Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes

In this work, we present a machine learning (ML) approach for predicting the optimal range separation parameters in transition metal complexes (TMCs), aiming to reduce the computational cost associated with optimally tuned range-separated hybrid (OT-RSH) functionals while preserving their accuracy....

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Hlavní autor:	Xiang-Yang Liu (1408399) (author)
Další autoři:	Qing-Xin Xiang (22210810) (author), Sheng-Rui Wang (18436159) (author), Dongyi Xiao (22185291) (author), Wei-Hai Fang (1268982) (author), Ganglong Cui (1329708) (author)
Vydáno:	2025
Témata:	Biophysics Medicine Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified transition metal complexes reference values confirm reduced computational cost optimally tuned range computational cost associated based optoelectronic materials multiple regression models electronic structure prediction tmcs ), aiming machine learning parameters fully tuned lc support vector machine separated hybrid functionals separated hybrid machine learning resulting machine prediction model dimensional multiple tmqm database tmc represented model yielded learned functional fingerprint feature best performance advanced tmc accurate framework 62 087
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Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes

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