Seminar Series - Development of Multimodal Machine Learning Atomistic Potentials - Professor Olexandr Isayev, Chemistry, Carnegie Mellon University

Tuesday, June 22 2021 2pm via Zoom

Speaker: Professor Olexandr Isayev, Department of Chemistry, Carnigie Mellon University

Abstract: Machine learning interatomic potentials (MLIPs) are widely used for describing molecular energy and continue bridging the speed and accuracy gap between quantum mechanical (QM) and classical approaches like force fields. In this Account, we focus on the out-of-the-box approaches to developing transferable MLIPs for diverse chemical tasks. First, we introduce the “Accurate Neural Network engine for Molecular Energies,” ANAKIN-ME, method (or ANI for short). The ANI model utilizes Justin Smith Symmetry Functions (JSSFs) and realizes training for vast data sets. The training data set of several orders of magnitude larger than before has become the key factor of the knowledge transferability and flexibility of MLIPs. As the quantity, quality, and types of interactions included in the training data set will dictate the accuracy of MLIPs, the task of proper data selection and model training could be assisted with advanced methods like active learning (AL), transfer learning (TL), and multitask learning (MTL). Next, we describe the AIMNet “Atoms-in-Molecules Network” that was inspired by the quantum theory of atoms in molecules. The AIMNet architecture lifts multiple limitations in MLIPs. It encodes long-range interactions and learnable representations of chemical elements. We also discuss the AIMNet-NSE (Neural Spin Equilibration) model that expands the applicability domain of AIMNet from neutral molecules toward open-shell systems. The AIMNet- NSE encompasses a dependence of the potential on molecular charge and spin. It brings ML and physical models one step closer, ensuring the correct molecular energy behavior over the total molecular charge and spin state