ArticleJournal of computer-aided molecular design2021
Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.Current drug discovery technologies · 2026Review
- Accurate Deep Potential Model of Temperature-Dependent Elastic Constants for Phosphorus-Doped Silicon.Nanomaterials (Basel, Switzerland) · 2025Article
- Unlocking the Potential of Machine Learning in Enhancing Quantum Chemical Calculations for Infrared Spectral Prediction.ACS omega · 2025Article
- The Role of Bond Functions in Describing Intermolecular Electron Correlation for Van der Waals Dimers: A Study of (CHInternational journal of molecular sciences · 2024Article
- Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques.Frontiers in artificial intelligence · 2024Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Structure-basedComputational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.