[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120212-en":3,"doc-seo-120212-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120212,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Interatomic Potentials for Heterogeneous Catalysis","Machine learning interatomic potentials are developed to model heterogeneous catalysis processes with improved fidelity and efficiency. The work presents a methodological framework using the M(C)IP concept and related neural potential components to represent potential energy surfaces and atomic interactions. It discusses evaluation through benchmark datasets and training setups, compares predictive performance across different network architectures, and analyzes how well the learned potentials reproduce reference energies, forces, and structural properties relevant to catalytic environments.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2024  \nMachine Learning Interatomic Potentials for Heterogeneous Catalysis  \nTang, Deqi ; Ketkaew, Rangsiman ; Luber, Sandra  \nDOI: [https://doi.org/10.1002/chem.202401148](https://doi.org/10.1002/chem.202401148)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-271350](https://doi.org/10.5167/uzh-271350)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4 .0) License.  \nOriginally published at:  \nTang, Deqi; Ketkaew, Rangsiman; Luber, Sandra (2024) . Machine Learning Interatomic Potentials for Heterogeneous Catalysis. Chemistry, 30(60):e202401148 .  \nDOI: [https://doi.org/10.1002/chem.202401148](https://doi.org/10.1002/chem.202401148)  \nChemistry—A European Journal  \n􀀁􀀂􀀃􀀄􀀂􀀅  \n􀀁􀀂􀀃􀀄􀀂􀀅􀀆􀀇􀀈􀀉􀀄􀀈􀀉􀀉􀀊􀀇􀀋􀀌􀀍􀀎􀀄􀀊􀀉􀀊􀀏􀀉􀀈􀀈􀀏􀀐  \n􀀒􀀒􀀒.􀀜h􀀂􀀎􀀂􀀗􀀕j.􀀛􀀕􀀈  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇 􀀈􀀇􀀂􀀉􀀆􀀅􀀆􀀊 􀀋􀀆􀀌􀀇􀀉􀀂􀀌􀀍􀀎􀀅􀀃 􀀏􀀍􀀌􀀇􀀆􀀌􀀅􀀂􀀐􀀑 􀀒􀀍􀀉􀀓􀀇􀀌􀀇􀀉􀀍􀀊􀀇􀀆􀀇􀀍􀀔􀀑 􀀕􀀂􀀌􀀂􀀐􀀖􀀑􀀅􀀑  \n􀀁􀀂􀀃􀀄 􀀅􀀆􀀇􀀈􀀉 􀀊􀀆􀀋 􀀌􀀆􀀇􀀈􀀍􀀄􀀎􀀆􀀇 􀀏􀀂􀀐􀀑􀀆􀀂􀀒􀀉 􀀊􀀆􀀋 􀀆􀀇􀀓 􀀔􀀆􀀇􀀓􀀕􀀆 􀀖􀀗􀀘􀀂􀀕􀀙 􀀊􀀆􀀋  \n􀀚􀀐􀀛􀀎􀀄􀀍􀀐􀀄􀀜 􀀎􀀛􀀓􀀂􀀝􀀄􀀇􀀈 􀀜􀀆􀀇 􀀞􀀕􀀛 􀀄􀀓􀀂 􀀆􀀝􀀗􀀆􀀘􀀝􀀂 􀀄􀀇􀀍􀀄􀀈h􀀐􀀍 􀀄􀀇􀀐􀀛 􀀐h􀀂􀀓􀀂􀀍􀀄􀀈􀀇 􀀛f 􀀇􀀛 􀀂􀀝 h􀀂􀀐􀀂􀀕􀀛􀀈􀀂􀀇􀀂􀀛􀀗􀀍 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍 􀀆􀀍 􀀇􀀂􀀂􀀓􀀂􀀓 􀀇􀀛􀀒􀀆􀀓􀀆y􀀍􀀄􀀇 􀀐h􀀂 􀀆􀀕􀀂􀀆􀀍 􀀛f􀀉 􀀂. 􀀈.