Travel Support for Workshop on Best Practices in Modeling Across Scales from Materials Discovery to Manufacturing; Arlington, Virginia; Summer 2023

该奖项为主要演讲者和演讲者提供差旅支持，以参加“从材料发现到制造的跨规模建模的最佳实践：实现对错误和不确定性的系统分析”的研讨会。本研讨会将讨论了解材料创新和制造的第一原理建模领域的挑战和机遇的必要性。研讨会提供了一个论坛，分享材料建模、不确定性量化和误差分析方面的前沿研究。它为参与者提供了一个展示他们的科学成就的机会，与来自学术界、政府实验室和行业的同行和同事互动，扩大他们的网络，并在更广泛的先进材料和制造业社区内促进新的合作。研讨会为与会者提供了对材料发现、设计和制造周期的更广泛的看法，这些对于推动造福社会的新技术至关重要。与NSF扩大科学和工程参与的目标一致，研讨会寻求妇女和代表不足的少数群体作为演讲者和参与者参与。该研讨会符合培养先进材料和制造的国家优先事项。该研讨会为专家提供了一个平台，让他们介绍和讨论他们在从材料建模和发现到制造的各个范围内对误差和不确定性进行系统分析的最佳实践方面的研究和结果。研讨会参与者将讨论数据科学、机器学习和人工智能(AI)在开发材料及其制造的基本原理建模方面的应用。研讨会汇集了建模和仿真、多尺度建模、机器学习、信息学、数据分析、流程设计和制造的人工智能等领域的研究人员和专家，讨论交叉途径、挑战和机遇。其目标是确定错误和不确定性分析并将其集成到标准计算工作流中，以便严格的错误确定和报告成为材料建模社区的标准规范。研讨会的重点是新材料发现的创新途径、新材料的设计和制造以及确定交叉研究机会。研讨会分成不同的环节，包括国内外知名专家的演讲，他们讨论了他们的最新研究，并确定了以下主题的研究差距：(1)电子结构、第一原理和量子化学方法的误差控制，(2)与实验者合作时建模者的最佳实践，(3)更高尺度的误差控制：原子间势，包括多尺度材料建模中机器学习的误差传播，(4)材料合成、加工和制造的误差控制，(5)将错误分析纳入工作流程和材料数据库。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。

This award provides travel support for key speakers and presenters to attend the workshop on “Best Practices in Modeling Across Scales from Materials Discovery to Manufacturing: Towards Systematic Analysis of Errors and Uncertainty." This workshop will address the need to understand the challenges and opportunities in the area of first principles modeling for materials innovation and manufacturing. The workshop provides a forum to share leading-edge research on materials modeling, uncertainty quantification and error analysis. It offers participants an opportunity to showcase their scientific accomplishments, interact with peers and colleagues from academia, government labs, and industry, extend their network, and foster new collaborations within the broader advanced materials and manufacturing communities. The workshop provides attendees with a broader view of the materials discovery, design, and manufacturing cycle, that are critical to advancing new technologies that benefit society. Consistent with NSF's goal of broadening participation in science and engineering, the workshop seeks participation of women and underrepresented minority groups as speakers and attendees. The workshop meets the national priority of fostering advanced materials and manufacturing.This workshop provides a platform for experts to present and discuss their research and results in Best Practices in Systematic Analysis of Errors and Uncertainty Across Scales from Materials Modeling and Discovery to Manufacturing. The workshop participants will discuss the application of Data Science, Machine Learning and Artificial Intelligence (AI) in developing First Principles Modeling of Materials and their Manufacturing. The workshop brings together researchers and experts in modeling and simulation, multiscale modeling, machine learning, informatics, data analytics, artificial intelligence for process design and manufacturing to discuss cross-cutting pathways, challenges, and opportunities. The goal is to determine and integrate error and uncertainty analysis into standard computational workflows, so that rigorous error determination and reporting become a standard norm for the materials modeling community. The workshop focuses on innovative pathways for new materials discovery, their design and manufacture, and identification of cross-cutting research opportunities. The workshop is organized into different sessions and includes talks by leading domestic and international experts discussing their latest research and identifying research gaps on topics such as (1) Error control for electronic structure, first principles, and quantum chemistry methods, (2) Best practices for modelers when working with experimentalists, (3) Error control at higher scales: interatomic potentials including machine learned error propagation in multiscale materials modeling, (4) Error control for materials synthesis, processing, and manufacture, (5) Incorporating error analysis into workflows and materials data repositories.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.