Project Grant R44GM133270
- Quantum Simulation Technologies Inc., doing business as Qsimulate, received a $442,362 Project Grant from the National Institutes of Health's National Institute of General Medical Sciences to develop and validate a novel high-performance implementation of density functional theory quantum mechanics for scoring ligand-protein interactions. The award was made under the Biomedical Research and Research Training program (CFDA 93.859), which supports basic research that increases understanding of...
- Quantum Simulation Technologies Inc., doing business as Qsimulate, was awarded a $250,000 Project Grant from the Department of Energy Office of Science on February 14, 2022 to develop cloud-based low-scaling quantum chemistry simulations for materials. The grant is being carried out under the Office of Science Financial Assistance Program (CFDA #81.049), which aims to deliver scientific discoveries and tools to transform understanding of nature and advance U.S. energy and economic security....
- This three-year, $450,000 Project Grant from the National Science Foundation's Division of Chemistry and Mathematical and Physical Sciences program will support the development of new computational methods for simulating complex molecular and materials systems. Professor Nicholas Jackson of the University of Illinois, Urbana-Champaign will establish a paradigm for scalable quantum chemical predictions using electronic structure models that operate on coarse-grained molecular representations....
- The $249,416 Project Grant from the Department of Energy Office of Science, under the Office of Science Financial Assistance Program (CFDA #81.049), will fund research into new theories and ontologies for quantum mechanical cluster modeling of proteins and enzymes. The awardee, Q-Chem Inc. located in Pleasanton, California, will utilize the funding from February 22, 2021 through April 1, 2022 to develop computational software and technical reports related to modeling biochemical reactions at the...
- This $300,000 EAGER award from the National Science Foundation's (NSF) Division of Chemistry, under the Mathematical and Physical Sciences (CFDA 47.049) program, supports the development of quantum-inspired electronic structure theory methods by Virginia Polytechnic Institute & State University (Virginia Tech). The project aims to leverage ideas from quantum algorithms to create novel classical algorithms for accelerating chemistry simulations on current classical computers. The key...
- The National Science Foundation awarded a $232,670 Project Grant to the University of Hawaii at Manoa under the Integrative Activities federal grant program (CFDA 47.083) to develop an active machine learning protocol for accelerating ab initio molecular dynamics simulations of chemical reactions. Principal Investigator Rui Sun and their research group will design a machine learning algorithm using interpolated moving ridge regression trained on data from previous ab initio energy gradient...
- This $499,999 National Science Foundation project grant in the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of new quantum chemistry algorithms by Professor David Mazziotti and his research group at the University of Chicago from September 2022 through August 2025. The grant aims to enable applications to more complex chemical systems through exploring reduced density matrix theory in combination with recent advances in quantum computing, machine learning,...
- This $450,000 collaborative research project grant, awarded April 1, 2026, through the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049), supports the development of hardware-specific quantum algorithms for path integral-based quantum molecular simulation. Led by investigators at New York University (Mark Tuckerman and Norah Hoffmann) and Cornell University (Nandini Ananth), the project will create novel quantum computing algorithms capable of predicting...
- This $600,000 Project Grant from the National Science Foundation's Division of Chemistry, under the Mathematical and Physical Sciences program (CFDA 47.049), will support research into practical strategies for implementing quantum chemistry on near-term quantum computers. James Freericks of Georgetown University and Dominika Zgid of the University of Michigan will collaborate to develop hybrid quantum-classical methodologies to run important parts of calculations on noisy intermediate-scale...
- This collaborative research project, funded by the National Science Foundation (NSF) Division of Chemistry under the Mathematical and Physical Sciences program (CFDA 47.049), supports the development of computational modeling tools to advance catalytic chemistry research. Led by Professor Hibbitts at Purdue University and Professor Plaisance at Louisiana State University, the project creates an implicit adlayer model using density functional theory to study chemical reactions on catalyst...
MULTISCALE AB INITIO QM/MM AND MACHINE LEARNING METHODS FOR ACCELERATED FREE ENERGY SIMULATIONS - Q-CHEM IS A STATE-OF-THE-ART COMMERCIAL COMPUTATIONAL QUANTUM CHEMISTRY SOFTWARE PROGRAM THAT HAS AIDED ABOUT 60,000 USERS IN THEIR MODELING OF MOLECULAR PROCESSES IN A WIDE RANGE OF DISCIPLINES, INCLUDING BIOLOGY, CHEMISTRY, AND MATERIALS SCIENCE. IN THIS PROPOSAL, WE SEEK TO SIGNIFICANTLY REDUCE THE COMPUTATIONAL TIME (NOW AROUND 500,000 CPU HOURS) REQUIRED TO OBTAIN ACCURATE FREE ENERGY PROFILES OF ENZYMATIC REACTIONS. SPECIFICALLY, WE PROPOSE TO USE A MULTIPLE TIME STEP (MTS) SIMULATION METHOD, WHERE A LOW-LEVEL (AND LESS ACCURATE) QUANTUM CHEMISTRY OR MACHINE LEARNING MODEL IS USED TO PROPAGATE THE SYSTEM (I.E. MOVE ALL ATOMS) AT EACH TIME STEP (USUALLY 0.5 OR 1 FS), AND THEN A HIGH-LEVEL (I.E. MORE ACCURATE AND EXPENSIVE) QUANTUM CHEMISTRY METHOD IS USED TO CORRECT THE FORCE ON THE ATOMS AT LONGER TIME INTERVALS. IN THIS WAY, THE SIMULATION CAN BE PERFORMED AT THE HIGH- LEVEL ENERGY SURFACE IN A FRACTION OF TIME, COMPARED WITH SIMULATIONS PERFORMED ONLY USING THE HIGH-LEVEL QUANTUM CHEMICAL METHOD. IN THE PHASE I PROPOSAL, WE SUCCESSFULLY RE-PARAMETERIZED LOW-LEVEL QUANTUM CHEMISTRY MODELS AND DEVELOPED MACHINE LEARNING MODELS FOR MTS SIMULATIONS. THROUGH THESE DEVELOPMENTS, WE WERE ABLE TO EXTEND THE HIGH-LEVEL FORCE UPDATE TO ONLY ONCE EVERY 8 FS OR LONGER. IN THE PHASE II PERIOD, WE WILL FURTHER IMPROVE AND AUTOMATE THE WORKFLOW FOR DEVELOPING THE LOW-COST MODELS, WHICH WILL FURTHER ENHANCE THE COMPUTATIONAL EFFICIENCY OF OUR MTS SIMULATIONS. IN ADDITION, THESE ADVANCES WILL BE COMBINED BY THE ENZYDOCK METHOD TO FACILITATE THE STUDY OF MULTI-STEP ENZYME REACTIONS AND THE DESIGN OF COVALENT/NONCOVALENT INHIBITORS AND MUTANT ENZYMES. THE ADDITION OF THESE NEW TOOLS WILL ALSO FURTHER STRENGTHEN Q-CHEM'S POSITION AS A GLOBAL LEADER IN THE MOLECULAR MODELING SOFTWARE MARKET, MAKING OUR PROGRAM THE MOST EFFICIENT AND RELIABLE COMPUTATIONAL QUANTUM CHEMISTRY PACKAGE FOR SIMULATING LARGE, COMPLEX CHEMICAL/BIOLOGICAL SYSTEMS.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $660.2k | 4/5/24 | ||
| Not listed | $57.4k | 11/20/23 | ||
| Not listed | $57.4k | 11/20/23 | ||
| Not listed | $620.6k | 3/14/23 | ||
| Not listed | $620.6k | 3/14/23 |