{"id":935,"date":"2024-10-18T17:17:24","date_gmt":"2024-10-18T17:17:24","guid":{"rendered":"https:\/\/doublelayer.eu\/vilab\/?p=935"},"modified":"2024-10-18T18:03:45","modified_gmt":"2024-10-18T18:03:45","slug":"optimizers-in-ase","status":"publish","type":"post","link":"https:\/\/doublelayer.eu\/vilab\/2024\/10\/18\/optimizers-in-ase\/","title":{"rendered":"Optimizers in ASE"},"content":{"rendered":"\n

Optimising even simple intermediate on metal surfaces might be tricky. They tend change the adsorption site. OOH also very flexible in changing geometry. Thus, some optimizers get stuck (like BFGSLineSearch) while others just crush (like GPMin).<\/p>\n\n\n\n

Here is my test with OCPCalculator and EquiformerV2-31M-S2EF-OC20-All+MD (on 1 CPU in Google Colab). Optimisation of OH on Pt(111) is simple.<\/p>\n\n\n

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Here SciPyFminBFGS failed and other optimizers ended up with the same structure, shown below.<\/p>\n\n\n

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\"\"<\/figure><\/div>\n\n\n

Optimisation of OOH on Pt(111) is challenging. GoodOldQuasiNewton, FIRE, FIRE2, MDMin, and BFGSLineSearch (QuasiNewton) do not converge in 95 cycles (which I set as a maximum number of cycles). GPMin, SciPyFminBFGS, SciPyFminCG, and LBFGSLineSearch failed. <\/p>\n\n\n

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\"\"<\/figure><\/div>\n\n
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Geometry obtained with FIRE2 (similar to BFGS and FIRE)<\/figcaption><\/figure><\/div>\n\n
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Geometry obtained with LBFGS. It is clearly far from the optimum.<\/figcaption><\/figure><\/div>","protected":false},"excerpt":{"rendered":"

Optimising even simple intermediate on metal surfaces might be tricky. They tend change the adsorption site. OOH also very flexible in changing geometry. Thus, some optimizers get stuck (like BFGSLineSearch) while others just crush (like GPMin). Here is my test with OCPCalculator and EquiformerV2-31M-S2EF-OC20-All+MD (on 1 CPU in Google Colab). Optimisation of OH on Pt(111)… Read More<\/a><\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12,41],"tags":[48,53],"class_list":["post-935","post","type-post","status-publish","format-standard","hentry","category-know-how","category-teaching","tag-ase","tag-gpaw"],"_links":{"self":[{"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/posts\/935","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/comments?post=935"}],"version-history":[{"count":7,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/posts\/935\/revisions"}],"predecessor-version":[{"id":951,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/posts\/935\/revisions\/951"}],"wp:attachment":[{"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/media?parent=935"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/categories?post=935"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/doublelayer.eu\/vilab\/wp-json\/wp\/v2\/tags?post=935"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}