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Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0017710
Agency Tracking Number: 0000240900
Amount: $1,000,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 08b
Solicitation Number: DE-FOA-0001795
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-27
Award End Date (Contract End Date): 2020-07-15
Small Business Information
738 Arrow Grand Circle
Covina, CA 91722-2147
United States
DUNS: 002559065
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Chengyang Jiang
 (626) 858-5758
Business Contact
 Shaolih Hsu
Phone: (626) 858-5758
Research Institution
 California Institute of Technology
1200 East California Boulevard, Mail Code 273-6 1200 East California Boulevard, Mail Code 273-6
Pasadena, CA 91125-0000
United States

 () -
 Nonprofit College or University

This Small Business Technology Transfer (STTR) program aims at development and demonstration of an integrated theoretically/experimentally combinatorial method for the accurate prediction of rheological behaviors of special polymer solutions. In-depth understandings of dynamic responses of the polymer solutions under external shear are essential for development of the “smart” polymer-based additive (SPA) flooding as advanced enhanced oil recovery (EOR) processes. However, conventional coarse-graining molecular dynamic simulations are not useful for Polymer Discovery because they lack the chemical and thermodynamic degrees of freedom needed to describe dynamic effects from hydrophobicity, polarization, transient telechelic linking, and temperature and pressure fluctuations, among others. Scientists from SGTC and CALTECH will team up together to apply advanced multi-scale multi-paradigm simulation methods recently developed by the CALTECH’s group to develop the robust molecular predictive methods and demonstrate the applicability for SPA-EOR applications. In particular, throughout this program, we will develop accurate first-principles tuned coarse-grain molecular modeling and machine learning methods to improve our fundamental understandings and the molecular in-silico screening to obtain best performance polymers. We propose a strongly integrated theoretical/experimental project to develop and demonstrate scientific feasibility of the efficient yet accurate molecular predictive method to simulate high-molecular weight, water-soluble functionalized polymeric solution. Our theoretical programs includes state-of-the-art multi-scale molecular dynamic simulation methods that retains the accuracy of Quantum Mechanism methods for reactive systems, yet at the scale of millions of atoms and over time scale of milliseconds and longer, and machine learning methods that use the chemometrical, compositional and conformational information obtained from by our dynamics methods, to reduce the search space for ‘smart’ polymeric solutions with optimum performance (e.g. shear-thickening and thinning in EOR applications). Our experimental programs include laboratorial synthesis, rheological property characterization, and unique multi-core flooding tests of the special polymer candidates. Commercial Applications and Other Benefits: Special polymeric materials have extensive applications in building, coating, plastics, membranes, aerospace, automotive, electronics, packaging and medical devices, etc. Although this proposed project has been focused mainly on energy production applications, the success of our proposed robust molecular predictive methods for high throughput screening and molecular designs, could have much broad impacts in the Materials Discovery.

* Information listed above is at the time of submission. *

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