The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three general scales of abstractions of what a material is - atomistic, physical, and design. At each, an efficient materials informatics infrastructure is being built from the ground up based on (1) the fundamental understanding of the underlying prior knowledge, including the data, (2) deployment routes that take advantage of it, and (3) pathways to extend it in an autonomous or semi-autonomous fashion, while heavily relying on artificial intelligence (AI) to guide well-established DFT-based ab initio and CALPHAD-based thermodynamic methods. The resulting multi-level discovery infrastructure is highly generalizable as it focuses on encoding problems to solve them easily rather than looking for an existing solution. To showcase it, this dissertation discusses the design of multi-alloy functionally graded materials (FGMs) incorporating ultra-high temperature refractory high entropy alloys (RHEAs) towards gas turbine and jet engine efficiency increase reducing CO2 emissions, as well as hypersonic vehicles. It leverages a new graph representation of underlying mathematical space using a newly developed algorithm based on combinatorics, not subject to many problems troubling the community. Underneath, property models and phase relations are learned from optimized samplings of the largest and highest quality dataset of HEA in the world, called ULTERA. At the atomistic level, a data ecosystem optimized for machine learning (ML) from over 4.5 million relaxed structures, called MPDD, is used to inform experimental observations and improve thermodynamic models by providing stability data enabled by a new efficient featurization framework.

翻译：计算研究的真正力量通常体现在其直接成果或为他人提供的赋能。本研究同时探索了这两条路径，通过多个具体项目在三个不同抽象层面——原子尺度、物理尺度和设计尺度——展开材料研究。在每个层面，我们都在构建一个高效的材料信息学基础设施，其基础包括：(1) 对底层先验知识（含数据）的根本性理解；(2) 利用该知识的部署路径；(3) 以自主或半自主方式扩展该知识的途径。整个过程高度依赖人工智能（AI）来指导成熟的基于DFT的从头算方法和基于CALPHAD的热力学方法。由此形成的多层次发现基础设施具有高度通用性，其核心在于将问题编码以便于求解，而非寻找现有解决方案。为展示其应用，本论文讨论了多功能梯度材料（FGMs）的设计，该材料融合了超高温难熔高熵合金（RHEAs），旨在提高燃气轮机和喷气发动机效率以减少CO2排放，并应用于高超音速飞行器。研究采用了一种基于组合数学新开发的算法，构建了底层数学空间的新型图表示方法，该方法避免了该领域常见的诸多问题。在此基础上，通过优化采样全球规模最大、质量最高的高熵合金数据集ULTERA，学习材料性能模型与相图关系。在原子层面，利用专为机器学习（ML）优化的数据生态系统MPDD（包含超过450万个弛豫结构），通过新型高效特征化框架提供稳定性数据，从而解释实验观测并改进热力学模型。