Machine Learned Potentials for In2O3

In2O3 的机器学习潜力

• 批准号: 2468419
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2468419
• 关键词: Machine; Learned; Potentials; In2O3

Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: Recent progress in constructing machine learned potentials for molecular and solid systems has redefined the boundaries of computational chemistry and solid-state physics at the atomic scale. However, simulations of part molecular part solid systems, such as those required for heterogeneous catalysis, have remained challenging. Here we will fit a Gaussian Approximation Potentials (GAP) for Indium Oxide as a test model. This project will set the stepping stones to test chemically relevant observables, investigate molecular surface interactions and sets the path to an all encompassing potential for heterogeneous catalysis.

第一年：CDT所有一年级学生成员的通用培训活动。2-4年级：在构建分子和固体系统的机器学习潜力方面的最新进展重新定义了原子尺度上的计算化学和固态物理的边界。然而，部分分子部分固体系统的模拟，例如非均相催化所需的系统，仍然具有挑战性。在这里，我们将拟合氧化铟的高斯近似电位（GAP）作为测试模型。该项目将为测试化学相关的可观测量，研究分子表面相互作用奠定基础，并为多相催化的全面潜力奠定基础。