Machine-learned interatomic potential: Difference between revisions

Revision as of 00:10, 7 July 2023

Revision as of 00:12, 7 July 2023

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Beginning in the 1990s, researchers have employed machine learning programs to construct [[interatomic potential]]s, mapping atomic structures to their potential energies. Such machine learning potentials (MLPs) promised to threadfill the gap between [[density functional theory]], a highly-accurate but computationally-intensive simulation program, and empirically-derived or intuitively-approximated potentials, which were far computationally lighter but substantially less accurate. Improvements in artificial intelligence technology have served to heighten the accuracy of MLPs while lowering their computational cost, increasing machine learning's role in fitting potentials.<ref name="ML">{{cite journal|last1=Kocer|last2=Ko|last3=Behler|first1=Emir|first2=Tsz Wai|first3=Jorg|journal=Annual Review of Physical Chemistry|title=Neural Network Potentials: A Concise Overview of Methods|date=2022|volume=73|pages=163-86}}</ref><ref>{{cite journal|last1=Blank|first1=TB|last2=Brown|first2=SD|last3=Calhoun|last4=Doren|first4=DJ|first3=AW|date=1995|title=Neural network models of potential energy surfaces|journal=Journal of Chemistry and Physics|volume=103|number=10|pages=4129-37}}</ref>

Machine learning potentials began by using neural networks to tackle low dimensional systems. While promising, these models could not systematically account for interatomic energy interactions; they could be applied to small molecules in a vacuum and molecules interacting with frozen surfaces, but not much else, and even in these applications often relied on force fields or potentials derived empirically or with simulations.<ref name="ML"/> These models thus remained confined to academia.