Abstracts Book - IMRC 2018

• SD1-O014 Invited Talk
A QUANTUM CHEMICAL LAYER FOR DEEP LEARNING OF
ELECTRONIC PROPERTIES
David J Yaron 1 , Haichen Li 1 , Christopher R Collins 1
1 Carnegie Mellon University, Department of Chemistry, United States.
The high computational cost of ab initio electronic structure calculations
remains a challenge for computational design of molecules and materials. Semiempirical
models, such as Density Functional Tight Binding (DFTB), can compute
electronic structure at a greatly reduced cost. However, the accuracy of such
models is insufficient for many applications. We will present a means to
systematically improve the accuracy of such models while maintaining their low
computational cost. The key enabling technology is the implementation of DFTB
as a layer that can be integrated into deep learning models of machine learning.
This layer takes, an input, DFTB parameters generated by a standard deep
learning network. The layer generates, as output, electronic properties selfconsistent
solutions of the DFTB model. The quantum chemical layer allows
backpropagation, such that the system can be trained efficiently to data on
electronic
properties.
We will present results on use of this layer to predict electronic properties of
organic molecules and other molecular systems. A challenge with the
development of such models is the highly flexible nature of the model form. This
flexibility can lead to over training, whereby the model performs well on the
systems used to train the model but does not transfer well to other systems. We
will present a number of regularization strategies that reduce the model
flexibility by requiring the DFTB parameters to behave in physically reasonable
ways. For example, constraining electronic couplings to decrease monotonically
with distance improves transfer smaller to larger molecular systems. Such
regularizations allow models to be developed that apply to large classes of
molecular systems.
Keywords: Electronic structure theory, machine learning, density functional tight
binding
Presenting authors email: yaron@cmu.edu