Collaborative Research: Accelerating the Discovery of Electronic Materials through Human-Computer Active Search

协作研究：通过人机主动搜索加速电子材料的发现

• 批准号: 1940175
• 负责人: Remco Chang
• 金额: $ 23.18万
• 依托单位: Tufts University
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940175&HistoricalAwards=false
• 关键词: Collaborative; Research; Accelerating; Discovery; Electronic

The overarching goal of this project is to accelerate the discovery of materials with tailored electronic properties through human-computer active search. These efforts will lay the groundwork for accelerating materials discovery, and advance the capability to control electronic properties in materials with the potential for profound societal impact. The thermoelectric and photocatalytic materials predicted, synthesized, and characterized in this research can realize societal advances in the space of energy and solar fuels. High-efficiency thermoelectric materials can revolutionize how heat sources are transformed into electrical power by eliminating the traditional intermediate mechanical energy conversions. Earth-abundant light-responsive catalysts are emerging as an alternative to costly, rare metal catalysts to store solar energy as portable liquid fuels, like ethanol. These green reactions are enabling low-cost, carbon-neutral fuels. The team brings together expertise in materials science, chemistry, machine learning, visualization, metadata, and knowledge frameworks to develop multi-fidelity, expert-guided active search strategies within materials science and chemistry. Resonances among the team's existing outreach programs will broaden inclusion of students from underrepresented groups and be moderated via the Alliance for Diversity in Science and Engineering. The work will provide cross-disciplinary training to graduate students and postdocs in all aspects of material informatics, including participating in and leading team efforts, co-mentorship of Ph.D. and postdoctoral researchers, inclusive symposia at national conferences, and a summer workshop focused on the intersection of visualization, machine learning, ontological engineering and materials science. Through enabling the acceleration of the discovery of new materials, this project supports the goals of the Materials Genome Initiative. An interdisciplinary team will create a search framework for scientific discovery that leverages recent advances in material databases, machine learning, visualization, human-machine interaction, and knowledge structures. To broadly assess the efficacy of this approach, the search effort will span the electronic behavior of both molecules and crystalline materials: (i) new organic photocatalysts for solar fuels production and (ii) new thermoelectric materials for electricity generation. Central to this effort is the engagement of domain experts and associated feedback in a human-in-the-loop active search process. Dynamic visualizations will enable the user to (i) understand the underlying reasons why the materials are being suggested and (ii) provide a user steering capability to identify and annotate specific aspects of the explored search space. Domain-expert annotations and feedback will be parsed against a suite of ontologies, further aiding the search process by providing relational insight between features. New molecules and materials will be explored through a combination of first principles calculations and high-throughput, automated experimentation; these results will be incorporated into a continually growing open-access database. Efficiently integrating and directing evolving data-streams from experiment, computation, and human steering during the search will be achieved with a multi-fidelity active search policy. Through enabling the acceleration of the discovery of new materials, this project supports the goals of the Materials Genome Initiative. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR 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.

该项目的首要目标是通过人机主动搜索加速发现具有定制电子特性的材料。这些努力将为加速材料发现奠定基础，并提高控制材料电子特性的能力，从而产生深远的社会影响。本研究预测、合成和表征的热电和光催化材料可以实现能源和太阳能燃料领域的社会进步。高效热电材料可以彻底改变热源转化为电能的方式，消除传统的中间机械能转换。地球上丰富的光反应催化剂正在成为昂贵的稀有金属催化剂的替代品，可以将太阳能储存为便携式液体燃料，如乙醇。这些绿色反应使低成本、碳中性的燃料成为可能。该团队汇集了材料科学、化学、机器学习、可视化、元数据和知识框架方面的专业知识，以开发材料科学和化学领域的多保真度、专家指导的主动搜索策略。团队现有的外展项目之间的共鸣将扩大来自代表性不足群体的学生的参与，并通过科学与工程多样性联盟进行调节。这项工作将为研究生和博士后提供材料信息学各个方面的跨学科培训，包括参与和领导团队工作，博士和博士后研究人员的共同指导，全国会议上的包容性专题讨论会，以及一个专注于可视化，机器学习，本体论工程和材料科学交叉的夏季研讨会。通过加速新材料的发现，该项目支持了材料基因组计划的目标。一个跨学科团队将为科学发现创建一个搜索框架，该框架将利用材料数据库、机器学习、可视化、人机交互和知识结构方面的最新进展。为了更广泛地评估这种方法的有效性，研究工作将跨越分子和晶体材料的电子行为：(i)用于太阳能燃料生产的新型有机光催化剂和（ii）用于发电的新型热电材料。这项工作的核心是领域专家的参与和在人在环主动搜索过程中的相关反馈。动态可视化将使用户能够(i)理解被建议的材料的潜在原因，（ii）提供用户指导能力，以识别和注释所探索的搜索空间的特定方面。领域专家的注释和反馈将针对一套本体进行解析，通过提供特征之间的关系洞察力进一步帮助搜索过程。新的分子和材料将通过第一性原理计算和高通量自动化实验的结合来探索；这些结果将被纳入一个不断增长的开放获取数据库。通过多保真度主动搜索策略，可以有效地集成和指导搜索过程中来自实验、计算和人工操纵的不断变化的数据流。通过加速新材料的发现，该项目支持了材料基因组计划的目标。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分，由HDR和美国国家科学基金会数学和物理科学理事会材料研究部共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs

CAVA：使用知识图进行探索性柱状数据增强的可视化分析系统

• DOI: 10.1109/tvcg.2020.3030443
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Cashman, Dylan;Xu, Shenyu;Das, Subhajit;Heimerl, Florian;Liu, Cong;Humayoun, Shah Rukh;Gleicher, Michael;Endert, Alex;Chang, Remco
• 通讯作者: Chang, Remco

Facilitating Exploration with Interaction Snapshots under High Latency

高延迟下的交互快照促进探索

• DOI: 10.1109/vis47514.2020.00034
• 发表时间: 2021
• 期刊: 2020 IEEE Visualization Conference (VIS
• 作者: Wu, Yifan;Chang, Remco;Hellerstein, Joseph M.;Wu, Eugene
• 通讯作者: Wu, Eugene

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

视觉验证与视觉估计：散点图中平均值的研究

• 发表时间: 2023
• 期刊: IEEE VIS Conference
• 作者: Daniel Braun, Ashley Suh

Kyrix-S: Authoring Scalable Scatterplot Visualizations of Big Data

• DOI: 10.1109/tvcg.2020.3030372
• 发表时间: 2020-07
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Wenbo Tao;Xinli Hou;Adam Sah;L. Battle;Remco Chang;M. Stonebraker

Automatic Y-axis Rescaling in Dynamic Visualizations

• DOI: 10.1109/vis49827.2021.9623319
• 发表时间: 2021-09
• 期刊: 2021 IEEE Visualization Conference (VIS)
• 作者: J. Fisher;Remco Chang;Eugene Wu