NRT-DESE: Data-Enabled Discovery and Design of Energy Materials

NRT-DESE：基于数据的能源材料发现和设计

• 批准号: 1545403
• 负责人: Raymundo Arroyave
• 金额: $ 297.69万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545403&HistoricalAwards=false
• 关键词: NRT; DESE; Data; Enabled; Discovery

NRT-DESE: Data-Enabled Discovery and Design of Energy Materials (D3EM)Accelerating the discovery of new materials that enable transformative technologies is needed to transform the nation?s energy landscape. This National Science Foundation Research Traineeship (NRT) award will create and institutionalize a new training model at Texas A&M University that equips Master?s and doctoral students with the skills to advance research at the interface of materials science, informatics, and engineering design. The traineeship addresses the data-enabled science and engineering research priority theme and tackles three main challenges: 1) the need to accelerate materials discovery and development, particularly in energy-related technologies; 2) the need to instill in scientists and engineers the capability to transform data into knowledge, and use this knowledge to discover and design advanced materials; and 3) the need to educate scientists and engineers who internalize the interdisciplinary research process. The project anticipates preparing eighty (80) master?s and doctoral students, including forty-one (41) funded trainees, through an interdisciplinary curriculum in materials science, design, and informatics enriched with energy and entrepreneurship-related courses and activities. This traineeship will involve faculty from two colleges and six departments with expertise in materials science, engineering design, and informatics as well as in graduate education and curriculum development. The program will closely align the desired technical and professional skills, curriculum innovations, and learning outcomes. The pedagogical model will include mediated, relational, situated and transformative components. Collaboration will be embedded throughout the program, particularly in the development of learning communities, internships, and the capstone Materials Design Studio. This traineeship will equip graduate students with the necessary knowledge and experience, interdisciplinary focus, and technical and professional skills (including communication, collaboration, leadership) to thrive in a range of career options including industry, academia, and national research laboratories. It will also equip entrepreneurship-minded trainees with the knowledge and experience necessary to pursue deployment and commercialization of technologies generated from their research. The program will contain strong education research and rigorous evaluation components, which will enable refinement of effective and novel approaches and dissemination of the training model to the wider graduate education community. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

NRT-DESE：数据驱动的能源材料发现和设计（D3 EM）加速发现新材料，实现变革性技术是改变国家所必需的？的能源景观。这个国家科学基金会研究培训（NRT）奖将创造和制度化的一个新的培训模式，在得克萨斯州A M大学，装备硕士？在材料科学，信息学和工程设计的接口推进研究的技能，硕士和博士生。该培训课程涉及数据支持的科学和工程研究优先主题，并应对三个主要挑战：1）需要加速材料发现和开发，特别是在能源相关技术方面; 2）需要向科学家和工程师灌输将数据转化为知识的能力，并利用这些知识发现和设计先进材料;（3）需要培养科学家和工程师，使其将跨学科研究过程内化。该项目预计准备八十（80）硕士？s和博士生，包括四十一（41）资助的学员，通过材料科学，设计和信息学的跨学科课程，丰富了能源和实习相关的课程和活动。该培训将涉及来自两个学院和六个部门的教师，他们在材料科学，工程设计和信息学以及研究生教育和课程开发方面具有专业知识。该计划将紧密结合所需的技术和专业技能，课程创新和学习成果。教学模式将包括中介、关系、情境和变革组成部分。合作将嵌入整个计划，特别是在学习社区，实习和顶点材料设计工作室的发展。该实习将为研究生提供必要的知识和经验，跨学科重点以及技术和专业技能（包括沟通，协作，领导力），以在包括工业，学术界和国家研究实验室在内的一系列职业选择中茁壮成长。 它还将使具有研究意识的受训人员掌握必要的知识和经验，以便将他们的研究所产生的技术加以应用和商业化。该计划将包含强大的教育研究和严格的评估组成部分，这将使有效和新颖的方法和培训模式传播到更广泛的研究生教育界的细化。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆的，新的，潜在的变革性和可扩展的STEM研究生教育培训模型。该培训轨道致力于在高优先级的跨学科研究领域的STEM研究生的有效培训，通过全面的培训模式，是创新的，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

项目成果

Materials Design using an Active Subspace Batch Bayesian Optimization Approach

• DOI: 10.2514/6.2022-0075
• 发表时间: 2022-01
• 期刊: AIAA SCITECH 2022 Forum
• 作者: Danial Khatamsaz;D. Allaire

A differential evaporation model to predict chemistry change of additively manufactured metals

用于预测增材制造金属化学变化的微分蒸发模型

• DOI: 10.1016/j.matdes.2021.110328
• 发表时间: 2022
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Ranaiefar, Meelad;Honarmandi, Pejman;Xue, Lei;Zhang, Chen;Elwany, Alaa;Karaman, Ibrahim;Schwalbach, Edwin J.;Arroyave, Raymundo
• 通讯作者: Arroyave, Raymundo

Batch active learning for accelerating the development of interatomic potentials

• DOI: 10.1016/j.commatsci.2022.111330
• 发表时间: 2022-06
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Nathan Wilson;D. Willhelm;Xiaoning Qian;R. Arróyave;X. Qian

Thermodynamics of Wettability: A Physical Chemistry Laboratory Experiment

• DOI: 10.1021/acs.jchemed.2c00243
• 发表时间: 2022-06-28
• 期刊: JOURNAL OF CHEMICAL EDUCATION
• 影响因子: 3
• 作者: Davidson, Rachel D.;O'Loughlin, Thomas E.;Banerjee, Sarbajit
• 通讯作者: Banerjee, Sarbajit

Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

金属增材制造多重耦合仿真模型的贝叶斯校准：贝叶斯网络方法

• DOI: 10.1115/1.4052270
• 发表时间: 2022
• 期刊: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
• 作者: Ye, Jiahui;Mahmoudi, Mohamad;Karayagiz, Kubra;Johnson, Luke;Seede, Raiyan;Karaman, Ibrahim;Arroyave, Raymundo;Elwany, Alaa
• 通讯作者: Elwany, Alaa