Accelerating quantum chemistry with machine learning (ML) and artificial Intelligence (AI)
Olexandr Isayev
(Carnegie Mellon University, Pittsburgh, USA)
Deep learning is revolutionizing many areas of science and technology, particularly in natural language processing, speech recognition and computer vision. In this talk, we will provide an overview into latest developments of machine learning and AI methods and application to the problem of quantum chemistry 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.
First we will present a deep learning model that approximate solution of Schrodinger equation. Focusing on parametrization for drug-like organic molecules and proteins, we have developed a single ‘universal’ model which is highly accurate compared to reference quantum mechanical calculations at speeds 10^6 faster. Second, we propose an improved machine learning framework for simulating molecules in arbitrary spin and charge states.