Project Grant 2143346
- This $320,117 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support the development of machine learning (ML) tools to rapidly predict the attractive forces between desired molecules and candidate adsorbent surfaces. The aim is to enable more efficient computational screening and identification of optimal adsorbents for industrial separation processes involving chemisorption. The research will be conducted by the Colorado School of...
- This National Science Foundation (NSF) Project Grant under the Engineering program (CFDA 47.041) supports a $337,663 research project at the University of Oklahoma from August 1, 2024 to July 31, 2027. The project aims to develop a revolutionary approach to synthesizing materials and chemicals under high-pressure conditions using porous materials. It proposes leveraging the high pressures observed within adsorbed fluid or solid films on solid substrates as an alternative to traditional,...
- This Project Grant award from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) provides $300,000 to the University of Alaska Fairbanks (UAF) to collaborate with NASA Ames Research Center on developing new porous sorbents for low-pressure carbon dioxide (CO2) capture. The goal is to engineer defects in metal-organic frameworks (MOFs) to increase their CO2 adsorption capacity at low pressures, which will enable more effective CO2 capture and storage...
- This National Science Foundation (NSF) Project Grant award, funded through the Mathematical and Physical Sciences program (CFDA 47.049), provides $424,350 to Duquesne University from September 1, 2023 to August 31, 2026. The project aims to design and discover energy-efficient, environmentally friendly, and cost-effective metal-free catalysts for the activation and conversion of small molecules, such as converting carbon dioxide to useful chemicals and fuels. The research approach combines...
- This National Science Foundation (NSF) Engineering program (CFDA 47.041) grant award of $502,972 to the University of Notre Dame's Notre Dame Research Division, awarded on July 1, 2024, will fund a research project to develop a novel hybrid modeling paradigm for multicomponent water vapor adsorption. The project aims to advance fundamental understanding of water vapor and water vapor mixture adsorption in porous materials, which is crucial for technologies like atmospheric water harvesting and...
- This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $325,000 to The Johns Hopkins University to develop computational approaches to examine how metal clusters within the tiny, ordered pores of aluminosilicate materials called zeolites can be used to optimize catalytic processes for nitrogen-based hydrogen carrier chemistries. The goal is to understand how nanometer-scale confinement impacts metal clusters and influences their catalytic...
- This CAREER award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) supports computational and theoretical research at New York University (NYU) aimed at efficiently sampling complex energy landscapes to determine the number of possible material configurations and their probabilities. The $280,000 award, with a project period from March 1, 2025 to February 28, 2030, will enable the development of new methods for enumerating...
- This $452,715 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports the development of 3D architectures that embed porous gas adsorbent materials inside high thermal conductivity substrates. The goal is to improve thermal control and enhance gas sorption performance for applications such as air purification, carbon capture, molecular sensing, gas separations, and catalysis. The key research objectives include developing methodologies to...
- The National Science Foundation (NSF) Engineering program (CFDA 47.041) awarded a $691,033 Project Grant to the University of Massachusetts to develop a physics-enabled deep learning approach for improving the energy efficiency of adsorptive separations of aqueous mixtures. The project aims to identify general principles governing the behavior of chemical mixtures within confined nanoporous environments, combining experiments, computer modeling, and machine learning to predict mixture behavior...
