Student-Faculty Programs Office
Summer 2017 Announcements of Opportunity

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Project:  Towards Low-Cost Electronic Structure: Machine-Learning Strategies for Extending Fock-Corrected Density Functional Theory
Disciplines:  Chemistry, Computer Science
Mentor:  Thomas Miller, Professor of Chemistry, (CCE), tfm@caltech.edu
Background:  There is an ever-growing demand for inexpensive and accurate density functional theory (DFT) calculations in the study of a variety of complex chemical systems. New methods are needed to improve the balance between the cost of a DFT calculation and its accuracy. The Miller group has recently developed the Fock-corrected density-functional theory, a semi-empirical DFT method, to attain improvement in accuracy without losing the affordable cost of using minimal-basis DFT [J. Chem. Theory Comput., 12, 5811 (2016).]. However, the method is currently limited to a small number of elements (i.e., carbon and hydrogen). Working with a summer researcher, we propose to employ neural networks and other machine learning strategies to further test the FC-DFT approach and to efficiently parameterize FC-DFT for a greater range of elements.
Description:  1. Become familiar with mean-field electronic structure methods, such as DFT
2. Become familiar with the use of neural-network machine-learning algorithms
3. Write code for FC-DFT parameterization using machine-learning algorithms
References:  1. http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00685
2. http://ciml.info
Student Requirements:  1. CH 1, MA 1, PH 1, CS 1
2. Extensive programming experience
3. Interest in chemistry, computation, and data science
Programs:  This AO can be done under the following programs:

  Program    Available To
       Amgen Scholars    Non-Caltech students only  

Click on a program name for program info and application requirements.


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Problems with or questions about submitting an AO?  Call Jen Manglos of the Student-Faculty Programs Office at (626) 395-2885.
 
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