EAGER: SSMCDAT2023: Revealing Local Symmetry Breaking in Intermetallics: Combining Statistical Mechanics and Machine Learning in PDF Analysis

EAGER：SSMCDAT2023：揭示金属间化合物中的局部对称性破缺：在 PDF 分析中结合统计力学和机器学习

• 批准号: 2334261
• 负责人: Eric Toberer
• 金额: $ 19.91万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334261&HistoricalAwards=false
• 关键词: EAGER; SSMCDAT2023; Revealing; Local; Symmetry

PART 1: NON-TECHNICAL SUMMARYThis award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER project furthers the understanding of the structure of semiconductors, especially those with a high degree of atomic disorder. Semiconductors are critical to modern electronics, with their physical properties, like conductivity and band gap, largely depending on their atomic arrangements. Despite significant progress in studying well-ordered crystals over the past century, understanding the structure of disordered crystals remains a challenge. Key to this understanding is how factors such as composition and synthesis methods affect atomic structure and, consequently, properties like electronic and thermal transport. This project employs cutting-edge artificial intelligence techniques, specifically symmetry-aware neural networks, to predict the forces between atoms in disordered materials. These predictions aid in modeling the results of experimental measurements, offering insight into these complex structures. Given the inherent complexity of these disordered materials, the project also involves developing innovative ways to visualize these structures at an atomic level, enabling researchers to identify patterns between semiconductor structure and electronic properties.PART 2: TECHNICAL SUMMARYEstablishing predictive relationships between composition, processing conditions, and material properties in intermetallics and alloys has been hampered by difficulties in understanding local structure. In an intermetallic material, the local structure includes both the chemical tiling on the lattice (i.e., motifs) and atomic distortions arising from asymmetric coordination environments. Even with high fidelity determination of such structures, the diversity of local structures remains challenging to visualize. As such, both measurement and understanding of local structure remain key challenges, making current intermetallic materials development efforts primarily empirical. This EAGER project addresses these challenges with an integrated computational-experimental approach. The Ge(1-x)MnxTe system serves as a model system due to its significant solubility, strong neutron scattering, and continuous phase transition with temperature and composition. Experimental insights are gleaned from neutron pair distribution function measurements (PDF) collected from intermetallics with various compositions. Computational insights into fitting these PDF measurements hinge on (i) recent advancements in equivariant neural net-based force fields for molecular dynamics simulations and (ii) a robust statistical mechanics treatment of configurational and vibrational energies. This fitting procedure remains true to the underlying bonding energetics within the intermetallic or alloy, meaning the resulting structures can be scrutinized for their distribution of local structures. A key part of this project is the featurization of the resulting structural distortions and bonding. Featurization optimization, unsupervised learning, and visualization developments will allow insights into the impact of processing conditions and composition on the local structure of these materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

第 1 部分：非技术摘要该奖项是根据 EAGER 提案颁发的。它支持在理海大学举办的 SSMCDAT 2023 Datathon 上推进的项目的进展。这个 EAGER 项目进一步加深了对半导体结构的理解，特别是那些具有高度原子无序性的结构。半导体对于现代电子产品至关重要，其物理特性（如电导率和带隙）很大程度上取决于其原子排列。尽管在过去的一个世纪里，在研究有序晶体方面取得了重大进展，但了解无序晶体的结构仍然是一个挑战。这种理解的关键是成分和合成方法等因素如何影响原子结构，从而影响电子和热传输等特性。该项目采用尖端的人工智能技术，特别是对称感知神经网络，来预测无序材料中原子之间的力。这些预测有助于对实验测量结果进行建模，提供对这些复杂结构的洞察。考虑到这些无序材料固有的复杂性，该项目还涉及开发创新方法，在原子水平上可视化这些结构，使研究人员能够识别半导体结构和电子特性之间的模式。第 2 部分：技术摘要在成分、加工条件、金属间化合物和合金的材料性能由于难以理解局部结构而受到阻碍。在金属间材料中，局部结构包括晶格上的化学平铺（即图案）和由不对称配位环境引起的原子畸变。即使对此类结构进行高保真度测定，局部结构的多样性仍然难以可视化。因此，局部结构的测量和理解仍然是关键挑战，使得当前金属间材料的开发工作主要是经验性的。这个 EAGER 项目通过集成的计算实验方法解决了这些挑战。 Ge(1-x)MnxTe 体系由于其显着的溶解度、强中子散射以及随温度和成分的连续相变而可作为模型体系。实验见解是从具有不同成分的金属间化合物中收集的中子对分布函数测量 (PDF) 中获得的。拟合这些 PDF 测量的计算见解取决于 (i) 用于分子动力学模拟的基于等变神经网络的力场的最新进展，以及 (ii) 对构型和振动能量的稳健统计力学处理。这种拟合过程仍然符合金属间化合物或合金内的基本键合能量，这意味着可以仔细检查所得结构的局部结构分布。该项目的一个关键部分是对由此产生的结构扭曲和粘合的特征化。特征化优化、无监督学习和可视化开发将有助于深入了解加工条件和成分对这些材料局部结构的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。