About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Discovery and Design of Materials
|
| Presentation Title |
Humans-(less)-in-the-loop: moving toward autonomous materials design for metal-organic frameworks |
| Author(s) |
Scott Bobbitt, Jacob Harvey, Nickolas Gantzler, Calen Leverant, Dayton J. Vogel, Dorina Sava Gallis, Harrison Root, Jacob Boissiere, Benjamin Siu, Jason Sammon, Joshua Whiting |
| On-Site Speaker (Planned) |
Scott Bobbitt |
| Abstract Scope |
With advances in machine learning, simulation, and robotics, autonomous materials design seems more attainable than ever. However, there is still work to be done. This talk discusses recent progress in computationally guided discovery of metal–organic framework (MOF) catalysts for toxic chemical remediation. We developed an automated density functional theory (DFT) pipeline that generated thousands of binding energy and transition states, enabling rapid mapping of G-series agent interactions with diverse MOF structures. Machine learning models efficiently identified promising candidates while reducing calculations by up to 80%. Promising MOFs were synthesized on a robotic platform and validated through high-throughput gas phase catalysis experiments. Notable candidates showed comparable or superior DMMP degradation performance to benchmark materials like NU-1000. This work demonstrates progress towards a closed-loop computational-experimental framework that accelerates catalyst discovery, integrating high-throughput simulation, machine learning, and automated experiments.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 SAND2026-22908A |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |