Machine-learned interatomic potential: Difference between revisions

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{{Short description|Interatomic potentials constructed by machine learning programs}}

'''Machine-learned interatomic potentials''' ('''MLIPs'''), or simply '''machine learning potentials''' ('''MLPs'''), are [[interatomic potential]]s constructed byusing [[machine learning]] programs. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mapping atomic structures to their potential energies. These potentials are referred to as '''MLIPs''' or '''MLPs'''.

Such machine learning potentials promised to fill the gap between [[density functional theory]], a highly accurate but computationally intensive modelling method, and empirically derived or intuitively-approximated potentials, which were far lighter computationally but substantially less accurate. Improvements in [[artificial intelligence]] technology heightened the accuracy of MLPs while lowering their computational cost, increasing the role of machine learning 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|doi=10.1146/annurev-physchem-082720-034254 |pmid=34982580 |bibcode=2022ARPC...73..163K |doi-access=free|arxiv=2107.03727}}</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 Chemical Physics|volume=103|number=10|pages=4129–37|doi=10.1063/1.469597 |bibcode=1995JChPh.103.4129B }}</ref>