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Journal of Computational Chemistry

Volumn 34, Issue 19, 2013, Pages 1661-1671

Fast and accurate generation of ab initio quality atomic charges using nonparametric statistical regression

(2) Rai, Brajesh K a Bakken, Gregory A a

a PFIZER INC (United States)

Author keywords

dipole moment; electrostatic potential; hydration energy; machine learning; partial charges; random forest

Indexed keywords

AB INITIO CALCULATIONS; ELECTROSTATIC POTENTIALS; GENERALIZED BORN SOLVATIONS; HYDRATION ENERGIES; HYDRATION FREE ENERGIES; PARTIAL CHARGES; RANDOM FORESTS; SMALL ORGANIC MOLECULES;

CALCULATIONS; CHEMICAL PROPERTIES; CHLORINE; DECISION TREES; DIPOLE MOMENT; ELECTRONIC STRUCTURE; ELECTROSTATICS; FLUORINE; LEARNING SYSTEMS; MOLECULES; SYSTEMATIC ERRORS;

C (PROGRAMMING LANGUAGE);

ELEMENT; WATER;

ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; QUANTUM THEORY; REGRESSION ANALYSIS; STATIC ELECTRICITY; THERMODYNAMICS;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ELEMENTS; MODELS, CHEMICAL; MODELS, MOLECULAR; QUANTUM THEORY; REGRESSION ANALYSIS; STATIC ELECTRICITY; THERMODYNAMICS; WATER;

EID: 84879104663 PISSN: 01928651 EISSN: 1096987X Source Type: Journal
DOI: 10.1002/jcc.23308 Document Type: Article

Times cited : (50)

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