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Machine-learned interatomic potential
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<h2 id="gaussian-approximation-potential-gap">Gaussian Approximation Potential (GAP)</h2> <p>One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP),<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a><a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a><a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> which combines compact descriptors of local atomic environments<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a> with Gaussian process regression<a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a> to machine learn the <a href="/facts/Potential_energy_surface/YDUNcSM8">potential energy surface</a> 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 <a href="/facts/Carbon/ukrgQexo">Carbon</a>,<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a><a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> <a href="/facts/Silicon/cgWx0hdr">Silicon</a>,<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> <a href="/facts/Phosphorus/27xCeIqs">Phosphorus</a>,<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> and <a href="/facts/Tungsten/WYwbNDYX">Tungsten</a>,<a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> as well as for multicomponent systems such as Ge2Sb2Te5<a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a> and <a href="/facts/Austenitic_stainless_steel/oaJRCwTf">austenitic</a> <a href="/facts/Stainless_steel/IDdo8bEx">stainless steel</a>, Fe7Cr2Ni.<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> </p> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Kocer, Emir; Ko, Tsz Wai; Behler, Jorg (2022). "Neural Network Potentials: A Concise Overview of Methods". Annual Review of Physical Chemistry. 73: 163–86. arXiv:2107.03727. Bibcode:2022ARPC...73..163K. doi:10.1146/annurev-physchem-082720-034254. PMID 34982580. <a href="https://doi.org/10.1146%2Fannurev-physchem-082720-034254" target="_blank">https://doi.org/10.1146%2Fannurev-physchem-082720-034254</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Blank, TB; Brown, SD; Calhoun, AW; Doren, DJ (1995). "Neural network models of potential energy surfaces". Journal of Chemical Physics. 103 (10): 4129–37. Bibcode:1995JChPh.103.4129B. doi:10.1063/1.469597. <a href="/wiki/Bibcode_(identifier)" target="_blank">/wiki/Bibcode_(identifier)</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Kocer, Emir; Ko, Tsz Wai; Behler, Jorg (2022). "Neural Network Potentials: A Concise Overview of Methods". Annual Review of Physical Chemistry. 73: 163–86. arXiv:2107.03727. Bibcode:2022ARPC...73..163K. doi:10.1146/annurev-physchem-082720-034254. PMID 34982580. <a href="https://doi.org/10.1146%2Fannurev-physchem-082720-034254" target="_blank">https://doi.org/10.1146%2Fannurev-physchem-082720-034254</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Kocer, Emir; Ko, Tsz Wai; Behler, Jorg (2022). "Neural Network Potentials: A Concise Overview of Methods". Annual Review of Physical Chemistry. 73: 163–86. arXiv:2107.03727. Bibcode:2022ARPC...73..163K. doi:10.1146/annurev-physchem-082720-034254. PMID 34982580. <a href="https://doi.org/10.1146%2Fannurev-physchem-082720-034254" target="_blank">https://doi.org/10.1146%2Fannurev-physchem-082720-034254</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Ghasemi, SA; Hofstetter, A; Saha, S; Goedecker, S (2015). "Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network". Physical Review B. 92 (4): 045131. arXiv:1501.07344. Bibcode:2015PhRvB..92d5131G. doi:10.1103/PhysRevB.92.045131. <a href="/wiki/ArXiv_(identifier)" target="_blank">/wiki/ArXiv_(identifier)</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Kocer, Emir; Ko, Tsz Wai; Behler, Jorg (2022). "Neural Network Potentials: A Concise Overview of Methods". Annual Review of Physical Chemistry. 73: 163–86. arXiv:2107.03727. Bibcode:2022ARPC...73..163K. doi:10.1146/annurev-physchem-082720-034254. PMID 34982580. <a href="https://doi.org/10.1146%2Fannurev-physchem-082720-034254" target="_blank">https://doi.org/10.1146%2Fannurev-physchem-082720-034254</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Behler, J; Parrinello, M (2007). "Generalized neural-network representation of high-dimensional potential-energy surfaces". Physical Review Letters. 148 (14). Bibcode:2007PhRvL..98n6401B. doi:10.1103/PhysRevLett.98.146401. 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