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Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
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杰纳里斯3.0:通过刚性压力产生密集的分子晶体结构.

Yi Yang1, Rithwik Tom2, Jose A G L Wui3

  • 1Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

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科学领域:

  • 晶体学 晶体学是指结晶学.
  • 计算化学计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 分子晶体的多态性显著影响材料的性能和性能.
  • 晶体结构预测 (CSP) 对于计算探索多态体至关重要.
  • 现有的方法需要大量的计算资源来探索晶体结构的景观.

研究的目的:

  • 介绍Genarris 3.0,一个增强的开源代码用于分子晶体结构预测.
  • 通过机器学习的原子间潜力 (MLIPs) 加快对潜在能源景观的探索.
  • 开发和验证一个新的聚类和下行选择工作流程,以实现高效的多形态排名.

主要方法:

  • 杰纳里斯3.0集成了一个新的"刚性压力"算法,用于几何压缩.
  • 与MACE-OFF23的集成 (L) MLIPs用于加速的几何优化和能量排名.
  • 应用集群和下行选择工作流程,以实现高效的数据处理.

主要成果:

  • 杰纳里斯3.0成功地预测了六个不同的目标的晶体结构,包括能量材料.
  • 分析显示了MLIP性能的变化,特别是对于高能耗材料,这在新的工作流程中得到了缓解.
  • 该代码有效地生成适合训练机器学习模型的分子晶体数据集.

结论:

  • 杰纳里斯3.0为晶体结构预测提供了一个高效和有效的平台.
  • 整合MLIP和一个强大的工作流加速了稳定的多态的发现.
  • 生成的数据集对于推进材料科学中的机器学习应用非常有价值.