Interdisciplinary Training in Data Driven Soft Materials Research and Science Policy

软材料是那些在室温下容易因材料温度的变化而变形的材料。例子包括泡沫、凝胶和许多生物材料。由于研究对象的复杂性，软材料研究的进展越来越难以用传统的方法来实现。与此同时，越来越需要开发新的软材料来解决紧迫的社会问题，其中许多是与环境和能源相关的。从策略的角度来看，相关的技术实现环境也很复杂。对于在实验室之外工作、为社会提供技术解决方案的受过传统训练的科学家来说，驾驭它们尤其具有挑战性。这些问题突出了如何训练学生进行以学科为中心的软材料研究和解决与这些研究成果相关的社会问题的关键差距。NSF研究实习生（NRT）奖将通过实施一种新的研究生教育模式来解决这些差距，该模式使受训者能够利用由机器学习驱动的数据密集型战略进行软材料研究，并使他们能够通过科学政策在实验室之外做出有影响力的贡献。该项目将为预计50名博士生（20名nrt资助）和40名硕士生提供独特的培训机会，以开发和实践解决高复杂性软材料研究和通过科学政策理解技术的社会影响的新范式。此外，该项目将建立一个多元化、包容性和充满活力的学者社区，在内部和外部导师的网络中工作。该项目的研究人员旨在培养学术界、工业界和政府的毕业生领袖，在这些领域，利用数据和整合多学科知识的能力至关重要。两个目标指导该项目的实习经历：(1)帮助学生有效地利用数据密集型策略来促进复杂软材料系统的突破；(2)使学生能够整合来自多学科的信息和来自多个系统响应的综合效应，以有效地应对棘手的科学和社会挑战。第一个目标是通过一种跨学科的研究方法来实现，这种方法需要计算机科学（机器学习、数据科学）和传统跨学科方法（物理、化学、工程、生物学）的融合，目前用于研究软材料。第二个目标是通过科学政策方面的正式培训和体验式学习来实现，使学生具备工具（例如，思维过程、团队合作、跨学科方法），以便在融合研究中茁壮成长，并有效地解决涉及科学和技术的重要社会问题。该NRT项目包括三个主要研究领域：自主实验、胶体和大分子材料、活体软材料。潜在的贡献包括数据密集型方法的进步，自主实验作为软材料研究的一种发现模式，以及在信息论，控制论和材料科学的界面上发展新概念。该项目将实施定制的跨学科课程和相关证书项目，利用最佳实践来监测和评估学员和其他利益相关者的学术成果。该奖项为行业、政府和学术界的学员提供身临其境的体验。此外，该项目的目标是招募妇女和代表性不足的群体成员参与STEM研究领域，作为建立多元化社区的主要目标。为了实现这一目标，该项目的团队将建立一个为少数民族服务的机构网络，用于研究交流、以学生为中心的训练营和培训生招聘。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。

Soft materials are those which at room temperature are easily deformed by changes to the temperature of the material. Examples include foams, gels, and many biological materials. Due to the complexity of the objects under study, advances in soft materials research are becoming more challenging to achieve using traditional approaches. At the same time, there is a growing need to develop new soft materials designed to address pressing societal concerns, many of which are environment- and energy-related. The associated technology implementation landscapes are also complex from a policy perspective. They are particularly challenging to navigate for classically trained scientists working beyond the lab to provide technology solutions for society. These issues highlight critical gaps in how students are trained to undertake both discipline-centered soft materials research and to tackle societal issues associated with the products of such research. This NSF Research Traineeship (NRT) award will address these gaps by implementing a new graduate education model that equips trainees to leverage data-intensive strategies driven by machine-learning for soft materials research and positions them to make impactful contributions beyond the lab through science policy. The project will provide a unique training opportunity for an anticipated 50 Ph.D. students (20 NRT-funded) and 40 Master’s students to develop and exercise a new paradigm for tackling high complexity soft materials research and understanding societal implications of technology through science policy. In addition, the project will build a diverse, inclusive, and vibrant community of scholars working within a network of internal and external mentors. The project’s investigators aim to graduate leaders in academia, industry, and government, where the ability to harness data and integrate knowledge from multiple disciplines is critical. Two objectives guide the project’s traineeship experiences: (1) help students effectively harness data-intensive strategies to facilitate breakthroughs in complex soft material systems and (2) enable students to integrate information from multiple disciplines and the combined effects from multiple system responses, to effectively confront vexing scientific and societal challenges. The first objective is being met through a trans-disciplinary research approach entailing the convergence of Computer Science (machine learning; data science) and Autonomous Experimentation with the traditional inter-disciplinary approaches (Physics, Chemistry, Engineering, Biology) currently used to study soft materials. The second objective is being attained through formal training and experiential learning in science policy to equip students with tools (e.g., thought processes, teamwork, transdisciplinary approaches) to thrive in convergence research and effectively address important societal issues involving science and technology. This NRT project encompasses three major research areas: Autonomous Experimentation, Colloidal and Macromolecular Materials, and Living Soft Materials. Potential contributions include the advancement of data-intensive methodologies, autonomous experimentation as a mode of discovery in soft materials research, and the development of new concepts at the interface of information theory, control theory, and materials science. The project will implement a custom-designed trans-disciplinary curriculum and associated certificate program that use best practices to monitor and evaluate academic outcomes that are responsive to trainees and other stakeholders. This award supports immersive experiences for trainees in industry, government, and academia. Furthermore, the project targets the recruitment of women and members of groups underrepresented in their participation in STEM fields of study as a major goal of building a diverse community. Towards this goal, the project's team will develop a network of minority-serving institutions for research exchanges, student-focused bootcamps, and trainee recruitment.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.