{"id":20512030,"url":"https://github.com/wardlt/ternary-semiconductors-mhm","last_synced_at":"2026-04-20T11:02:01.610Z","repository":{"id":91621874,"uuid":"154890953","full_name":"WardLT/ternary-semiconductors-mhm","owner":"WardLT","description":"Scripts from a paper on discovering ternary semiconductors with machine learning and crystal structure prediction","archived":false,"fork":false,"pushed_at":"2018-10-26T20:47:00.000Z","size":36,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-05T22:44:49.187Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WardLT.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-10-26T20:29:26.000Z","updated_at":"2023-03-31T02:24:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"61391874-0f62-4af3-aadb-3290749e6bc8","html_url":"https://github.com/WardLT/ternary-semiconductors-mhm","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WardLT/ternary-semiconductors-mhm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fternary-semiconductors-mhm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fternary-semiconductors-mhm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fternary-semiconductors-mhm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fternary-semiconductors-mhm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WardLT","download_url":"https://codeload.github.com/WardLT/ternary-semiconductors-mhm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fternary-semiconductors-mhm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32044291,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T10:33:29.490Z","status":"ssl_error","status_checked_at":"2026-04-20T10:32:30.107Z","response_time":94,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-15T20:39:26.942Z","updated_at":"2026-04-20T11:02:01.590Z","avatar_url":"https://github.com/WardLT.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ternary Semiconductors from Machine Learning and Crystal Structure Prediction\n\nThis repository contains scripts and datasets needed to reproduce the machine learning results from a recent paper by Amsler et al. \n\n## Contents\n\nThe key machine learning task performed in this manuscript is to identify the compositions in the Ba-As-S ternary system that are favorable for semiconductor applications.\n\nThe `make-deltae-model.in` and `make-bandgap-model.in` models employ [Magpie](https://bitbucket.org/wolverton/magpie) to train machine learning models on data from the [OQMD](http://oqmd.org/) to predict stability (via the formation energy) and the band gap energy fo the model. The training set for the model is available in [`./datasets/`](./datasets/) and the models are in [`./models/`](./models) directory. \n\nThe `scan-BaAs-system.in` script runs the models on the Ba-As-S system and computes the stability of each prediction with respect to the convex hull of the training set. \n\nThe Jupyter notebook `plot-BaAsS-results.ipynb` produces the plots seen in the paper.\n\n## Installation\n\nYou must clone the repository using `git clone --recursive` to get the necessary Magpie source code. Then, follow the instructions in the Magpie documentation to compile Magpie.\n\nBesides Magpie, you need to install a Python 3 version of Jupyter with the packages listed in `requirements.txt`\n\n## Running the Scripts\n\nCall `./run-all.bs` to execute all of the scripts in this study.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fternary-semiconductors-mhm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwardlt%2Fternary-semiconductors-mhm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fternary-semiconductors-mhm/lists"}