Elements: Software: Autonomous, Robust, and Optimal In-Silico Experimental Design Platform for Accelerating Innovations in Materials Discovery

要素：软件：用于加速材料发现创新的自主、稳健和优化的计算机实验设计平台

基本信息

批准号：
1835690
负责人：
Byung-Jun Yoon
金额：
$ 60万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835690&HistoricalAwards=false
关键词：
Elements Software Autonomous Robust Optimal

项目摘要

Accelerating the development of novel materials that have desirable properties is a critical challenge as it can facilitate advances in diverse fields across science, engineering, and medicine with significant contributions to economic growth. For example, the US Materials Genome Initiative calls for cutting the time for bringing new materials from discovery to deployment by half at a fraction of the cost, by integrating experiments, computer simulations, and data analytics. However, the current prevailing practice in materials discovery relies on trial-and-error experimental campaigns and/or high-throughput screening approaches, which cannot efficiently explore the huge design space to develop materials with the targeted properties. Furthermore, measurements of material composition, structure, and properties often contain considerable errors due to technical limitations in materials synthesis and characterization, making this exploration even more challenging. This project aims to develop a software platform for robust autonomous materials discovery that can shift the current trial-and-error practice to an informatics-driven one that can potentially expedite the discovery of novel materials at substantially reduced cost and time. Throughout the project, the PI and Co-PIs will mentor students and equip them with the skills necessary to tackle interdisciplinary problems that involve materials science, computing, optimization, and artificial intelligence. Research findings in the project will be incorporated into the courses taught by the PI and Co-PIs, thereby enriching the learning experience of students.The objective of this project is to develop an effective in-silico experimental design platform to accelerate the discovery of novel materials. The platform will be built on optimal Bayesian learning and experimental design methodologies that can translate scientific principles in materials, physics, and chemistry into predictive models, in a way that takes model and data uncertainty into account. The optimal Bayesian experimental design framework will enable the collection of smart data that can help exploring the material design space efficiently, without relying on slow and costly trial-and-error and/or high-throughput screening approaches. The developed methodologies will be integrated into MSGalaxy, a modular scientific workflow management system, resulting in an accessible, reproducible, and transparent computational platform for accelerated materials discovery that allows easy and flexible customization as well as synergistic contributions from researchers across different disciplines.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Materials Research in the 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.

加速具有理想特性的新型材料的开发是一个至关重要的挑战，因为它可以促进科学，工程和医学各种领域的进步，并为经济增长做出了重大贡献。例如，美国材料基因组倡议要求通过整合实验，计算机模拟和数据分析，以减少从发现到部署的新材料的时间。但是，当前的材料发现中的主要实践依赖于试验实验活动和/或高通量筛选方法，这些筛选方法无法有效地探索具有目标特性的巨大设计空间，以开发材料。此外，由于材料合成和表征的技术限制，材料组成，结构和性质的测量通常包含相当大的错误，从而使这种探索更具挑战性。该项目旨在开发一个软件平台，以发现强大的自主材料发现，该计划可以将当前的试用练习转移到以信息学为导向的驱动器的范围内，该实践可能会以大幅减少的成本和时间来加快新颖材料的发现。在整个项目中，PI和Co-Pis将指导学生，并为他们提供解决涉及材料科学，计算，优化和人工智能的跨学科问题所需的技能。该项目中的研究结果将纳入PI和Co-Pis教授的课程中，从而丰富了学生的学习经验。该项目的目的是开发一个有效的内部实验设计平台，以加速发现新型材料。该平台将基于最佳的贝叶斯学习和实验设计方法，这些方法可以将材料，物理和化学中的科学原理转化为预测模型，以将模型和数据不确定性考虑在内。最佳的贝叶斯实验设计框架将使可以收集智能数据，这些数据可以有助于有效地探索材料设计空间，而无需依靠缓慢且昂贵的试验和/或高通量筛选方法。 The developed methodologies will be integrated into MSGalaxy, a modular scientific workflow management system, resulting in an accessible, reproducible, and transparent computational platform for accelerated materials discovery that allows easy and flexible customization as well as synergistic contributions from researchers across different disciplines.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Materials Research in the该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响评论标准，这是值得支持的。

项目成果

期刊论文数量（36）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Microstructure classification in the unsupervised context

无监督环境下的微观结构分类

DOI：
10.1016/j.actamat.2021.117434
发表时间：
2022
期刊：
Acta Materialia
影响因子：
9.4
作者：
Kunselman, Courtney;Sheikh, Sofia;Mikkelsen, Madalyn;Attari, Vahid;Arróyave, Raymundo
通讯作者：
Arróyave, Raymundo

On the Importance of Microstructure Information in Materials Design: PSP vs PP

DOI：
10.1016/j.actamat.2021.117471
发表时间：
2021-11
期刊：
Acta Materialia
影响因子：
9.4
作者：
Abhilash Molkeri;Danial Khatamsaz;Richard Couperthwaite;Jaylen James;R. Arróyave;D. Allaire;Ankit Srivastava
通讯作者：
Abhilash Molkeri;Danial Khatamsaz;Richard Couperthwaite;Jaylen James;R. Arróyave;D. Allaire;Ankit Srivastava

Bayesian Active Learning by Soft Mean Objective Cost of Uncertainty

通过软平均客观不确定性成本进行贝叶斯主动学习

DOI：
发表时间：
2021
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Zhao, Guang;Dougherty, Edward;Yoon, Byung-Jun;Alexander, Francis J.;Qian, Xiaoning
通讯作者：
Qian, Xiaoning

Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations