Predictive Modeling of Functional Nanoporous Materials, Nanoparticle Assembly, and Reactive Systems

PI Name: 
J. Ilja Siepmann
PI Email:
University of Minnesota
Allocation Program: 
Allocation Hours at ALCF: 
117 Million
Research Domain: 
Materials Science

This project supports work to develop a functional understanding of nanoporous materials important  to  industrial  clean  energy  production.  An  interdisciplinary,  collaborative  team  will use Argonne’s Leadership Computing Facility to perform predictive modeling studies that  will  accelerate  the  discovery  and  design  of  nanoporous  materials  with  tailored  functions  for  a  variety  of  energy-­related  applications.  Specific  applications  include nanoporous  membranes  for  separation  of  C8  aromatics,  for  second-­generation  biofuel  production,  for  purification  of  diols  as  renewable  feedstock  compounds  for  high-­value polymers,  and  for  separation  of  light  gases.  The  research  will  also  tackle  uncertainty  quantification  for  large-­scale  screening  studies,  reactive  equilibria  in  compressed  vapors and  in  nanoporous  confinement,  hydration  forces  and  ion  distribution  involved  in  nanoparticles assembly, and halide perovskites for photovoltaics. In addition, this research will  contribute  to  the  development  of  a  computational  infrastructure  for  screening  and  design of new heterogeneous catalysts and associated scaffolds.