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Network Covalent Solids02:18

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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相关实验视频

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Negative Additive Manufacturing of Complex Shaped Boron Carbides
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超硬BCO化合物的计算机驱动设计

Madhubanti Mukherjee1, Harikrishna Sahu1, Mark D Losego1

  • 1School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

ACS applied materials & interfaces
|February 17, 2024
PubMed
概括

研究人员利用机器学习和第一原则计算探索了超硬的-碳-氧材料. 发现了四个稳定,潜在的超硬BCO相,推动了对新型超硬材料的研究.

关键词:
B−C−O 化学空间空间在 DFT 方面,它是最重要的.维克尔的硬度 维克尔的硬度晶体结构搜索 搜索 搜索弹性模块 弹性的模块机器学习 (ML) 是指机器学习.超级硬的 超级硬的

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 固态物理 固态物理

背景情况:

  • 具有强共价键的材料,特别是含,碳和氧 (B-C-O) 的材料,显示出超硬度的潜力 (维克硬度> 40 GPa).
  • B-C-O材料的巨大化学空间使得全面的探索具有挑战性.

研究的目的:

  • 为了加速发现超硬BCO材料.
  • 系统地选假设的 B-C-O 组合物,并确定稳定的超硬相.

主要方法:

  • 利用机器学习 (ML) 模型对潜在的BCO组合进行初始选.
  • 采用第一原理计算,特别是密度函数理论 (DFT),用于原子层结构搜索.
  • 进行了详细的分析,以评估候选结构的热力学,机械和动态稳定性.

主要成果:

  • 确定了四个潜在的超硬BCO阶段.
  • 这些相表现出有希望的热力学,机械和动态稳定性.
  • 成功地证明了ML和DFT在复杂材料空间的导航中的协同作用.

结论:

  • 该研究成功地确定了具有潜在超硬性质的新型,稳定的BCO相.
  • 集成的ML-DFT方法对于目标材料发现是有效的.
  • 这项工作为进一步研究先进的B-C-O超硬材料开辟了道路.