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

该项目支持开发一个规模桥接软件基础设施，以根据第一性原理预测材料的非平衡行为。在其开发过程中，该软件基础设施正被应用于预测重要的锂金属合金的热力学和动力学性质。目前，锂合金作为全固态锂电池的阳极受到了极大的关注，它们可以取代商用电池技术中的石墨阳极，从而显著提高能量密度。除了应用于锂合金之外，这些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对材料的动态响应产生基本和机械的见解。多尺度方法依赖于第一原理统计力学来计算广义相场模型的基本热力学和动力学成分，该模型描述了非平衡状态下材料的形态演变。该方法的一个关键组成部分是使用基于团簇展开的代理模型来内插蒙特卡罗和分子动力学模拟中的第一原理电子结构计算。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(一种第一原理统计力学代码套件)的基础上进行扩展，从而能够对结晶态和非晶态物质的机器学习的团簇扩展代理模型进行参数化。软件基础设施包括：(1)用于生成结晶学和非结晶学模型的丰富数据库的计数工具，以训练机器学习的原子间相互作用势(MLIP)；(2)用于采样替代模型的软件，用于在计算的热力学和动力学性质中进行不确定性量化；以及(3)软件，用于实现粗粒化方案，将MLIP的预测映射到基于晶体的团簇展开哈密顿量上。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我们能够制定强大的设计原则，通过随后的高通量第一原理计算来发现新材料。该奖项由NSF高级网络基础设施办公室联合材料研究部支持。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.