Interdisciplinary Training in Data Driven Soft Materials Research and Science Policy

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.