{"payload":{"header_redesign_enabled":false,"results":[{"id":"623237723","archived":false,"color":"#3572A5","followers":8,"has_funding_file":false,"hl_name":"pythonpanda2/AL4GAP_JCP","hl_trunc_description":"This repository provides documentation for running the Active Learning workflow for fitting Gaussian Approximation Potentials.","language":"Python","mirror":false,"owned_by_organization":false,"public":true,"repo":{"repository":{"id":623237723,"name":"AL4GAP_JCP","owner_id":46133994,"owner_login":"pythonpanda2","updated_at":"2023-08-24T11:36:07.555Z","has_issues":true}},"sponsorable":false,"topics":["vasp","density-functional-theory","high-performance-computing","quip","lammps","active-learning","opls","molten-salt","scan-xc","gaussian-approximation-potential"],"type":"Public","help_wanted_issues_count":0,"good_first_issue_issues_count":0,"starred_by_current_user":false}],"type":"repositories","page":1,"page_count":1,"elapsed_millis":99,"errors":[],"result_count":1,"facets":[],"protected_org_logins":[],"topics":null,"query_id":"","logged_in":false,"sign_up_path":"/signup?source=code_search_results","sign_in_path":"/login?return_to=https%3A%2F%2Fgithub.com%2Fsearch%3Fq%3Drepo%253Apythonpanda2%252FAL4GAP_JCP%2B%2Blanguage%253APython","metadata":null,"csrf_tokens":{"/pythonpanda2/AL4GAP_JCP/star":{"post":"tJhcme_hcq3AhNAhSSy3xGiR_NSv7OIC1lmld_EKmPcrag5byS_xL1JSAumgKByKjs9epnoXRrCBvkcBsCDvlw"},"/pythonpanda2/AL4GAP_JCP/unstar":{"post":"ZtX7_OeHwmqIGydB82h7yG4nFHRGS-Vd1M012UXirCgA2Zfg8CBsqb4bTg6in0t2Ff--yKVnj6-iJNxxcsmWjQ"},"/sponsors/batch_deferred_sponsor_buttons":{"post":"OaTqHNLoObVoPub8MDWKLRE5OhTyWL9-jMJpkNf2nBk0AOIo1oUuH8YkpOzoRjNzuqrPJrEB92nrWuVOWe6FUA"}}},"title":"Repository search results"}