- This Project Grant award of $310,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research effort led by Professor Daniel Mittleman of Brown University and Professor Michael Ruggiero of the University of Rochester. The project aims to investigate guest-host molecule interactions in porous materials, such as metal-organic frameworks (MOFs) and clathrates, using a combination of vibrational spectroscopies and...
CAREER: NAVIGATING THERMODYNAMIC LANDSCAPES FOR PHASE EQUILIBRIA PREDICTIONS USING MOLECULAR MODELING AND MACHINE LEARNING -THIS AWARD IS FUNDED IN WHOLE OR IN PART UNDER THE AMERICAN RESCUE PLAN ACT OF 2021 (PUBLIC LAW 117-2). THIS CAREER PROJECT WILL USE ADVANCED COMPUTER SIMULATIONS AND MACHINE LEARNING ALGORITHMS TO ADVANCE FUNDAMENTAL UNDERSTANDING OF ADSORPTION OF GASES IN POROUS MATERIALS. ADSORPTION INVOLVES THE CONCENTRATION OR REJECTION OF MOLECULES INTERACTING WITH A MATERIAL SURFACE. IT IS A UBIQUITOUS PHENOMENON PRESENT IN OUR EVERYDAY LIVES AND IN MANY INDUSTRIAL AND BIOLOGICAL SETTINGS. IMPORTANT TECHNOLOGICAL APPLICATIONS THAT DEPEND ON ADSORPTION PROCESSES INCLUDE DRUG DELIVERY, POWER PRODUCTION AND ENERGY STORAGE, WATER HARVESTING, AND OTHERS THAT AFFECT THE OVERALL SOCIETAL WELL-BEING OF HUMANITY. THIS RESEARCH PROJECT MAKES USE OF POWERFUL COMPUTATIONAL MODELING TOOLS TO UNCOVER A COMPREHENSIVE PICTURE OF THE INTERACTIONS BETWEEN THE GAS SPECIES AND MATERIALS ONTO WHICH THEY ADSORB. THIS RESEARCH WILL LEAD TO FUNDAMENTAL INSIGHTS INTO THE ADSORPTION PROCESS AND THE IDENTIFICATION OF PROMISING NEW ADSORBENTS THAT ARE CRUCIAL FOR TECHNOLOGICAL ADVANCEMENTS IN AREAS OF NATIONAL IMPORTANCE INCLUDING HEALTH CARE, CLIMATE CHANGE, AND WATER SCARCITY. INTEGRATED OUTREACH AND EDUCATION COMPONENTS WITHIN THIS PROJECT INCLUDE INCREASING LITERACY OF MACHINE LEARNING AT THE UNDERGRADUATE AND GRADUATE LEVELS THROUGH COURSE DESIGN; HOSTING MIDDLE SCHOOL TEACHERS THROUGH THE NOTRE DAME SENIOR STEM TEACHING FELLOWS RESIDENCY PROGRAM TO CREATE COURSE MATERIALS FOR 6-8TH GRADERS CENTERED ON PROBABILITY AND STATISTICS; AND TRANSLATION OF THE MIDDLE SCHOOL COURSE MATERIAL INTO SPANISH FOR DISSEMINATION TO HISPANIC COMMUNITIES TO IMPROVE THEIR REPRESENTATION IN STEM FIELDS. THIS RESEARCH PROGRAM WILL INTEGRATE ADVANCED MOLECULAR MODELING AND MACHINE LEARNING METHODS TO CREATE A UNIVERSAL GAS ADSORPTION MODEL. BY SPECIFYING THE ABSORBENT MATERIAL, AN ADSORBATE GAS SPECIES, AND THE ADSORPTION CONDITIONS (TEMPERATURE AND PRESSURE), THE MODEL WILL BE ABLE TO ACCURATELY PREDICT THE AMOUNT OF GAS THAT IS ADSORBED WITHIN THE MATERIAL PORES AT EQUILIBRIUM. AN ADSORPTION MODEL WITH SUCH PREDICTIVE CAPABILITIES WOULD CONSTITUTE AN IMPORTANT ENGINEERING DESIGN TOOL, ELIMINATING THE CURRENT BOTTLENECK POSED BY THE HIGH COMPUTATIONAL COST OF SCREENING ALL POTENTIAL MATERIALS WITH MOLECULAR SIMULATIONS AND FUNDAMENTALLY ADVANCING DRUG DELIVERY, POWER PRODUCTION AND ENERGY STORAGE (E.G., HYDROGEN), AND ATMOSPHERIC WATER HARVESTING AND CARBON CAPTURE TECHNOLOGIES. THE DEVELOPMENT OF MODELS TO PREDICT THE NATURE OF GAS PHYSISORPTION IN POROUS MATERIALS WILL BE DEVELOPED WITHIN AN ACTIVE LEARNING (AL) FRAMEWORK TO EFFICIENTLY NAVIGATE THE LARGE CHEMICAL SPACES OF ADSORBATES AND ADSORBENTS. THE PROPERTIES OF ABSORBENT MATERIALS AND GAS MOLECULES WILL BE REPRESENTED AS ?FEATURES? ALCHEMICALLY TO MAXIMIZE THE RANGE OF MATERIALS AND MOLECULES THAT CAN BE STUDIED IN A COMPUTATIONALLY FEASIBLE MANNER. THE AL ALGORITHM WILL INFORM, IN AN AUTOMATED FASHION, WHICH SIMULATIONS TO PERFORM TO ACHIEVE ACCURATE PREDICTIONS WITH A LIMITED NUMBER OF SIMULATIONS, THUS ALLOWING FOR AN EXHAUSTIVE YET EFFICIENT EXPLORATION OF THE FEATURE SPACE. THE RESEARCH PLAN IS BASED ON THREE OBJECTIVES: (1) IMPLEMENT AND VALIDATE AN ACTIVE LEARNING FRAMEWORK CAPABLE OF NAVIGATING ADSORPTION LANDSCAPES, (2) NAVIGATE THE FEATURE LANDSCAPES OF SIMPLE GAS ADSORBATES, AND (3) SIMULTANEOUSLY NAVIGATE THE FEATURE LANDSCAPES OF MOLECULES AND POROUS MATERIALS FOR GAS ADSORPTION. BECAUSE THE PROPOSED AL FRAMEWORK WILL BE READILY ADAPTABLE TO OTHER ADSORPTION/MATERIAL DESIGN SCENARIOS, PHASE EQUILIBRIUM STUDIES BEYOND GAS ADSORPTION WILL BENEFIT. THESE RESEARCH EFFORTS WILL BE COMPLEMENTED BY OUTREACH EFFORTS TO MIDDLE SCHOOLS AND THE PUBLIC THROUGH BILINGUAL CURRICULUM DEVELOPMENT AND MIDDLE SCHOOL TEACHER TRAINING IN PROBABILITY AND STATISTICS, AND DISSEMINATION OF THE COURSE MATERIALS IN SPANISH TO THE LOCAL HISPANIC COMMUNITY AND IN PUERTO RICO. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $102.6k | 8/28/25 | ||
| Not listed | $408.2k | 1/27/22 |