􀀉 􀀜h􀀂􀀎􀀄􀀍􀀐􀀕y􀀉 􀀎􀀆􀀐􀀂􀀕􀀄􀀆􀀝􀀍 􀀍􀀜􀀄􀀂􀀇􀀜􀀂􀀉 􀀆􀀇􀀓 􀀘􀀄􀀛􀀝􀀛􀀈y. C􀀝􀀆􀀍􀀍􀀄􀀜􀀆􀀝 f􀀛􀀕􀀜􀀂 f􀀄􀀂􀀝􀀓􀀍 􀀆􀀇􀀓 􀀁􀀂 􀀃􀀄􀀃􀀅􀀃􀀆 􀀜􀀆􀀝􀀜􀀗􀀝􀀆􀀐􀀄􀀛􀀇􀀍 h􀀆 􀀂 􀀘􀀂􀀂􀀇 􀀒􀀄􀀓􀀂􀀝y 􀀆􀀓􀀛􀀞􀀐􀀂􀀓 􀀄􀀇 􀀎􀀛􀀝􀀂􀀜􀀗􀀝􀀆􀀕 􀀍􀀄􀀎􀀗􀀝􀀆􀀐􀀄􀀛􀀇􀀍 . H􀀛􀀒􀀂 􀀂􀀕􀀉 􀀐h􀀂􀀍􀀂 􀀎􀀂􀀐h􀀛􀀓􀀍􀀗􀀍􀀗􀀆􀀝􀀝y 􀀍􀀗ff􀀂􀀕 f􀀕􀀛􀀎 􀀐h􀀂 􀀓􀀕􀀆􀀒􀀘􀀆􀀜􀀑􀀍 􀀛f 􀀂􀀄􀀐h􀀂􀀕 􀀝􀀛􀀒 􀀆􀀜􀀜􀀗􀀕􀀆􀀜y 􀀛􀀕 h􀀄􀀈h 􀀜􀀛􀀍􀀐 . 􀀌􀀂􀀜􀀂􀀇􀀐􀀝y􀀉 􀀐h􀀂 􀀓􀀂 􀀂􀀝􀀛􀀞􀀎􀀂􀀇􀀐 􀀛f 􀀎􀀆􀀜h􀀄􀀇􀀂 􀀝􀀂􀀆􀀕􀀇􀀄􀀇􀀈􀀄􀀇􀀐􀀂􀀕􀀆􀀐􀀛􀀎􀀄􀀜 􀀞􀀛􀀐􀀂􀀇􀀐􀀄􀀆􀀝􀀍 (M􀀖IP􀀍) h􀀆􀀍 􀀘􀀂􀀜􀀛􀀎􀀂 􀀎􀀛􀀕􀀂 􀀆􀀇􀀓 􀀎􀀛􀀕􀀂􀀞􀀛􀀞􀀗􀀝􀀆􀀕 􀀆􀀍 􀀐h􀀂y 􀀜􀀆􀀇 􀀐􀀆􀀜􀀑􀀝􀀂 􀀐h􀀂 􀀞􀀕􀀛􀀘􀀝􀀂􀀎􀀍 􀀄􀀇 􀀃􀀗􀀂􀀍􀀐􀀄􀀛􀀇 􀀆􀀇􀀓 􀀜􀀆􀀇  \n􀀓􀀂􀀝􀀄 􀀂􀀕 􀀕􀀆􀀐h􀀂􀀕 􀀆􀀜􀀜􀀗􀀕􀀆􀀐􀀂 􀀕􀀂􀀍􀀗􀀝􀀐􀀍 􀀆􀀐 􀀍􀀄􀀈􀀇􀀄f􀀄􀀜􀀆􀀇􀀐􀀝y 􀀝􀀛􀀒􀀂􀀕 􀀜􀀛􀀎􀀞􀀗􀀐􀀆 -􀀐􀀄􀀛􀀇􀀆􀀝 􀀜􀀛􀀍􀀐 . I􀀇 􀀐h􀀄􀀍 􀀕􀀂 􀀄􀀂􀀒􀀉 􀀐h􀀂 􀀆􀀐􀀛􀀎􀀄􀀍􀀐􀀄􀀜 􀀎􀀛􀀓􀀂􀀝􀀄􀀇􀀈 􀀛f 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜􀀍y􀀍􀀐􀀂􀀎􀀍 􀀒􀀄􀀐h 􀀐h􀀂 􀀆􀀄􀀓 􀀛f M􀀖IP􀀍 􀀄􀀍 􀀓􀀄􀀍􀀜􀀗􀀍􀀍􀀂􀀓􀀉 􀀍h􀀛􀀒􀀜􀀆􀀍􀀄􀀇􀀈 􀀕􀀂􀀜􀀂􀀇􀀐􀀝y 􀀓􀀂 􀀂􀀝􀀛􀀞􀀂􀀓 M􀀖IP 􀀎􀀛􀀓􀀂􀀝􀀍 􀀆􀀇􀀓 􀀍􀀂􀀝􀀂􀀜􀀐􀀂􀀓 􀀆􀀞􀀞􀀝􀀄􀀜􀀆􀀐􀀄􀀛􀀇􀀍 f􀀛􀀕 􀀐h􀀂􀀎􀀛􀀓􀀂􀀝􀀄􀀇􀀈 􀀛f h􀀂􀀐􀀂􀀕􀀛􀀈􀀂􀀇􀀂􀀛􀀗􀀍 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜 􀀍y􀀍􀀐􀀂􀀎􀀍 . W􀀂 􀀆􀀝􀀍􀀛 h􀀄􀀈h􀀝􀀄􀀈h􀀐􀀐h􀀂 􀀘􀀂􀀍􀀐 􀀞􀀕􀀆􀀜􀀐􀀄􀀜􀀂􀀍 􀀆􀀇􀀓 􀀜h􀀆􀀝􀀝􀀂􀀇􀀈􀀂􀀍 f􀀛􀀕 M􀀖IP􀀍 􀀆􀀇􀀓 􀀈􀀄 􀀂 􀀆􀀇 􀀛􀀗􀀐􀀝􀀛􀀛􀀑 f􀀛􀀕 f􀀗􀀐􀀗􀀕􀀂 􀀒􀀛􀀕􀀑􀀍 􀀛􀀇 M􀀖IP􀀍 􀀄􀀇 􀀐h􀀂 f􀀄􀀂􀀝􀀓 􀀛f h􀀂􀀐􀀂􀀕􀀛􀀈􀀂􀀇􀀂􀀛􀀗􀀍􀀜􀀆􀀐􀀆􀀝y􀀍􀀄􀀍 .  \n􀀗􀀘 􀀋􀀆􀀌􀀉􀀍􀀙􀀔􀀃􀀌􀀅􀀍􀀆  \n􀀁􀀂􀀍􀀞􀀄􀀐􀀂 􀀐h􀀂 f􀀆􀀍􀀐 􀀓􀀂 􀀂􀀝􀀛􀀞􀀎􀀂􀀇􀀐 􀀛f 􀀐h􀀂 􀀎􀀛􀀓􀀂􀀕􀀇 􀀜􀀆􀀐􀀆􀀝y􀀍􀀄􀀍 􀀄􀀇􀀓􀀗􀀍􀀐􀀕y􀀉􀀐h􀀂 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐 􀀓􀀂􀀍􀀄􀀈􀀇 h􀀆􀀍 􀀍􀀐􀀄􀀝􀀝 􀀘􀀂􀀂􀀇 􀀝􀀆􀀕􀀈􀀂􀀝y 􀀘􀀆􀀍􀀂􀀓 􀀛􀀇 􀀆 􀀐􀀕􀀄􀀆􀀝-􀀆􀀇􀀓 -􀀂􀀕􀀕􀀛􀀕 􀀂x􀀞􀀂􀀕􀀄􀀎􀀂􀀇􀀐􀀆􀀝 􀀎􀀂􀀆􀀇􀀍 . 