Collaborative Research: Elements: Flexible & Open-Source Models for Materials and Devices

该项目将开发第一原理材料建模软件，可以接近多个长度和时间尺度（多尺度）。该软件将能够模拟复杂的系统，如整个设备和介观尺寸的材料。在该项目的过程中，主要研究人员计划开发一个基于Python的开源软件，旨在标准化和推广多尺度模拟方法。 这将使计算机建模在新化合物，材料和设备的设计中的使用成为可能。我们的目标是使多尺度模拟可重现，并可由更广泛的社区。在这方面，该项目将解决“实验室2.0”的概念，即计算机模拟取代实验室实验，如材料设计和昂贵的组合搜索可行的化学工艺。该软件将使用机器学习进行自我优化，并利用非专家可接近的线性工作流程。教育和多样性将通过来自高中和大学的代表性不足的少数民族直接参与黑客讲习班和夏季研究计划来促进。一种利用连续体模型的远程多尺度能力与特定相互作用的精确短程原子描述的方法，以及利用量子嵌入技术的理想可扩展性，将被调查。拟议实现的主要驱动程序将是一个Python代码库，它将执行当前软件中计算量不大的部分，但在通常的非面向对象语言中需要许多行代码的情况下，代码量很大。这是获得所需的与群集拓扑无关的工作流的关键。与计算可扩展性和代码刚性相关的长期存在的问题将以三管齐下的方法解决，该方法旨在开发（1）实现具有高度面向对象代码（例如，量子、经典原子和连续体求解器），（2）以最佳利用高性能计算架构的方式实现模块化工具的组合的混合工具，以及（3）实现高级数据启用优化策略的超工具，该策略生成组合几种混合工具的最佳工作流，从而使软件具有广泛的适用性并可被非专家访问。 这些目标将使多尺度模拟可重现，并可由更广泛的社区访问。该项目将解决“实验室2.0”范式，即计算机模拟取代实验室实验，如材料设计和可行的化学过程的组合搜索。由此产生的软件将使用机器学习进行自我优化，并利用非专家可接近的线性工作流程。教育和多样性将包括来自高中和大学的代表性不足的少数民族直接参与黑客讲习班和夏季研究计划。NSF高级网络基础设施办公室的这一奖项由NSF数学和物理科学理事会内的化学部和材料研究部共同支持。这一奖项反映了NSF的法定使命，并被认为值得支持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。

The project will develop first principles materials modeling software that can approach multiple length and time scales (multiscale). This software will be capable of modeling systems as complex as entire devices and materials of mesoscopic sizes. Over the course of the project the principal investigators plan to develop an open-source python-based software aimed at standardizing and generalizing multiscale simulations methods. This will enable the use of computer modeling in the design of new compounds, materials and devices. The goals are to render multiscale simulations reproducible and accessible by the broader community. In that context, the project will address the notion of "lab 2.0", by which computer simulations replace laboratory experiments in tasks such as materials design and costly combinatorial searches for viable chemical processes. The software will be self-optimized using machine learning and exploit linear workflows approachable by nonexperts. Education and diversity will be promoted by direct participation of underrepresented minorities from high schools and colleges in hackathon workshops and summer research programs.An approach that leverages the long-range multiscale capabilities of continuum models with accurate short-range atomistic descriptions of specific interactions, and that exploits the ideal scalability of quantum-embedding techniques, will be investigated. The main driver of the proposed implementation will be a Python codebase which will carry out the part of current software that is not computationally heavy, but instead is code heavy where many lines of code are needed in typically non-object-oriented languages. This is key to obtain the desired cluster-topology-agnostic workflows. Longstanding problems related to computational scalability and code stiffness will addressed in a three-pronged approach aimed at developing (1) modular tools implementing modules with highly object-oriented codes (e.g., quantum, classical atomistic, and continuum solvers), (2) hybrid tools implementing combinations of modular tools in a way that best exploits high-performance computing architectures, and (3) hyper tools implementing a high-level data-enabled optimization strategy that generates optimal workflows combining several hybrid tools, thereby making the software of broad applicability and accessible to nonexperts. These goals will render multiscale simulations reproducible and accessible by the broader community. The project will address the "lab 2.0" paradigm, by which computer simulations replace laboratory experiments in tasks such as materials design and combinatorial searches for viable chemical processes. The resultant software will be self-optimized using machine learning and exploit linear workflows approachable by nonexperts. Education and diversity will include the direct participation of underrepresented minorities from high schools and colleges in hackathon workshops and summer research programs.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry and the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences.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.