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<p>As quantum mechanics marks its centennial in 2025, machine learning interatomic potentials have emerged as transformative tools in molecular modeling, bridging quantum mechanical accuracy with classical efficiency. Here we examine their development through four defining challenges—achieving chemical accuracy, maintaining computational efficiency, ensuring interpretability and reaching universal generalizability. We highlight architectural innovations, physics-informed approaches, and foundation models trained on extensive data. Together, these developments chart a path toward predictive, transferable and physically grounded machine learning frameworks for next-generation computational chemistry.</p>

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Machine learning interatomic potentials at the centennial crossroads of quantum mechanics

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Bhupalee Kalita,
  • Hatice Gokcan,
  • Olexandr Isayev

摘要

As quantum mechanics marks its centennial in 2025, machine learning interatomic potentials have emerged as transformative tools in molecular modeling, bridging quantum mechanical accuracy with classical efficiency. Here we examine their development through four defining challenges—achieving chemical accuracy, maintaining computational efficiency, ensuring interpretability and reaching universal generalizability. We highlight architectural innovations, physics-informed approaches, and foundation models trained on extensive data. Together, these developments chart a path toward predictive, transferable and physically grounded machine learning frameworks for next-generation computational chemistry.