Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning
作者： Timothy L. Fletcher , Paul L. A. Popelier
关键词: Mean squared error 、 Amino acid 、 Kriging 、 Multipole expansion 、 Electric potential energy 、 Electrostatics 、 Physics 、 Machine learning 、 Artificial intelligence 、 Basis (linear algebra) 、 Polarizability
摘要: A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all …
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