HDR Institute: Institute for Data Driven Dynamical Design

HDR 研究所：数据驱动动态设计研究所

• 批准号: 2118201
• 负责人: Eric Toberer
• 金额: $ 1554.07万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118201&HistoricalAwards=false
• 关键词: HDR; Institute; Data; Driven; Dynamical

From molecules to robots, designing for dynamics has common theoretical underpinnings despite differences in length and time scale. However, such research is often overwhelmed by the high dimensional design space. The Institute for Data-Driven Dynamical Design addresses the challenge of prediction of dynamical processes in materials, including ion and molecular transport, catalytic pathways, and phase transformations in metamaterials, with a focus on discovering fundamentally new mechanisms and pathways. This research represents a paradigm shift from traditional material efforts involving incremental improvements in ground-state and steady-state properties. Developments in the data sciences target (i) strategies for encoding complex structures and mechanistic pathways for machine intelligence, (ii) new predictive capabilities for evolving systems, and (iii) advances in visualization and integrating machine and human expertise. Fueling these data science developments are large-scale simulations of dynamical processes across high dimensional design spaces. Experimental validation of these large-scale simulations addresses both end-product prediction and mechanistic pathways therein. The Institute's data science innovations may advance fields both within and beyond STEM involving complex time-evolving systems including molecular biology, atmospheric science, geophysics, and physical cosmology. The Institute seeks to grow and unite the dispersed data-driven design community. Long-term growth is sought through outreach activities involving (i) high school coding schools, (ii) undergraduate involvement in data-rich research, and (iii) a post-baccalaureate bridge program that introduces students to data sciences and motivate them to pursue higher degrees. Data-driven design community activities include (i) interdisciplinary summer schools and workshops, (ii) a Fellows program to collaboratively grow and disseminate the Institute’s developments, and (iii) dedicated efforts to create open-source software for the design community. Throughout these efforts, the Institute actively seeks to recruit, retain, and graduate a diverse array of students in STEM. This virtual Institute seeks to design complex dynamical materials and structures through the union of machine and human intelligence. To learn dynamical behavior and ultimately discover new mechanisms, three core data science needs are addressed: (i) new representations and learning architectures that capture and encode the spatial arrangement, interactions, and temporal evolution of complex materials and geometrical structures, (ii) efficient exploration of high dimensional, time-dependent design spaces, and (iii) new visual analytics tools to quantitatively incorporate human-in-the-loop design feedback. Advances in each of these areas form a virtuous cycle that accelerates discovery of new materials, driven by new mechanisms. This Institute converges an interdisciplinary team focused on four design spaces at their `tipping point', where large quantities of dynamical data can be readily created: (i) crystalline solids with tailored ion transport for fuel cells and batteries, (ii) pressure-sensitive metamaterials for robotics, (iii) light driven catalytic reactions for chemical production, and (iv) synthesis and assembly of porous frameworks for chemical separations. These four areas are testbeds for cyberinfrastructure development for the broader scientific community. Interwoven throughout these activities are dedicated activities to build a new generation of STEM talent at the intersection of data science and the physical sciences/engineering and to broaden participation in STEM through targeted outreach.This project is part of the National Science Foundation's Big Idea activities in Harnessing the Data Revolution (HDR). The award by the Office of Advanced Cyberinfrastructure is jointly supported by the Divisions of Chemistry, Materials Research, and Mathematical Sciences within the NSF Directorate for 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.

