### Brian Barch and Norman Tubman

###
EECS Department

University of California, Berkeley

Technical Report No. UCB/EECS-2017-104

May 12, 2017

### http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-104.pdf

In this paper, we train a neural network on atomic configurations to predict energy as a function of atom position, then use this neural network to perform optimization to solve for minimum energy atomic configuration. This is a problem of interest because it could potentially provide a boost to both accuracy and speed over traditional numeric methods of solving for the structure of molecules. Previous papers have shown that neural networks trained on the results of numerical simulations can reproduce those results to high accuracy. We construct one such neural network and experiment with new methods of optimizing its parameters, then use it as a function to optimize in order to find the minimum energy configuration for a systems of a few homonuclear atoms. The results are promising, with our neural network accuracy beating that of the baseline neural network for the problem, and the minimization results showing an improvement over the data the neural network was trained on.

BibTeX citation:

@mastersthesis{Barch:EECS-2017-104, Author = {Barch, Brian and Tubman, Norman}, Title = {Solving Minimum Energy Structures with Neural Networks}, School = {EECS Department, University of California, Berkeley}, Year = {2017}, Month = {May}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-104.html}, Number = {UCB/EECS-2017-104}, Abstract = {In this paper, we train a neural network on atomic configurations to predict energy as a function of atom position, then use this neural network to perform optimization to solve for minimum energy atomic configuration. This is a problem of interest because it could potentially provide a boost to both accuracy and speed over traditional numeric methods of solving for the structure of molecules. Previous papers have shown that neural networks trained on the results of numerical simulations can reproduce those results to high accuracy. We construct one such neural network and experiment with new methods of optimizing its parameters, then use it as a function to optimize in order to find the minimum energy configuration for a systems of a few homonuclear atoms. The results are promising, with our neural network accuracy beating that of the baseline neural network for the problem, and the minimization results showing an improvement over the data the neural network was trained on.} }

EndNote citation:

%0 Thesis %A Barch, Brian %A Tubman, Norman %T Solving Minimum Energy Structures with Neural Networks %I EECS Department, University of California, Berkeley %D 2017 %8 May 12 %@ UCB/EECS-2017-104 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-104.html %F Barch:EECS-2017-104