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，该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质大小的材料的复杂系统进行建模。在项目过程中，主要研究人员计划开发一种基于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使用。目标是使更广泛的社区可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，计算机模拟取代了诸如材料设计和昂贵组合搜索的任务中的实验室实验。该软件将使用机器学习和利用非XPERT可接近的线性工作流进行自优化。教育和多样性将通过在黑客马拉松讲习班和夏季研究计划中直接参与代表性不足的少数群体的直接参与。一种方法来利用连续模型的远距离多尺度能力，具有准确的短距离原子能描述，对特定的交互作用，并利用了对量子 - embed技术的理想可伸缩能力进行的，并利用了对量子 - embedding技术的理想可伸缩性。拟议的实现的主要驱动程序将是Python代码库，该代码库将执行当前软件的一部分，而不是计算重量，而是代码很重，在典型非对象的语言中需要许多代码行。这是获得所需的群集 - 敏捷工作流程的关键。与计算可伸缩性和代码刚度有关的长期问题将以三方面的方法解决，旨在开发（1）具有高度面向对象的代码（例如，量子，经典原子和连续性溶解度）的模块的模块化工具，（2）最佳地实现高级型号的模块化工具，以实现高级型号计算，以实现高效率的计算工具。支持数据的优化策略，该策略结合了几种混合工具，从而生成最佳的工作流程，从而使无关紧要的软件可访问。这些目标将使更广泛的社区可重现和访问多尺度模拟。该项目将解决“实验室2.0”范式，通过该范式，计算机模拟取代了诸如材料设计和合并搜索可行化学过程的任务中的实验室实验。最终的软件将使用机器学习和利用非XPERT可访问的线性工作流进行自优化。教育和多样性将包括来自高中和学院在Hackathon研讨会和夏季研究计划中的代表性不足的少数群体的直接参与。该奖项由NSF高级Cyberinfrasture办公室颁发的奖项，由NSF的数学和物理科学授予的NSF材料研究局共同支持。基金会的智力优点和更广泛的影响评论标准。

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