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Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.

Tobias Morawietz1, Nongnuch Artrith2

  • 1Bayer AG, Pharmaceuticals, R&D, Digital Technologies, Computational Molecular Design, 42096, Wuppertal, Germany.

Journal of Computer-Aided Molecular Design
|October 9, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) accelerates atomistic simulations using quantum mechanics (QM) for drug discovery and materials science. This enhances accuracy, reduces costs, and expands accessible scales for industrial R&D.

Keywords:
Drug discoveryEnergy materialsIndustrial applicationsMachine learningNeural networksQuantum mechanics

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Drug Discovery

Background:

  • Atomistic simulations are crucial for industrial applications like drug design and materials development.
  • Quantum mechanical (QM) simulations offer high accuracy but are computationally expensive.
  • Extending simulation scales is vital for addressing complex industrial challenges.

Purpose of the Study:

  • To review advances in machine learning (ML) for accelerating QM-based atomistic simulations.
  • To demonstrate how ML enhances the applicability, accuracy, and efficiency of simulations.
  • To highlight the impact of ML-accelerated simulations on industrial R&D.

Main Methods:

  • Integration of machine learning algorithms with quantum mechanical frameworks.
  • Development of ML models to predict system properties at reduced computational cost.
  • Application of ML-accelerated simulations to drug discovery and energy materials.

Main Results:

  • ML methods significantly extend the reach of QM simulations to larger length and time scales.
  • Enhanced accuracy and reduced computational expense for industrially relevant properties.
  • Demonstrated benefits in optimizing protein-ligand interactions and designing new energy materials.

Conclusions:

  • ML-accelerated atomistic simulations are transforming pharmaceutical, chemical, and materials industries.
  • These methods offer unprecedented capabilities for industrial research and development.
  • Future opportunities lie in further integrating ML for advanced molecular and materials modeling.