{"id":37633265,"url":"https://github.com/fernandezfran/mlpotentials","last_synced_at":"2026-01-16T11:00:31.036Z","repository":{"id":167730531,"uuid":"459630525","full_name":"fernandezfran/MLPotentials","owner":"fernandezfran","description":"Machine learning interatomic potentials and their application to lithium batteries (seminar talk in Spanish).","archived":false,"fork":false,"pushed_at":"2022-07-01T22:15:40.000Z","size":18128,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-25T11:24:46.849Z","etag":null,"topics":["lithium-ion-batteries","machine-learning","machine-learning-potential"],"latest_commit_sha":null,"homepage":"","language":"TeX","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/fernandezfran.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-02-15T15:11:18.000Z","updated_at":"2022-07-01T21:55:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"ece9b586-02ba-4d2b-8954-f849045018a6","html_url":"https://github.com/fernandezfran/MLPotentials","commit_stats":null,"previous_names":["fernandezfran/mlpotentials"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/fernandezfran/MLPotentials","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandezfran%2FMLPotentials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandezfran%2FMLPotentials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandezfran%2FMLPotentials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandezfran%2FMLPotentials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fernandezfran","download_url":"https://codeload.github.com/fernandezfran/MLPotentials/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fernandezfran%2FMLPotentials/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478106,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T06:30:42.265Z","status":"ssl_error","status_checked_at":"2026-01-16T06:30:16.248Z","response_time":107,"last_error":"SSL_read: 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":["lithium-ion-batteries","machine-learning","machine-learning-potential"],"created_at":"2026-01-16T11:00:19.516Z","updated_at":"2026-01-16T11:00:30.971Z","avatar_url":"https://github.com/fernandezfran.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Potenciales interatómicos de aprendizaje automático y su aplicación a baterías de litio\n\nSeminario del doctorado en Física de [FAMAF](https://www.famaf.unc.edu.ar/investigaci%C3%B3n/%C3%A1reas-de-investigaci%C3%B3n/f%C3%ADsica-ofi/seminarios-de-f%C3%ADsica/), dictado en la Aula Magna Enrique Gaviola, el 22/04/2022.\n\nA la grabación de las diapositivas más la charla se puede acceder a través del \nsiguiente [link](https://drive.google.com/file/d/1oIAxwzCobBo9PtcgWuFWWTdKZAFTW-8H/view?usp=sharing).\n\n\n## Resumen\n\nEn el campo de las simulaciones computacionales existen principalmente dos \nvariantes para el estudio de materiales. Por un lado, las que se realizan con \npotenciales de interacción que se calculan a partir de primeros principios, y por\notro lado las que emplean algún tipo de aproximación para estos potenciales. Las \nprimeras de ellas tienen una gran precisión pero se encuentran limitadas a \nsistemas pequeños mientras que las segundas permiten simulaciones en escalas más \ngrandes, pero su precisión depende de la forma funcional que se elija para el \npotencial en cuestión. Debido a la complejidad en aumento de los sistemas \nelectroquímicos de interés en el área de las baterías de litio, es necesario que \nlas simulaciones puedan realizarse a escalas grandes sin perder precisión. Los \npotenciales interatómicos de aprendizaje automático ofrecen representar la \nsuperficie energía-potencial mediante un entrenamiento con datos a partir de \ncálculos de estructura electrónica, que permiten llevar esto a cabo. En este \nseminario se introducen dichos potenciales y se presentan aplicaciones de los \nmismos en distintos componentes de las baterías de litio.\n\n\n## Referencias\n\n- Deringer, V. L. (2020). Modelling and understanding battery materials with \nmachine-learning-driven atomistic simulations. _Journal of Physics: Energy_,\n2(4), 041003.\n\n- Deringer, V. L., Caro, M. A., \\\u0026 Csányi, G. (2019). Machine learning interatomic \npotentials as emerging tools for materials science. _Advanced Materials_,\n31(46), 1902765.\n\n- Mishin, Y. (2021). Machine-learning interatomic potentials for materials \nscience. _Acta Materialia_, 214, 116980.\n\n- Behler, J. (2017). First principles neural network potentials for reactive \nsimulations of large molecular and condensed systems. _Angewandte Chemie \nInternational Edition_, 56(42), 12828-12840.\n\n- Behler, J. (2016). Perspective: Machine learning potentials for atomistic \nsimulations. _The Journal of chemical physics_, 145(17), 170901.\n\n- Mueller, T., Hernandez, A., \\\u0026 Wang, C. (2020). Machine learning for \ninteratomic potential models. _The Journal of chemical physics_, 152(5), 050902.\n\n- Hong, Y., Hou, B., Jiang, H., \\\u0026 Zhang, J. (2020). Machine learning and \nartificial neural network accelerated computational discoveries in materials \nscience. _Wiley Interdisciplinary Reviews: Computational Molecular Science_, \n10(3), e1450.\n\n- Botu, V., Batra, R., Chapman, J., \\\u0026 Ramprasad, R. (2017). Machine learning \nforce fields: construction, validation, and outlook. _The Journal of Physical \nChemistry C_, 121(1), 511-522.\n\n- Li, W., Ando, Y., Minamitani, E., \\\u0026 Watanabe, S. (2017). Study of Li atom \ndiffusion in amorphous Li3PO4 with neural network potential. _The Journal of \nchemical physics_, 147(21), 214106.\n\n- Fujikake, S., Deringer, V. L., Lee, T. H., Krynski, M., Elliott, S. R., \\\u0026 \nCsányi, G. (2018). Gaussian approximation potential modeling of lithium \nintercalation in carbon nanostructures. _The Journal of chemical physics_, \n148(24), 241714.\n\n- Artrith, N., Urban, A., \\\u0026 Ceder, G. (2018). Constructing first-principles \nphase diagrams of amorphous Li x Si using machine-learning-assisted sampling with\nan evolutionary algorithm. _The Journal of chemical physics_, 148(24), 241711.\n\n\n## Compilación\n\nPara compilar se puede utilizar el **Makefile** simplemente tipeando en la \nterminal (Linux OS):\n```bash\nmake\n```\npara borrar los archivos generados utilizar `make clean`.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandezfran%2Fmlpotentials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffernandezfran%2Fmlpotentials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffernandezfran%2Fmlpotentials/lists"}