This $178,986 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports data-intensive and computational research and education at The Pennsylvania State University (Penn State) to develop new machine learning methods for the inverse design of sequence-defined macromolecules. The goal is to create a computationally efficient approach to rationally design macromolecules that self-assemble into target morphologies with...
This federal Project Grant award of $244,960 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports computational research to discover new block polymer materials. The project aims to develop generative AI and machine learning techniques to predict novel self-assembled block polymer structures and identify the corresponding polymer formulations and processing parameters. The goal is to enable the discovery of new multifunctional materials...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $273,291 to the University of California, Santa Barbara to develop computational tools that combine machine learning and scientific computing for the exploration and prediction of polymer systems. The goal is to accelerate the discovery of new materials and provide a framework for computationally costly problems across various scientific domains. The research...
This four-year project grant from the National Science Foundation Division of Materials Research, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $1,414,494 to the University of Illinois to conduct collaborative research in machine learning algorithms for the prediction and synthesis of next-generation superhard functional materials. The research aims to advance scientific understanding and develop new materials through the use of machine learning to model material...
The National Science Foundation (NSF) awarded a $2,574,835 Project Grant under its Integrative Activities program (CFDA 47.083) to the University of Illinois at Urbana-Champaign. The award supports the acquisition of an automated, high-throughput system for the combinatorial design and development of complex polymer systems. This state-of-the-art system integrates component dispensing, mixing, and processing with high-throughput rheological, optical, and thermal characterization of materials....
This $248,461 Project Grant award was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award will support research to efficiently design patchy particles for targeted assembly of bulk and finite soft materials structures, using state-of-the-art data-driven methods. The project aims to develop a fast design pipeline for building blocks that could potentially be synthesized in a laboratory environment. The...
This $140,390 Project Grant was awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the University of Illinois in Chicago. The grant aims to establish predictable strategies for making porous peptide materials by combining peptides with tailored pi-stacking aromatic groups that drive the assembly and provide added functionality. Additionally, the grant will develop 3D-printed molecular puzzles to better teach the...
This Project Grant award of $421,781 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the University of Chicago in developing advanced polymer materials with unprecedented mechanical properties and self-healing capabilities. The research aims to create an integrated database of adaptive polymer networks that are highly stretchable, resilient, and self-healing through the use of novel double-threaded slide-ring polymers and dynamic...
This National Science Foundation (NSF) Award under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) aims to establish an effective framework to ingest, inform, and leverage multimodal data from materials science experiments to advance large language models (LLMs) for generating hypotheses toward the design of new and superior metallic alloys. The $150,843 project, awarded on February 1, 2025, is being conducted by the University of Wisconsin-Madison. The work supports...
This $598,958 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support research at the University of Illinois to study the mechanical behavior of two-dimensional atomic sheets with defects. The project will leverage advances in artificial intelligence and machine learning to overcome computational challenges in modeling the elasticity, strength, and fracture properties of these two-dimensional lateral heterostructures. The research aims to...