Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discover...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Akksay Singh (20176616) (author)
مؤلفون آخرون:	Jiaqi Wang (369548) (author), Graeme Henkelman (1287513) (author), Lei Li (29537) (author)
منشور في:	2024
الموضوعات:	Biophysics Medicine Neuroscience Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified later improving stability important tool used hessian matrix calculated density functional theory dft force calls accelerated computational modeling optimizer effectively reduces based neural network dft hybrid framework neural network hybrid optimizer computational simulations computational efficiency uncertainty estimation test systems results demonstrate reliable algorithms oxide cluster metal surface metal hydride materials discovery material science geometry optimization enhance efficiency delta method
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Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization

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