{"id":15559,"date":"2016-03-12T00:32:00","date_gmt":"2016-03-11T22:32:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=15559"},"modified":"2016-03-12T00:32:00","modified_gmt":"2016-03-11T22:32:00","slug":"sgo-an-ultrafast-engine-for-atomic-structure-global-optimization-by-differential-evolution","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15559","title":{"rendered":"SGO: An ultrafast engine for atomic structure global optimization by differential evolution"},"content":{"rendered":"<p>This paper presents a fast method for global search of atomic structures. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and an ultrafast density functional theory plane-wave code run on GPU machines. It can search the global minimum configuration of crystals, two-dimensional materials and quantum clusters in a very short time (half or several hours). The engine is also able to search the energy landscape of a given system, which is useful for exploration of materials properties for emerging applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a fast method for global search of atomic structures. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and an ultrafast density functional theory plane-wave code run on GPU machines. It can search the global minimum configuration of crystals, two-dimensional materials and quantum clusters [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[196,616,166,20,1650],"class_list":["post-15559","post","type-post","status-publish","format-standard","hentry","category-paper","tag-condensed-matter","tag-differential-evolution","tag-materials-science","tag-nvidia","tag-nvidia-geforce-gtx-980"],"views":2310,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15559","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15559"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15559\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15559"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15559"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}