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Machine learning takes materials modeling into new era - Deep learning approach enables accurate electronic structure calculations at large scales

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack o .

Lenz fiedlerAttila cangiSiva rajamanickamNvidia corpCarolinaat state universityDepartment head of matter under extreme conditionsGeorgia institute of technologySambanova systems incSandia national laboratoriesMaterials learning algorithmsActing department headExtreme conditionsGeorgia instituteNorth carolinaComput mater

Deep Learning Revolutionizes Electronic Structure Calculations

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies.

United statesNew mexicoAttila cangiSiva rajamanickamLaura thomsonjulLenz fiedlerGeorgia institute of technologySemiconductors industrySambanova systems incSandia national laboratoriesCarolinaat state universityNvidia corpDepartment head of matter under extreme conditionsSandia national laboratories in albuquerqueAdvanced systems understandingHelmholtz zentrum dresden rossendorf

Machine Learning Takes Materials Modeling Into New Era

Machine Learning Takes Materials Modeling Into New Era
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New mexicoUnited statesLenz fiedlerAttila cangiSiva rajamanickamNvidia corpGeorgia institute of technologyCarolinaat state universitySandia national laboratories in albuquerqueDepartment head of matter under extreme conditionsSambanova systems incSandia national laboratoriesAdvanced systems understandingHelmholtz zentrum dresden rossendorfComputational materialsMaterials learning algorithms

Machine learning takes materials modeling into new era - Helmholtz-Zentrum Dresden-Rossendorf, HZDR

The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies. Researchers from the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, USA, have now pioneered a machine learning-based simulation method (npj Computational Materials, DOI: 10.1038/s41524-023-01070-z) that supersedes traditional electronic structure simulation techniques. Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales.

United statesNew mexicoAttila cangiLenz fiedlerMartin laquaSiva rajamanickamSandia national laboratories in albuquerqueGeorgia institute of technologyDepartment head of matter under extreme conditionsSambanova systems incCarolinaat state universityNvidia corpAttila cangi acting department headSandia national laboratoriesAdvanced systems understandingHelmholtz zentrum dresden rossendorf

Machine learning enables accurate electronic structure calculations at large scales for material modeling

Machine learning enables accurate electronic structure calculations at large scales for material modeling
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New mexicoUnited statesSiva rajamanickamLenz fiedlerAttila cangiDepartment head of matter under extreme conditionsSandia national laboratoriesSandia national laboratories in albuquerqueAdvanced systems understandingHelmholtz zentrum dresden rossendorfMaterials learning algorithmsActing department headExtreme conditionsComputational materials