University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Combining scalar and vector learning to predict molecular dipole moments

Combining scalar and vector learning to predict molecular dipole moments

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The dipole moment of a molecule is vital in understanding its interaction with other molecules and with light (and thus its spectroscopic properties). Despite its importance, an accurate quantum-chemical calculation can be computationally expensive, and sensitive to the computational details. I describe a method for learning molecular dipole moments by combining two machine-learning methods that each represent a different type of physical effect: scalar Gaussian Process Regression (GPR), which learns partial atomic charges and thus resembles charge separation, and vector GPR , which learns partial atomic dipoles and resembles atomic polarization. While these two methods have their own advantages and disadvantages, combining the two gives rise to a single method with the best features of both, opening up the way to accurate molecular simulations and calculations of spectra.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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