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DOE OSTI · 2507282

Data-Driven Approach for Controlled Icosahedral Boron- Rich Compound Growth

Liu, Bin [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000178907612)·Edgar, James H. [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000309185964)·Comer, Jeffrey [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000344371260)
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Abstract

This final technical report summarizes the research accomplishments and research highlights at the end of the funding period. This project aimed to leverage existing and new computational data produced from first-principles and molecular dynamics simulations to understand the thermodynamic, mechanical, and electronic properties of icosahedral boron compounds. The goal is to achieve targeted material properties by controlling the synthesis routes of these boron-rich compounds.

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36 MATERIALS SCIENCEdata-driven approachdensity functional theoryicosahedral boron compoundsmachine learningmetal flux methodmolecular dynamicsReaxFF

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Liu, Bin [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000178907612), Edgar, James H. [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000309185964), Comer, Jeffrey [Kansas State Univ., Manhattan, KS (United States)] (ORCID:0000000344371260). 2025-01-30. Data-Driven Approach for Controlled Icosahedral Boron- Rich Compound Growth. https://doi.org/10.2172/2507282

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36 MATERIALS SCIENCE↗