􀀚􀀍 􀀆 􀀕􀀂􀀍􀀗􀀝􀀐􀀉 􀀐h􀀂 􀀐􀀄􀀎􀀂􀀝􀀄􀀇􀀂 f􀀛􀀕 􀀐h􀀂􀀓􀀂 􀀂􀀝􀀛􀀞􀀎􀀂􀀇􀀐 􀀆􀀇􀀓 􀀜􀀛􀀎􀀎􀀂􀀕􀀜􀀄􀀆􀀝􀀄z􀀆􀀐􀀄􀀛􀀇 􀀛f 􀀆 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐 􀀜􀀆􀀇 􀀐􀀆􀀑􀀂 10– 20 y􀀂􀀆􀀕􀀍 . 􀀊1􀀋 U􀀇􀀓􀀂􀀕􀀍􀀐􀀆􀀇􀀓􀀄􀀇􀀈 􀀐h􀀂 􀀎􀀄􀀜􀀕􀀛􀀍􀀜􀀛􀀞􀀄􀀜 􀀎􀀂􀀜h􀀆􀀇􀀄􀀍􀀎􀀍 􀀄􀀇 -􀀛􀀝 􀀂􀀓 􀀄􀀇 􀀜􀀆􀀐􀀆􀀝y􀀍􀀄􀀍 􀀄􀀍 􀀕􀀂􀀈􀀆􀀕􀀓􀀂􀀓 􀀆􀀍 􀀆􀀇 􀀄􀀎􀀞􀀛􀀕􀀐􀀆􀀇􀀐 􀀆􀀍􀀞􀀂􀀜􀀐 􀀛f 􀀐h􀀂􀀜􀀆􀀐􀀆􀀝y􀀍􀀄􀀍 􀀄􀀇􀀓􀀗􀀍􀀐􀀕y 􀀐􀀛 􀀍h􀀛􀀕􀀐􀀂􀀇 􀀐h􀀂 􀀐􀀄􀀎􀀂 f􀀕􀀆􀀎􀀂 f􀀛􀀕 􀀓􀀂 􀀂􀀝􀀛􀀞􀀄􀀇􀀈 􀀇􀀂􀀒 h􀀂􀀐􀀂􀀕􀀛􀀈􀀂􀀇􀀂􀀛􀀗􀀍 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍􀀉 􀀒h􀀂􀀕􀀂 􀀎􀀗􀀝􀀐􀀄􀀞􀀝􀀂 􀀞h􀀆􀀍􀀂􀀍 􀀆􀀕􀀂 􀀄􀀇 􀀛􀀝 􀀂􀀓 􀀄􀀇􀀐h􀀂 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜 􀀞􀀕􀀛􀀜􀀂􀀍􀀍 . 􀀅􀀛 f􀀆􀀜􀀄􀀝􀀄􀀐􀀆􀀐􀀂 􀀐h􀀂 􀀜􀀛􀀎􀀞􀀕􀀂h􀀂􀀇􀀍􀀄􀀛􀀇 􀀛f 􀀍􀀐􀀕􀀗􀀜􀀐􀀗􀀕􀀂-􀀞􀀕􀀛􀀞􀀂􀀕􀀐y 􀀕􀀂􀀝􀀆􀀐􀀄􀀛􀀇􀀍h􀀄􀀞􀀍 􀀛f 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍􀀉 􀀆􀀐􀀛􀀎􀀄􀀍􀀐􀀄􀀜 􀀎􀀛􀀓􀀂􀀝􀀄􀀇􀀈􀀉􀀂. 􀀈 .􀀉 􀀁􀀂 􀀃􀀄􀀃􀀅􀀃􀀆 􀀜􀀆􀀝􀀜􀀗􀀝􀀆􀀐􀀄􀀛􀀇􀀍 􀀆􀀇􀀓 􀀜􀀝􀀆􀀍􀀍􀀄􀀜􀀆􀀝 􀀎􀀛􀀝􀀂􀀜􀀗􀀝􀀆􀀕 􀀓y􀀇􀀆􀀎􀀄􀀜􀀍(M􀀁) 􀀍􀀄􀀎􀀗􀀝􀀆􀀐􀀄􀀛􀀇􀀍 􀀘􀀆􀀍􀀂􀀓 􀀛􀀇 f􀀛􀀕􀀜􀀂 f􀀄􀀂􀀝􀀓􀀍􀀉 h􀀆 􀀂 􀀘􀀂􀀂􀀇 􀀒􀀄􀀓􀀂􀀝y 􀀗􀀐􀀄􀀝􀀄z􀀂􀀓 􀀄􀀇 􀀐h􀀂 􀀂x􀀞􀀝􀀛􀀕􀀆􀀐􀀄􀀛􀀇 􀀛f 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜 􀀎􀀂􀀜h􀀆􀀇􀀄􀀍􀀎􀀍 􀀆􀀇􀀓 􀀓􀀂􀀍􀀄􀀈􀀇  \n􀀛f 􀀇􀀛 􀀂􀀝 h􀀂􀀐􀀂􀀕􀀛􀀈􀀂􀀇􀀂􀀛􀀗􀀍 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍 . I􀀇 􀀎􀀆􀀇y 􀀍􀀜􀀂􀀇􀀆􀀕􀀄􀀛􀀍􀀉 􀀐h􀀂􀀆􀀐􀀛􀀎􀀄􀀍􀀐􀀄􀀜 􀀎􀀛􀀓􀀂􀀝􀀄􀀇􀀈 􀀛f 􀀐h􀀂 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜 􀀞􀀕􀀛􀀜􀀂􀀍􀀍 􀀕􀀂􀀝􀀄􀀂􀀍 􀀛􀀇 􀀇􀀗􀀎􀀂􀀕􀀛􀀗􀀍􀀂􀀇􀀂􀀕􀀈y 􀀆􀀇􀀓 f􀀛􀀕􀀜􀀂 􀀂 􀀆􀀝􀀗􀀆􀀐􀀄􀀛􀀇􀀍 f􀀛􀀕 􀀐h􀀂 􀀎􀀆􀀇y-􀀘􀀛􀀓y 􀀍y􀀍􀀐􀀂􀀎􀀜􀀛􀀇􀀍􀀐􀀄􀀐􀀗􀀐􀀄􀀇􀀈 􀀐h􀀂 􀀜􀀆􀀐􀀆􀀝y􀀐􀀄􀀜 􀀍y􀀍􀀐􀀂􀀎 . 