从分子到机器人，尽管长度和时间尺度存在差异，但动力学设计具有共同的理论基础。然而，此类研究常常被高维设计空间淹没。数据驱动动态设计研究所解决了动态过程预测的挑战。这项研究代表了传统材料工作的范式转变，涉及基态和稳态的渐进式改进。数据科学的发展目标是（i）为机器智能编码复杂结构和机械路径的策略，（ii）不断发展的系统的新预测能力，以及（iii）可视化以及集成机器和人类专业知识的进步。科学发展是跨高维设计空间的动态过程的大规模模拟，这些大规模模拟的实验验证解决了其中的最终产品预测和机械路径，可能会推动涉及复杂的 STEM 内外的领域。该研究所致力于通过涉及 (i) 高中编码学校、 (ii) 本科生参与数据丰富的研究，以及 (iii) 向学生介绍数据科学并激励他们攻读更高学位的学士学位后桥梁课程，包括 (i) 跨学科暑期学校和研讨会。 , (ii) 研究员计划，以合作发展和传播研究所的发展成果，以及 (iii) 致力于为设计界创建开源软件。在这些努力中，研究所积极寻求招募、留住和培养多样化的人才。该虚拟学院旨在通过机器和人类智能的结合来设计复杂的动态材料和结构，以学习动态行为并最终发现新的机制，解决了三个核心数据科学需求：(i) 新的表示和学习。捕获和编码的架构复杂材料和几何结构的空间排列、相互作用和时间演化，(ii) 对高维、时间相关的设计空间的有效探索，以及 (iii) 新的视觉分析工具，以定量纳入人机交互设计反馈这些领域的进步形成了一个良性循环，在新机制的驱动下加速了新材料的发现。该研究所汇聚了一个跨学科团队，专注于四个处于“临界点”的设计空间，可以轻松创建大量动态数据。 : (i) 用于燃料电池和电池的具有定制离子传输的结晶固体，(ii) 用于机器人技术的压敏超材料，(iii) 用于化学生产的光驱动催化反应，以及 (iv) 用于化学分离的多孔框架的合成和组装。这四个领域是为更广泛的科学界开发网络基础设施的试验台，这些活动交织在一起，致力于在数据科学和物理科学/工程的交叉领域培养新一代 STEM 人才，并扩大对这些领域的参与。通过有针对性的推广来促进 STEM。该项目是美国国家科学基金会利用数据革命 (HDR) 的大创意活动的一部分，该奖项由高级网络基础设施办公室颁发，并得到了化学、材料研究和数学科学部门的共同支持。 NSF 数学和物理科学理事会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

用于预测数据交互和检测探索偏差的用户建模技术的统一比较

• DOI: 10.1109/tvcg.2022.3209476
• 发表时间: 2023
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Ha, Sunwoo;Monadjemi, Shayan;Garnett, Roman;Ottley, Alvitta
• 通讯作者: Ottley, Alvitta

Alloying-Induced Structural Transition in the Promising Thermoelectric Compound CaAgSb

• DOI: 10.1021/acs.chemmater.3c02621
• 发表时间: 2024-02
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: A. Shawon;Weeam Guetari;Kamil M Ciesielski;Rachel Orenstein;Jiaxing Qu;Sevan Chanakian;Md. Towhidur Rahman;Elif Ertekin;Eric Toberer;Alexandra Zevalkink

Exploring Pre-Trained Language Models to Build Knowledge Graph for Metal-Organic Frameworks (MOFs)

• DOI: 10.1109/bigdata55660.2022.10020568
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Yuan An;Jane Greenberg;Xiaohua Hu;Alexander Kalinowski;Xiao Fang;Xintong Zhao;Scott McClellan;F. Uribe-Romo;Kyle Langlois;Jacob Furst;Diego A. Gómez-Gualdrón;Fernando Fajardo-Rojas;Katherine Ardila;S. Saikin;Corey A. Harper;Ron Daniel

The Vendi Score: A Diversity Evaluation Metric for Machine Learning

• DOI: 10.48550/arxiv.2210.02410
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Dan Friedman;Adji B. Dieng

Simulating high-entropy alloys at finite temperatures: An uncertainty-based approach

在有限温度下模拟高熵合金：基于不确定性的方法

• DOI: 10.1103/physrevmaterials.7.063801
• 发表时间: 2023
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Novick, Andrew;Nguyen, Quan;Garnett, Roman;Toberer, Eric;Stevanović, Vladan
• 通讯作者: Stevanović, Vladan