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Parallel Two-Electron Reduced Density Matrix Based Electronic Structure Software for Highly Correlated Molecules and Materials

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-19-C-0048
Agency Tracking Number: A2-7741
Amount: $996,269.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: A14A-T013
Solicitation Number: 14.A
Solicitation Year: 2014
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-13
Award End Date (Contract End Date): 2021-08-12
Small Business Information
6601 Owens Drive Suite 105
Pleasanton, CA 94588
United States
DUNS: 837635556
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Eugene Deprince
 Associate Professor
 (850) 645-1291
Business Contact
 Hilary Pople
Phone: (773) 343-6072
Research Institution
 Florida State University
 Gary Ostrander Gary Ostrander
Sponsored Research Administration 874 Traditions Way, Third Floor
Tallahassee, FL 32306
United States

 (850) 644-1464
 Nonprofit College or University

The variational two-electron reduced-density matrix (v2RDM) method provides an effective framework for computer modeling of the electronic structure of complex molecules and materials that exhibit strong correlation effects. In Phase I and Phase II of this STTR we have demonstrated that the GPU-accelerated implementation of the v2RDM-based complete active space self-consistent field (v2RDM-CASSCF) method can treat problems as large as 64 electrons in 64 orbitals on a single workstation, which exceeds by a large margin the capabilities of alternative state-of-the art CASSCF implementations. In this Sequential Phase 2 project, we propose to build upon our success and extend the v2RDM methodology to alternative descriptions of strong electron correlation such as the v2RDM-driven doubly occupied configuration interaction (DOCI) approach, as well as to the treatment of dynamical correlation effects via techniques such as the adiabatic connection (AC) and multiconfigurational pair-density functional theory (MCPDFT). In addition, we propose a number of algorithmic improvements that aim to significantly improve the efficiency of the code.

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