Graduate Traineeship on Advances in Materials Science using Machine Learning

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。应对社会的重大挑战将需要一支准备好整合多学科知识的劳动力，作为一个团队工作，并利用不同背景的个人的智力贡献。然而，雇主经常发现，进入职场的研究生缺乏跨学科培训和对数据科学的理解。这些情况需要STEM研究生教育项目的新设计，以促进跨学科团队建设，将数据科学与解决方案方法相结合，并使研究生接触到多元文化的工作环境。这项授予阿克伦大学（UA）的国家科学基金会研究培训（NRT）奖将支持俄亥俄州东北部的第一个此类培训计划，该计划面向在材料科学和机器学习界面工作的硕士和博士生，该计划将导致新材料的发现和材料研究的重大进展。该实习项目将为40名硕士和博士学生提供服务，其中包括20名获得资助的硕士和博士学生，他们来自不同的背景，包括代表性不足的少数群体，并就读于高分子科学和高分子工程、机械工程和计算机科学的研究生课程。该NRT计划旨在识别和减轻知识差距，同时为广泛的材料研究界感兴趣的几个跨学科研究问题提供数据驱动的答案。例子包括i)材料模量随温度的可调性和通过增材制造生产的复合部件的质量一致性，ii)离子动力学和离子在晶体聚合物领域中的传输机制，如在电池中，iii)具有可调热性能和机械性能的化学可回收聚合物的分子设计；Iv)当前机器学习工具中的新特征选择技术，用于处理来自大型复杂聚合物系统的数据；v)高效、系统地集成来自多个来源的大规模异构数据。该研究将利用实验和数值方法的结合，包括分子动力学模拟、有限元分析、聚合物化学合成、增材制造技术和机器学习工具，以实现预期的目标。该项目将创建一个可扩展的材料数据库，可与现有的聚合物存储库集成，可用于开发新型复合材料，具有优异导电性的固体电解质的分子设计规则，以及可有效回收的新型聚合物材料的设计。学员将通过课程和研究工作、讲习班和研讨会、实验室轮岗、联邦和工业实验室实习以及国际接触，培养强大的跨学科技能。他们将获得数据科学工程研究生证书，并有望成为材料研究中数字化工具的有力支持者。学员还将参加高中和两年制大学的外展和辅导活动，以增强STEM领域的多样性。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Tackling society’s grand challenges will require a workforce that is prepared to work to integrate knowledge from multiple disciplines, to work as a team, and to leverage the intellectual contributions of individuals from diverse backgrounds. However, employers often find that graduate students entering the workforce lack interdisciplinary training and an appreciation for data science. These scenarios necessitate new designs of STEM graduate education programs that promote interdisciplinary team building, integration of data science in solution methodologies, and exposure of graduate students to multicultural work environments. This National Science Foundation Research Traineeship (NRT) award to The University of Akron (UA) will support the first such traineeship program in Northeast Ohio for masters and doctoral students working at the interface of materials science and machine learning that will lead to the discovery of new materials and major advances in materials research. The traineeship will serve 40 M.S. and Ph.D. students including 20 funded M.S. and Ph.D. students recruited from diverse backgrounds including underrepresented minority groups and enrolled in graduate programs in polymer science and polymer engineering, mechanical engineering, and computer science.This NRT program seeks to identify and mitigate the knowledge gaps while providing data-driven answers for several cross-disciplinary research questions that are of interest to the broad materials research community. Examples include i) the tunability of a material modulus with temperature and the quality consistency of composite parts produced via additive manufacturing, ii) the dynamics of ions and mechanisms of ion transport in crystalline polymer domains as in batteries, iii) the molecular design of chemically recyclable polymers with tunable thermal and mechanical properties; iv) novel feature selection techniques in current machine learning tools to handle data from large complex polymeric systems; and v) efficient and systematic integration of large-scale heterogeneous data from multiple sources. The research will utilize a combination of experimental and numerical approaches, including molecular dynamics simulation, finite element analysis, polymer chemistry synthesis, additive manufacturing techniques and machine learning tools, to achieve the desired objectives. This project will create a scalable materials database that is integrable with existing polymer repositories and can be used to develop novel composite materials, molecular design rules for solid electrolytes with superior conductivity, and the design of new polymer materials that can be efficiently recycled. The trainees will develop strong interdisciplinary skills via courses and research work, workshops and seminars, laboratory rotations, internships at federal and industrial laboratories, and international exposure. They will receive a Data Science Engineering graduate certificate and are expected to become strong proponents of digitalization tools in materials research. The trainees will also participate in outreach and mentoring activities at high schools and two-year colleges to enhance diversity in STEM fields.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.