Mukherjee Madhubanti, Sahu Harikrishna, Losego Mark D, Gutekunst Will R, Ramprasad Rampi
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
ACS Appl Mater Interfaces. 2024 Feb 28;16(8):10372-10379. doi: 10.1021/acsami.3c18105. Epub 2024 Feb 17.
Materials containing B, C, and O, due to the advantages of forming strong covalent bonds, may lead to materials that are superhard, i.e., those with a Vicker's hardness larger than 40 GPa. However, the exploration of this vast chemical, compositional, and configurational space is nontrivial. Here, we leverage a combination of machine learning (ML) and first-principles calculations to enable and accelerate such a targeted search. The ML models first screen for potentially superhard B-C-O compositions from a large hypothetical B-C-O candidate space. Atomic-level structure search using density functional theory (DFT) within those identified compositions, followed by further detailed analyses, unravels on four potentially superhard B-C-O phases exhibiting thermodynamic, mechanical, and dynamic stability.
含有硼、碳和氧的材料，由于形成强共价键的优势，可能会产生超硬材料，即维氏硬度大于40吉帕斯卡的材料。然而，探索这一广阔的化学、成分和构型空间并非易事。在这里，我们利用机器学习（ML）和第一性原理计算相结合的方法，来实现并加速这种有针对性的搜索。机器学习模型首先从一个庞大的假设性硼 - 碳 - 氧候选空间中筛选出潜在的超硬硼 - 碳 - 氧成分。在这些确定的成分中，使用密度泛函理论（DFT）进行原子级结构搜索，随后进行进一步的详细分析，揭示了四个具有热力学、力学和动态稳定性的潜在超硬硼 - 碳 - 氧相。
