Elements: Software to enable first-principles multi-scale simulations

该项目支持规模桥接软件基础设施的开发，以从第一性原理预测材料的非平衡行为。在其发展过程中，软件基础设施正在应用于预测重要的锂金属合金的热力学和动力学性质。锂合金目前非常有兴趣作为全固态锂电池的阳极，它们可以取代商业电池技术的石墨阳极，从而显著提高能量密度。除了应用于锂合金之外，该软件工具还旨在使科学家和工程师能够在功能和结构应用中生成关于材料动态响应的基本和机械见解。采用多尺度方法，依靠第一性原理统计力学计算基本的热力学和动力学成分的广义相场模型，描述的形态演变的材料脱离平衡。该方法的一个关键组成部分是使用基于簇展开的代理模型来插值蒙特卡罗和分子动力学模拟中的第一性原理电子结构计算。基础设施由库、可执行文件和jupyter笔记本组成，它们在CASM软件包（第一原理统计力学代码套件）上进行扩展，从而使机器学习的集群扩展代理模型能够参数化，用于物质的晶体和非晶体状态。软件基础设施包括(i)枚举工具，用于生成丰富的晶体学和非晶体学模型数据库，以训练机器学习的原子间电位（MLIPs）；（ii）在计算的热力学和动力学性质中进行不确定性量化的抽样代理模型软件；（iii）实现粗粒度方案的软件，将mlip的预测映射到基于晶体的簇扩展哈密顿量上。将宏观非平衡行为与电子结构水平上的性质联系起来的能力，使制定强大的设计原则成为可能，通过随后的高通量第一性原理计算，可以发现新材料。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，并得到材料研究部的联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project supports the development of a scale-bridging software infrastructure to predict non-equilibrium behavior of materials from first principles. In the course of its development, the software infrastructure is being applied to predict the thermodynamic and kinetic properties of important lithium-metal alloys. Lithium alloys are currently of great interest to serve as anodes in all-solid-state Li batteries, where they can replace the graphite anodes of commercial battery technologies and thereby enable significant increases in energy densities. Beyond its application to lithium alloys, the software tools are designed to empower scientists and engineers to generate fundamental and mechanistic insights about the dynamic response of materials in both functional and structural applications.A multi-scale approach is pursued that relies on first-principles statistical mechanics to calculate the essential thermodynamic and kinetic ingredients of generalized phase-field models that describe morphological evolution of a material out of equilibrium. A key component of the approach is the use of cluster expansion based surrogate models to interpolate first-principles electronic structure calculations within Monte Carlo and molecular dynamics simulations. The infrastructure consists of libraries, executables and jupyter notebooks that expand upon the CASM software package, a first-principles statistical mechanics code suite, and thereby enable the parameterization of machine-learned cluster expansion surrogate models for both crystalline and non-crystalline states of matter. The software infrastructure consists of (i) enumeration tools to generate a rich database of crystallographic and non-crystallographic models to train machine-learned interatomic potentials (MLIPs); (ii) software to sample surrogate models for uncertainty quantification in calculated thermodynamic and kinetic properties; and (iii) software to enable coarse-graining schemes that map predictions of MLIPs onto crystal-based cluster expansion Hamiltonians. The ability to link macroscopic non-equilibrium behavior to properties at the electronic structure level enables the formulation of powerful design principles with which new materials can be discovered through subsequent high-throughput first-principles calculations.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research.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.