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

该项目支持开发一个尺度桥接软件基础设施，以从第一原理预测材料的非平衡行为。在其开发过程中，软件基础设施正被应用于预测重要锂金属合金的热力学和动力学性质。锂合金目前在全固态锂电池中作为阳极非常受关注，它们可以取代商业电池技术的石墨阳极，从而能够显著提高能量密度。除了应用于锂合金之外，该软件工具旨在使科学家和工程师能够在功能和结构应用中对材料的动态响应产生基本的和机械的见解。场模型描述了材料在失去平衡状态下的形态演化。该方法的一个关键组成部分是使用基于簇扩展的代理模型内插Monte Carlo和分子动力学模拟的第一原理电子结构计算。基础设施包括库，可执行文件和jupyter笔记本电脑，扩展CASM软件包，第一原理统计力学代码套件，从而使机器学习的集群扩展代理模型的参数化结晶和非结晶状态的物质。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势（MLIP）;（ii）软件，用于对计算的热力学和动力学性质中的不确定性量化的替代模型进行采样;以及（iii）软件，用于实现粗粒度方案，将MLIP的预测映射到基于晶体的簇扩展Hamilton。将宏观非平衡行为与电子结构水平的性质联系起来的能力使得能够制定强大的设计原则，通过这些原则，可以通过随后的高通量第一-第二阶段发现新材料。该奖项由NSF高级网络基础设施办公室颁发，并得到材料研究部的共同支持。该奖项反映了NSF的法定使命，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。

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.