CAREER: Bayesian Symmetry-Respecting Machine Learning Framework for Predicting Electronic — NSF Award to Michigan Technological Un
Electronic-structure methods have a profound impact on several disciplines, especially materials research, as demonstrated by extensive studies in this field and the discovery of numerous advanced materials and devices with widespread applications. However, large-scale electronic structure calculations are prohibitivel
| Award title | CAREER: Bayesian Symmetry-Respecting Machine Learning Framework for Predicting Electronic |
|---|---|
| Award ID | 2442313 |
| Awardee | Michigan Technological University |
| City | HOUGHTON |
| State | MI |
| Amount obligated | $669,490 |
| Principal investigator | Susanta Ghosh |
| Program | Mechanics of Materials and Str, CAREER: FACULTY EARLY CAR DEV |
| Start date | 09/01/2025 |
| Abstract | Electronic-structure methods have a profound impact on several disciplines, especially materials research, as demonstrated by extensive studies in this field and the discovery of numerous advanced materials and devices with widespread applications. However, large-scale electronic structure calculations are prohibitively expensive. Machine learning models can accelerate these simulations, but current models often lack one or more of the following: uncertainty quantification, preservation of symme |
| Source | NSF Awards |
$799/mo
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