Google Accelerated Science team
Including prior knowledgeis important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely draws attention. We show that solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides animplicit regularization that greatly improves generalization. Two separations suffice for learning the entire one-dimensional H2 dissociation curve within chemical accuracy, including the strongly correlated region. Our models also generalize to unseen types of molecules and overcome self-interaction error.
Brief CV of Dr. Li Li:
Dr. Li Li has been a researcher at the Google Accelerated Science team since 2017. His research interests include machine learning and its application in physics, quantum chemistry and material sciences. He received his Ph.D. from University of California, Irvine in 2016, working with Professor Kieron Burke on machine learning approximation in density functional theory.
Contact: Lei Wang（王磊）
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