[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118896-en":3,"doc-seo-118896-105":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},118896,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Accelerating Electronic Structure Calculations with Machine Learning - Dissertation","New materials underpin modern life, yet discovering them typically requires costly, physics-based atomic-scale simulations. Electronic structure calculations are central to this process but are computationally expensive, motivating long-standing speed–accuracy approximations. Machine learning offers a path to accelerate materials discovery by improving the speed–accuracy trade-off. This dissertation introduces quantum-mechanical foundations, surveys learning-based and non-learning methods, and develops self-supervised learning to capture potential energy surface structure without expensive labeled energies and forces.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nAccelerating Electronic Structure Calculations with Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/6qz4z32w](https://escholarship.org/uc/item/6qz4z32w)  \nAuthor  \nRothchild, Daniel  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAccelerating Electronic Structure Calculations with Machine Learning  \nBy  \nDaniel Rothchild  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nComputer Science  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nAssociate Professor Joseph Gonzalez, Chair Professor Ion Stoica Assistant Professor Aditi Krishnapriyan  \nSummer 2023  \nAccelerating Electronic Structure Calculations with Machine Learning  \nCopyright 2023  \nby  \nDaniel Rothchild  \n1  \nAbstract  \nAccelerating Electronic Structure Calculations with Machine Learning  \nby  \nDaniel Rothchild  \nDoctor of Philosophy in Computer Science  \nUniversity of California, Berkeley  \nAssociate Professor Joseph Gonzalez, Chair  \nNew chemicals and new materials have transformed modern life: pharmaceuticals, pesticides, surfactants, alloys, catalysts, polymers, battery electrodes, and countless other materials play critical roles in healthcare, construction, energy, and other wide-ranging industries. New materials are not generally stumbled upon by happenstance, but rather are discovered through a long process that involves extensive physics-based computer simulations at the atomic level. Electronic structure calculations play an important role in the discovery process, but they can be extremely computationally expensive. As such, there is a long history of approximation methods that trade off speed and accuracy.  \nMachine learning has the potential to open a new frontier on this speed-accuracy trade-off, and in doing so, significantly accelerate discovery of new materials. In this dissertation, we first cover the quantum mechanical background necessary to understand the problem setting, written with the machine learning community in mind as the audience. Next, we survey the learning-based methods that are pushing the speed-accuracy frontier, along with some foundational non-learning-based methods. Lastly, we investigate self-supervised learning asa mechanism for understanding the shape of the potential energy surface without expensiveto-obtain supervision on energies and forces.  \ni  \nTo My Mother, Who I’m Pretty Sure Thinks There’s Only a 60% Chance I Actually  \nSubmit This  \nii  \nContents  \nContents ii  \nList of Figures iv  \nList of Tables vi  \n1 Quantum Mechanics Background 1  \n1.1 Electrons ...................................... 1  \nWavefunctions ................................... 1  \nMeasurement ................................... 3  \nTime Evolution .................................. 5  \nExample: Harmonic Oscillator .......................... 6  \nExample: Hydrogen Atom ............................ 7  \nSpin ........................................ 8  \n1.2 Approximation Methods ............................. 9  \nMultiple Electrons and Antisymmetry ...................... 9  \nHartree-Fock ................................... 10  \nDensity Functional Theory ............................ 12  \n2 Machine Learning Background 14  \n2.1 Interatomic Potentials .............................. 14  \nForce Fields .................................... 14  \nLocal Descriptor Methods ............................ 16  \nNeural Network Interatomic Potentials (NNIPs) ................ 17  \nEquivariant Neural-Network Interatomic Potentials .............. 19  \n2.2 Generative Modeling ............................... 20  \nProblem Setting .................................. 20  \nDenoising Diffusion Models ............................ 20  \n3 Self-Supervised PES Learning ","cbCaieGKOY8o9ISF","https://ap.wps.com/l/cbCaieGKOY8o9ISF","pdf",8467835,1,54,"English","en",105,"# Abstract\n# Quantum Mechanics Background\n## Electrons\n## Approximation Methods\n# Machine Learning Background\n## Interatomic Potentials\n## Generative Modeling\n# Self-Supervised PES Learning\n## Introduction\n## Physical Intuition Behind EDM\n## EDM as Boltzmann Generator\n# Conclusion\n# Bibliography","[{\"question\":\"Why are electronic structure calculations computationally expensive in materials discovery?\",\"answer\":\"They require detailed atomic-level physics simulations, and achieving accurate results often demands significant computation time and resources.\"},{\"question\":\"How does machine learning aim to improve the speed–accuracy trade-off?\",\"answer\":\"Machine learning-based methods can approximate or emulate expensive electronic-structure computations more efficiently, enabling faster exploration while maintaining useful accuracy.\"},{\"question\":\"What is the role of self-supervised learning in this dissertation?\",\"answer\":\"Self-supervised learning is investigated as a way to understand the shape of the potential energy surface without expensive supervision on energies and forces.\"}]","Accelerating Electronic Structure Calculations with Machine Learning - Dissertation | PDF",1785720834,136,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"accelerating-electronic-structure-calculations-with-machine-learning-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accelerating-electronic-structure-calculations-with-machine-learning-dissertation/118896/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are electronic structure calculations computationally expensive in materials discovery?","Question",{"text":75,"@type":76},"They require detailed atomic-level physics simulations, and achieving accurate results often demands significant computation time and resources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning aim to improve the speed–accuracy trade-off?",{"text":80,"@type":76},"Machine learning-based methods can approximate or emulate expensive electronic-structure computations more efficiently, enabling faster exploration while maintaining useful accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of self-supervised learning in this dissertation?",{"text":84,"@type":76},"Self-supervised learning is investigated as a way to understand the shape of the potential energy surface without expensive supervision on energies and forces.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]