Quantum-chemical insights from deep tensor neural networks
Schütt, Kristof T., Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R., Tkatchenko, Alexandre·2017·Nature Communications
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- Machine Learning Configuration InteractionJ. P. Coe · 2018 · Journal of Chemical Theory and Computation
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