Machine-learned interatomic potential: Difference between revisions

Revision as of 08:05, 9 April 2024

Revision as of 18:07, 13 May 2024

Line 10:

== Gaussian Approximation Potential (GAP) ==

One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP),<ref>{{Cite journal |last1=Bartók |first1=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|pmid=20481899 |arxiv=0910.1019 |bibcode=2010PhRvL.104m6403B }}</ref><ref>{{Cite journal |last1=Bartók |first1=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=December 2017 |title=Machine learning unifies the modeling of materials and molecules |journal=Science Advances |language=en |volume=3 |issue=12 |pages=e1701816 |doi=10.1126/sciadv.1701816 |issn=2375-2548 |pmc=5729016 |pmid=29242828|arxiv=1706.00179 |bibcode=2017SciA....3E1816B }}</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 |last1=Bartók |first1=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|arxiv=1209.3140 |bibcode=2013PhRvB..87r4115B }}</ref> with Gaussian process regression<ref>{{Cite book |last1=Rasmussen |first1=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 |last1=Deringer |first1=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|arxiv=1611.03277 |bibcode=2017PhRvB..95i4203D }}</ref> [[Silicon]],<ref>{{Cite journal |last1=Bartók |first1=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|arxiv=1805.01568 |bibcode=2018PhRvX...8d1048B }}</ref> [[Phosphorus]],<ref>{{Cite journal |last1=Deringer |first1=Volker L. |last2=Caro |first2=Miguel A. |last3=Csányi |first3=Gábor |date=2020-10-29 |title=A general-purpose machine-learning force field for bulk and nanostructured phosphorus |journal=Nature Communications |language=en |volume=11 |issue=1 |pages=5461 |doi=10.1038/s41467-020-19168-z |issn=2041-1723 |pmc=7596484 |pmid=33122630|bibcode=2020NatCo..11.5461D }}</ref> and [[Tungsten]],<ref>{{Cite journal |last1=Szlachta |first1=Wojciech J. |last2=Bartók |first2=Albert P. |last3=Csányi |first3=Gábor |date=2014-09-24 |title=Accuracy and transferability of Gaussian approximation potential models for tungsten |url=https://link.aps.org/doi/10.1103/PhysRevB.90.104108 |journal=Physical Review B |volume=90 |issue=10 |pages=104108 |doi=10.1103/PhysRevB.90.104108|bibcode=2014PhRvB..90j4108S }}</ref> as well as for multicomponent systems such as Ge₂Sb₂Te₅<ref>{{Cite journal |last1=Mocanu |first1=Felix C. |last2=Konstantinou |first2=Konstantinos |last3=Lee |first3=Tae Hoon |last4=Bernstein |first4=Noam |last5=Deringer |first5=Volker L. |last6=Csányi |first6=Gábor |last7=Elliott |first7=Stephen R. |date=2018-09-27 |title=Modeling the Phase-Change Memory Material, Ge 2 Sb 2 Te 5 , with a Machine-Learned Interatomic Potential |url=https://pubs.acs.org/doi/10.1021/acs.jpcb.8b06476 |journal=The Journal of Physical Chemistry B |language=en |volume=122 |issue=38 |pages=8998–9006 |doi=10.1021/acs.jpcb.8b06476 |pmid=30173522 |issn=1520-6106}}</ref> and [[Austenitic stainless steel|austenitic]] [[stainless steel]], Fe₇Cr₂Ni.<ref>{{Cite journal |last1=Shenoy |first1=Lakshmi |last2=Woodgate |first2=Christopher D. |last3=Staunton |first3=Julie B. |last4=Bartók |first4=Albert P. |last5=Becquart |first5=Charlotte S. |last6=Domain |first6=Christophe |last7=Kermode |first7=James R. |date=2024-03-22 |title=<nowiki>Collinear-spin machine learned interatomic potential for ${\mathrm{Fe}}_{7}{\mathrm{Cr}}_{2}\mathrm{Ni}$ alloy</nowiki> |url=https://link.aps.org/doi/10.1103/PhysRevMaterials.8.033804 |journal=Physical Review Materials |volume=8 |issue=3 |pages=033804 |doi=10.1103/PhysRevMaterials.8.033804|arxiv=2309.08689 }}</ref>

==References==