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Coupled cluster finite temperature simulations of periodic materials via machine learning

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摘要 Density functional theory is the workhorse of materials simulations.Unfortunately,the quality of results often varies depending on the specific choice of the exchange-correlation functional,which significantly limits the predictive power of this approach.Coupled cluster theory,including single,double,and perturbative triple particle-hole excitation operators,is widely considered the‘gold standard'of quantum chemistry as it can achieve chemical accuracy for non-strongly correlated applications.Because of the high computational cost,the application of coupled cluster theory in materials simulations is rare,and this is particularly true if finite-temperature properties are of interest for which molecular dynamics simulations have to be performed.
出处 《npj Computational Materials》 CSCD 2024年第1期2522-2528,共7页 计算材料学(英文)
基金 supported through the COMETE project(COnception in silico de Matériaux pour l’EnvironnemenT et l’Energie)co-funded by the European Union under the program“FEDER-FSE Lorraine et Massif des Vosges 2014-2020” B.H.,S.L.,and D.R.acknowledge the financial support of the Agence Nationale de la Recherche under the Lorraine Artificial Intelligence(LOR-AI)project(grant number ANR-20-THIA-0010-01) This work was granted access to the HPC resources of TGCC under the allocations 2022-A0120810433 and 2023-A0140810433 by GENCI T.B.acknowledges support from the Slovak Research and Development Agency under Contract No.APVV-20-0127 the grant VEGA 1/0254/24 from the Ministry of Education Research,Development and Youth of the Slovak Republic support and funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(Grant Agreement No.715594).
关键词 CLUSTER FINITE PERIODIC
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