􀀅h􀀂 􀀞􀀕􀀛􀀘􀀝􀀂􀀎 􀀜􀀆􀀇 􀀈􀀕􀀛􀀒 􀀂 􀀂􀀇􀀎􀀛􀀕􀀂 􀀜􀀛􀀎􀀞􀀝􀀂x 􀀒h􀀂􀀇 􀀐h􀀂 􀀂ff􀀂􀀜􀀐 􀀛f 􀀂x􀀞􀀝􀀄􀀜􀀄􀀐 􀀍􀀛􀀝 􀀂􀀇􀀐 􀀇􀀂􀀂􀀓􀀍 􀀐􀀛 􀀘􀀂􀀜􀀛􀀇􀀍􀀄􀀓􀀂􀀕􀀂􀀓􀀉 􀀛􀀕 􀀒h􀀂􀀇 􀀐h􀀂 􀀍􀀄z􀀂-􀀓􀀂􀀞􀀂􀀇􀀓􀀂􀀇􀀐 􀀞􀀕􀀛􀀞􀀂􀀕􀀐􀀄􀀂􀀍 􀀛f 􀀇􀀆􀀇􀀛 -􀀞􀀆􀀕􀀐􀀄􀀜􀀝􀀂 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍 􀀇􀀂􀀂􀀓 􀀐􀀛 􀀘􀀂 􀀎􀀛􀀓􀀂􀀝􀀂􀀓􀀉 􀀒h􀀄􀀜h 􀀜􀀆􀀇 􀀎􀀆􀀑􀀂 􀀓􀀂􀀇􀀍􀀄􀀐y f􀀗􀀇􀀜􀀐􀀄􀀛􀀇􀀆􀀝 􀀐h􀀂􀀛􀀕y (􀀁F􀀅)-􀀘􀀆􀀍􀀂􀀓 􀀍􀀄􀀎􀀗􀀝􀀆􀀐􀀄􀀛􀀇􀀍 􀀄􀀇􀀐􀀕􀀆􀀜􀀐􀀆􀀘􀀝􀀂 . 􀀊2􀀋 􀀚􀀍 􀀆􀀕􀀂􀀍􀀗􀀝􀀐􀀉 􀀒􀀂 h􀀆 􀀂 􀀍􀀂􀀂􀀇 􀀕􀀂􀀝􀀆􀀐􀀄 􀀂􀀝y 􀀎􀀆􀀇y 􀀆􀀞􀀞􀀝􀀄􀀜􀀆􀀐􀀄􀀛􀀇􀀍 􀀛f M􀀖IP􀀍 􀀄􀀇􀀜􀀆􀀐􀀆􀀝y􀀍􀀄􀀍 􀀕􀀂􀀍􀀂􀀆􀀕􀀜h􀀉 􀀂. 􀀈 .􀀉 f􀀛􀀕 􀀍􀀐􀀗􀀓y 􀀛f 􀀆􀀓􀀍􀀛􀀕􀀞􀀐􀀄􀀛􀀇 􀀞􀀕􀀛􀀞􀀂􀀕􀀐􀀄􀀂􀀍􀀉􀀍􀀐􀀕􀀗􀀜􀀐􀀗􀀕􀀂 􀀞􀀕􀀂􀀓􀀄􀀜􀀐􀀄􀀛􀀇􀀉 􀀆􀀇􀀓 􀀓y􀀇􀀆􀀎􀀄􀀜􀀍 􀀛f 􀀜􀀆􀀐􀀆􀀝y􀀍􀀐􀀍 . 􀀊3–5􀀋  \n􀀅h􀀂 􀀞􀀆􀀍􀀐 􀀐􀀒􀀛 􀀓􀀂􀀜􀀆􀀓􀀂􀀍 h􀀆 􀀂 􀀒􀀄􀀐􀀇􀀂􀀍􀀍􀀂􀀓 h􀀗􀀈􀀂 􀀆􀀓 􀀆􀀇􀀜􀀂􀀎􀀂􀀇􀀐􀀍􀀄􀀇 􀀐h􀀂 f􀀄􀀂􀀝􀀓 􀀛f M􀀖IP􀀍􀀉 􀀄􀀇 􀀐􀀂􀀕􀀎􀀍 􀀛f 􀀐h􀀂 􀀓􀀂 􀀂􀀝􀀛􀀞􀀎􀀂􀀇􀀐 􀀛f  \n􀀊􀀆􀀋 􀀇􀀈 􀀉􀀁􀀄􀀊􀀋 􀀌􀀈 􀀍􀀎􀀅􀀏􀀁􀀎􀀐􀀋 􀀑􀀈 􀀒􀀓􀀂􀀎􀀔  \n􀀇􀀎􀀕􀀁􀀔􀀅􀀖􀀎􀀄􀀅 􀀆􀀗 􀀘􀀙􀀎􀀖􀀃􀀚􀀅􀀔􀀛􀀋 􀀜􀀄􀀃􀀝􀀎􀀔􀀚􀀃􀀅􀀛 􀀆􀀗 􀀞􀀓􀀔􀀃 􀀙􀀋  \n􀀞􀀓􀀔􀀃 􀀙􀀋 􀀑􀀐􀀃􀀅z􀀎􀀔l􀀁􀀄d  \nE-􀀖􀀁􀀃l: 􀀚􀀁􀀄d􀀔􀀁􀀈l􀀓􀀂􀀎􀀔@ 􀀙􀀎􀀖􀀈􀀓z􀀙􀀈 􀀙  \n © 2024 􀀉􀀙􀀎 A􀀓􀀅􀀙􀀆􀀔(􀀚)􀀈 􀀘􀀙􀀎􀀖􀀃􀀚􀀅􀀔􀀛 - A E􀀓􀀔􀀆􀀕􀀎􀀁􀀄 J􀀆􀀓􀀔􀀄􀀁l 􀀕􀀓􀀂l􀀃􀀚􀀙􀀎d 􀀂􀀛 W􀀃l􀀎􀀛 -V􀀘H ","cbCaibEdtoFiczVr","https://ap.wps.com/l/cbCaibEdtoFiczVr","pdf",8987647,1,18,"English","en",105,"# Overview\n## Interatomic potential modeling for catalysis\n## Data, training setup, and evaluation\n## Model architectures and comparisons\n## Performance analysis and conclusions","[{\"question\":\"What is the main topic of the work?\",\"answer\":\"The work focuses on building machine learning interatomic potentials for heterogeneous catalysis.\"},{\"question\":\"How are the interatomic potentials evaluated?\",\"answer\":\"Evaluation is performed using benchmark datasets and comparisons of predicted quantities such as energies, forces, and related structural metrics against reference data.\"},{\"question\":\"Which kinds of models or neural components are involved?\",\"answer\":\"The document discusses different neural potential formulations/components (including M(C)IP and related network approaches) and compares their performance for representing atomic interactions.\"}]","Machine Learning Interatomic Potentials for Heterogeneous Catalysis | 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