Abstract
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand 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.
Highlights
Computational methods play an increasingly important role in R&D processes across the pharmaceutical, chemical, and materials industries
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
In this review we focus on ML approaches of strategy (1) and (2), i.e. machine-learning potentials (MLP) for accelerated simulations and ML models that predict the outcome of QM calculations, since those are the most mature and offer a reasonable balance of usability and pay-off for industrial applications
Summary
Computational methods play an increasingly important role in R&D processes across the pharmaceutical, chemical, and materials industries. 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. We briefly summarize the two main conventional approaches for atomistic simulations, based on molecular mechanics (MM) and QM, respectively, and we show how ML can help overcome their limitations This is followed by a discussion of recent methodological advances in ML-based interatomic potentials (force fields) for the modeling of complex molecular and materials systems.
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