Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
- PDF / 4,651,656 Bytes
- 30 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 224 Views
Machine learning‑accelerated quantum mechanics‑based atomistic simulations for industrial applications Tobias Morawietz1 · Nongnuch Artrith2 Received: 14 July 2020 / Accepted: 26 September 2020 © The Author(s) 2020
Abstract Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of proteinligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future. Keywords Quantum mechanics · Machine learning · Neural networks · Drug discovery · Energy materials · Industrial applications
Introduction Computational methods play an increasingly important role in R&D processes across the pharmaceutical, chemical, and materials industries. Computer-aided drug design [1–3] has the potential to lower the cost, decrease the failure rates, and speed up the discovery process. Computational materials methods help to identify novel materials [4, 5], for example, for renewable energy applications [6] such as catalytic energy conversion [7] and energy storage [8]. Results from atomistic simulations aid in the interpretation of experimental measurements and give insights into the structure, * Nongnuch Artrith [email protected] Tobias Morawietz [email protected] 1
Bayer AG, Pharmaceuticals, R&D, Digital Technologies, Computational Molecular Design, 42096 Wuppertal, Germany
Department of Chemical Engineering, Columbia University, New York, NY 10027, USA
2
dynamics and mechanisms of processes occurring on the atomic scale. In the last decades a new class of atomistic simulation techniques has emerged that combines machine learning (ML) with simulation methods based on quantum mechanical (QM) calculations. Such ML-based acceleration can dramatically increase the computational efficiency of QM-based simulations and enable to reach the large system sizes and long timescales required to access properties with relevance for industry. Here, we review a selection of ML-accelerated QM methods and their applications to drug design
Data Loading...
