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

合作研究：要素：灵活

基本信息

批准号：
1931473
负责人：
Michele Pavanello
金额：
$ 23.86万
依托单位：
Rutgers University Newark
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 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931473&HistoricalAwards=false
关键词：
Collaborative Research Elements Flexible Open

项目摘要

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)。该软件将能够对整个设备和介观尺寸的材料这样复杂的系统进行建模。在该项目的整个过程中，主要研究人员计划开发一个开放源码的基于蟒蛇的软件，旨在标准化和推广多尺度模拟方法。这将使在设计新化合物、材料和器件时使用计算机建模成为可能。其目标是使更广泛的社区能够重现和访问多尺度模拟。在这种情况下，该项目将解决“实验室2.0”的概念，即在材料设计和昂贵的组合搜索可行的化学过程等任务中，计算机模拟取代实验室实验。该软件将使用机器学习进行自我优化，并利用非专家可以接近的线性工作流。教育和多样性将通过来自高中和大学的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。将研究一种方法，利用连续介质模型的远程多尺度能力，对特定相互作用进行准确的短期原子描述，并利用量子嵌入技术的理想可扩展性。建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、但代码繁重的部分，这些代码在典型的非面向对象语言中需要很多行代码。这是获得所需的与群集拓扑无关的工作流的关键。与计算可伸缩性和代码僵化相关的长期问题将通过三管齐下的方法解决，其目标是开发(1)实现具有高度面向对象代码的模块的模块化工具(例如，量子、经典原子和连续统求解器)，(2)以最佳利用高性能计算体系结构的方式实现模块工具组合的混合工具，以及(3)实现高级数据启用的优化策略的超级工具，该策略结合几种混合工具生成最优的工作流，从而使软件具有广泛的适用性，并且非专家可以访问。这些目标将使更广泛的社区能够重现和使用多尺度模拟。该项目将解决“Lab 2.0”范例，在材料设计和组合搜索可行的化学过程等任务中，计算机模拟将取代实验室实验。生成的软件将使用机器学习进行自我优化，并利用非专家可以接近的线性工作流。教育和多样性将包括来自高中和大学的未被充分代表的少数群体直接参与黑客马拉松研讨会和暑期研究项目。该奖项由NSF高级网络基础设施办公室颁发，由NSF数学和物理科学局内的化学部和材料研究部联合支持。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。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Adaptive Subsystem Density Functional Theory

自适应子系统密度泛函理论

DOI：
10.1021/acs.jctc.2c00698
发表时间：
2022
期刊：
Journal of Chemical Theory and Computation
影响因子：
5.5
作者：
Shao, Xuecheng;Lopez, Andres Cifuentes;Khan Musa, Md Rajib;Nouri, Mohammad Reza;Pavanello, Michele
通讯作者：
Pavanello, Michele

Revised Huang-Carter nonlocal kinetic energy functional for semiconductors and their surfaces