Bridges-2 Webinar: AIMNet2: Foundation Neural Network Potential for Molecules and Reactions

AIMNet2: Foundation Neural Network Potential for Molecules and Reactions

Olexandr Isayev, Carnegie Mellon University

Join us for this webinar describing machine-learning and AI methods used for drug discovery and molecular design.
In this talk, we will provide an overview into the latest developments of machine learning and AI methods and application to the problem of drug discovery and molecular design at Isayev’s Lab at CMU. We identify several areas where existing methods have the potential to accelerate computational chemistry research and disrupt more traditional approaches. In this work, we present the 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable model for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 20 million hybrid quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with “exotic” element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization.

Date: September 13, 2024