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Elements: DeepPDB: An open-source automated framework to enable high-fidelity atomistic simulations in unexplored material space

元素：DeepPDB：一个开源自动化框架，可在未探索的材料空间中实现高保真原子模拟

基本信息

批准号：
2003808
负责人：
Wissam Saidi
金额：
$ 60万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-11-01 至 2024-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003808&HistoricalAwards=false
关键词：
Elements DeepPDB open source automated

项目摘要

A key requirement to address the world’s energy challenges is the development of new energy-efficient and smart materials. To aid in this process, state-of-the-art and revolutionary computational tools can be used to simulate new materials, thus providing a thorough understanding of their characteristics and behavior. Such materials simulation efforts can drastically reduce the time-to-market of new materials from decades to months. Traditional simulation approaches can accurately predict the behavioral properties of materials both at the smallest possible scale (atomic) and at the macroscopic level, i.e. millimeter or larger. Many critical materials properties are defined and needed at scales ranging from a few nanometers to micrometers, yet simulations are lacking at these levels. While algorithms do exist to simulate properties at these scales, often we lack the fundamental parameters, termed force-fields, for novel materials such as those for next-generation solar cells, batteries and jet turbine alloys. These force-fields are laborious to determine using traditional methods, requiring significant expertise and thus restricted by the human-in-the-loop. The primary goal of the proposed Deep Potential DataBase (DeepPDB) will be to offer an open-source toolkit with the ability to automatically generate estimates of force-fields parameters using advanced empirical-based computational tools. We will also curate and disseminate a validated repository of first-principles datasets and their corresponding potentials for inorganic materials. DeepPDB will serve both the materials science and machine learning communities, by providing the former with critical parameters to solve materials challenges and the latter by benchmark datasets for machine learning development. The resulting synergy will enable artificial intelligence and machine learning to play a greater role in computing critical materials properties for next-generation challenges. DeepPDB will also serve a critical educational objective, allowing the budding of a new generation of materials scientists, who understand how deep learning can be used to solve materials science challenges. DeepPDB aims to build a database of deep neural network potentials (DNP) for the simulation of inorganic materials. In the process DeepPDB will: (1) develop automated workflows that given a target composition, will run the necessary density functional theory (DFT) calculations, train DNPs, validate against metrics imposed by the training data, identify the input-data space with the largest uncertainty and iterate until an optimal DNP is trained; (2) openly disseminate the training DFT data along with the pre-trained DNPs; (3) develop transparent automated validation that encompasses both traditional DNP based methods as well as fully integrated tests that include target metrics. To accomplish this, DeepPDB will build a toolkit based on careful software engineering practices: a combination of feature- and sprint-based development cycles; constant continuous integration using unit-tests and integration tests as milestones; and a database-oriented approach to data and workflow management. The resulting open-source toolkit will serve as a foundational tool to investigate the properties of hitherto-unseen materials at length- and time-scales previously not possible.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by 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.

应对世界能源挑战的一个关键要求是开发新的节能和智能材料。为了帮助这一过程，可以使用最先进和革命性的计算工具来模拟新材料，从而提供对它们的特征和行为的透彻了解。这样的材料模拟工作可以将新材料的上市时间从几十个月大幅缩短到几个月。传统的模拟方法可以在最小的尺度(原子)和宏观的水平(即毫米或更大)准确地预测材料的行为性质。许多关键材料的性质是在几纳米到微米的尺度上定义和需要的，但在这些水平上缺乏模拟。虽然确实存在算法来模拟这些尺度下的属性，但我们往往缺乏新材料的基本参数，称为力场，例如用于下一代太阳能电池、电池和喷气涡轮机合金的那些材料。使用传统方法确定这些力场是费力的，需要大量的专业知识，因此受到人在回路中的限制。拟议的深势数据库(DeepPDB)的主要目标将是提供一个开放源码工具包，能够使用先进的基于经验的计算工具自动生成力场参数的估计。我们还将策划和传播一个经过验证的第一原理数据集及其无机材料的相应潜力的储存库。DeepPDB将为材料科学和机器学习社区提供服务，为前者提供解决材料挑战的关键参数，为后者提供用于机器学习发展的基准数据集。由此产生的协同效应将使人工智能和机器学习在计算下一代挑战的关键材料属性方面发挥更大作用。DeepPDB还将服务于一个关键的教育目标，允许新一代材料科学家的萌芽，他们了解如何利用深度学习来解决材料科学的挑战。DeepPDB的目标是建立一个用于模拟无机材料的深度神经网络势(DNP)数据库。在这一过程中，DeepPDB将：(1)开发自动化工作流程，在给定目标成分的情况下，运行必要的密度泛函理论(DFT)计算，训练DNP，对照训练数据强加的指标进行验证，识别具有最大不确定性的输入数据空间并迭代，直到训练出最优DNP；(2)公开传播训练DFT数据以及预先训练的DNP；(3)开发透明的自动化验证，其中包括传统的基于DNP的方法以及包括目标指标的完全集成的测试。为了实现这一点，DeepPDB将基于仔细的软件工程实践构建一个工具包：基于功能和Sprint的开发周期的组合；以单元测试和集成测试为里程碑的持续不断的集成；以及面向数据库的数据和工作流管理方法。由此产生的开源工具包将成为一个基础工具，用于研究迄今未见过的材料的性质-以及以前不可能的时间尺度。该奖项由NSF高级网络基础设施办公室获得，由NSF数学和物理科学局内的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（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

DOI：
10.1039/d3dd00046j
发表时间：
2023
期刊：
Digital Discovery
影响因子：
0
作者：
Christopher M. Andolina;W. Saidi
通讯作者：
Christopher M. Andolina;W. Saidi

Convergence acceleration in machine learning potentials for atomistic simulations

DOI：
10.1039/d1dd00005e
发表时间：
2022-02-14
期刊：
DIGITAL DISCOVERY
影响因子：
0
作者：
Bayerl, Dylan;Andolina, Christopher M.;Saidi, Wissam A.
通讯作者：
Saidi, Wissam A.

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

主动学习引导金属卤化物钙钛矿结晶的尺寸控制

DOI：
10.1021/acs.chemmater.1c03564
发表时间：
2022
期刊：
Chemistry of Materials
影响因子：
8.6
作者：
Li, Zhi;Nega, Philip W.;Nellikkal, Mansoor Ani;Dun, Chaochao;Zeller, Matthias;Urban, Jeffrey J.;Saidi, Wissam A.;Schrier, Joshua;Norquist, Alexander J.;Chan, Emory M.
通讯作者：
Chan, Emory M.

Designing multinary noble metal‐free catalyst for hydrogen evolution reaction