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

合作研究：要素：灵活

• 批准号: 1931479
• 负责人: Oliviero Andreussi
• 金额: $ 25.35万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931479&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Flexible; &amp

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.

该项目将开发可以接近多个长度和时间尺度（多尺度）的第一性原理材料建模软件。该软件将能够建模系统复杂的整个设备和材料的介观尺寸。在整个项目过程中，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。这将使在设计新的化合物、材料和设备时使用计算机建模成为可能。目标是使多尺度模拟可复制，并由更广泛的社区访问。在这种背景下，该项目将解决“实验室2.0”的概念，通过计算机模拟取代实验室实验，例如材料设计和昂贵的组合搜索可行的化学过程。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高中和大学中未被充分代表的少数族裔直接参与黑客马拉松研讨会和暑期研究项目来促进。将研究一种利用连续体模型的远程多尺度能力和精确的特定相互作用的短程原子描述，并利用量子嵌入技术的理想可扩展性的方法。所提议的实现的主要驱动因素将是Python代码库，它将执行当前软件中计算量不大的部分，但代码量很大，通常需要在非面向对象语言中编写许多行代码。这是获得所需的与集群拓扑无关工作流的关键。与计算可扩展性和代码刚性相关的长期问题将通过三管齐下的方法来解决，旨在开发(1)模块化工具实现具有高度面向对象代码的模块（例如，量子，经典原子和连续体求解器），(2)混合工具实现模块化工具的组合，以最好地利用高性能计算架构。(3)超级工具实现了一种高层次的数据优化策略，该策略可以结合多个混合工具生成最佳工作流，从而使软件具有广泛的适用性，并且非专家也可以访问。这些目标将使多尺度模拟重现，并为更广泛的社区所利用。该项目将解决“实验室2.0”范例，通过计算机模拟取代实验室实验，例如材料设计和可行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高中和大学中未被充分代表的少数族裔直接参与黑客马拉松研讨会和暑期研究项目。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，由美国国家科学基金会数学和物理科学理事会化学部和材料研究部联合支持。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。