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m Toxophilite1440 moved page Machine learning potential to Machine-learned interatomic potential: Switching to the more widely used literature name for these potentials |
Adding cross references to other Wiki pages in GAP section. |
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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. These potentials are generally referred to as 'machine-learned interatomic potentials' (MLIPs) or simply 'machine learning potentials' (MLPs). Such machine learning potentials promised to fill 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|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 Chemistry and Physics|volume=103|number=10|pages=4129–37|doi=10.1063/1.469597 |bibcode=1995JChPh.103.4129B }}</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.
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== Gaussian Approximation Potential (GAP) ==
One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP)<ref>{{Cite journal |last=Bartók |first=Albert P. |last2=Payne |first2=Mike C. |last3=Kondor |first3=Risi |last4=Csányi |first4=Gábor |date=2010-04-01 |title=Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons |url=https://link.aps.org/doi/10.1103/PhysRevLett.104.136403 |journal=Physical Review Letters |volume=104 |issue=13 |pages=136403 |doi=10.1103/PhysRevLett.104.136403}}</ref><ref>{{Cite journal |last=Bartók |first=Albert P. |last2=De |first2=Sandip |last3=Poelking |first3=Carl |last4=Bernstein |first4=Noam |last5=Kermode |first5=James R. |last6=Csányi |first6=Gábor |last7=Ceriotti |first7=Michele |date=2017-12 |title=Machine learning unifies the modeling of materials and molecules |url=https://www.science.org/doi/10.1126/sciadv.1701816 |journal=Science Advances |language=en |volume=3 |issue=12 |doi=10.1126/sciadv.1701816 |issn=2375-2548 |pmc=PMC5729016 |pmid=29242828}}</ref><ref>{{Cite web |title=Gaussian approximation potential – Machine learning atomistic simulation of materials and molecules |url=https://gap-ml.org/ |access-date=2024-04-04 |language=en-US}}</ref>, which combines compact descriptors of local atomic environments<ref>{{Cite journal |last=Bartók |first=Albert P. |last2=Kondor |first2=Risi |last3=Csányi |first3=Gábor |date=2013-05-28 |title=On representing chemical environments |url=https://link.aps.org/doi/10.1103/PhysRevB.87.184115 |journal=Physical Review B |volume=87 |issue=18 |pages=184115 |doi=10.1103/PhysRevB.87.184115}}</ref> with Gaussian process regression<ref>{{Cite book |last=Rasmussen |first=Carl Edward |title=Gaussian processes for machine learning |last2=Williams |first2=Christopher K. I. |date=2008 |publisher=MIT Press |isbn=978-0-262-18253-9 |edition=3. print |series=Adaptive computation and machine learning |___location=Cambridge, Mass.}}</ref> to machine learn the [[potential energy surface]] of a given system. To date, the GAP framework has been used to successfully develop a number of MLIPs for various systems, including for elemental systems such as [[Carbon]]<ref>{{Cite journal |last=Deringer |first=Volker L. |last2=Csányi |first2=Gábor |date=2017-03-03 |title=Machine learning based interatomic potential for amorphous carbon |url=https://link.aps.org/doi/10.1103/PhysRevB.95.094203 |journal=Physical Review B |volume=95 |issue=9 |pages=094203 |doi=10.1103/PhysRevB.95.094203}}</ref>, [[Silicon]]<ref>{{Cite journal |last=Bartók |first=Albert P. |last2=Kermode |first2=James |last3=Bernstein |first3=Noam |last4=Csányi |first4=Gábor |date=2018-12-14 |title=Machine Learning a General-Purpose Interatomic Potential for Silicon |url=https://link.aps.org/doi/10.1103/PhysRevX.8.041048 |journal=Physical Review X |volume=8 |issue=4 |pages=041048 |doi=10.1103/PhysRevX.8.041048}}</ref>,
==References==
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