domain-specific data), and democratise quantum-accurate large scale molecular simulations by lowering the barrier to entry into the field. Similarly, in the domain of organic chemistry, training just on small molecules and small clusters allows accurate simulation of condensed phase systems, and first principles prediction of quantities such as hydration free energies for the first time.  

Biography: His expertise lies in atomistic simulation, particularly in multiscale modeling that bridges quantum mechanics with larger length scales. He is currently focused on applying machine learning and other data-intensive techniques to materials modeling challenges, such as deriving force fields (interatomic potentials) from ab initio electronic structure data. Additionally, he has a strong interest in statistical problems within molecular dynamics, such as enhanced sampling algorithms that facilitate the exploration of the global configuration space of materials and molecules.

Hosted by CSIC Prof. Pablo Ordejón, Director of ICN2 and Theory and Simulation Group Leader at ICN2