Eight promising projects receive Schmidt Transformative Technology Fund awards

Eight innovative research projects recently received funding through the Eric and Wendy Schmidt Transformative Technology Fund — the largest number of annual awards in the fund’s history. The projects have potential to enable new capabilities and fundamental understandings across broad areas of science and engineering and involve faculty, researchers and graduate students from more than 10 Princeton departments and institutes. 

The goal of the fund is to enable researchers to make bold leaps rather than incremental advances in the natural sciences and engineering. It supports projects that lead to the invention of a disruptive new technology that can have a major impact on a field of research; the development of equipment or an enabling technology that will transform research in a field; or the innovative application of new technologies to solve complex research problems, open new avenues of inquiry, or significantly enhance the capabilities of existing research methodologies.

The fund was created in 2009 through a gift from Eric and Wendy Schmidt. Eric Schmidt is executive chairman and CEO of Relativity Space, co-founder of Schmidt Sciences, The Schmidt Family Foundation, and Schmidt Ocean Institute, the former chief executive officer of Google, and former executive chairman of Alphabet Inc., Google’s parent company. Wendy Schmidt is co-founder of Schmidt Sciences, and president and co-founder of The Schmidt Family Foundation and Schmidt Ocean Institute. Eric Schmidt earned his bachelor’s degree in electrical engineering from Princeton in 1976 and served as a Princeton trustee from 2004 to 2008.

“The research teams supported through the Schmidt fund this year are poised to create powerful new research tools and to use AI and machine learning to accelerate discovery,” said Princeton University Dean for Research Peter Schiffer. “The fund enables researchers across disciplines to take big swings, advance ambitious and exciting research, and even build new fields that benefit society.”

The eight projects highlighted below were selected for funding with the input of an anonymous panel of faculty reviewers. 

Accelerating twistable quantum materials design

• Leslie Schoop, professor of chemistry and director, Princeton Center for Complex Materials
• B. Andrei Bernevig, professor of physics

This interdisciplinary project focuses on “twistronics,” a process through which a slight twist between two-dimensional material layers leads to remarkable new quantum phenomena and the possibility of infinite new materials. Using an AI-guided approach, the researchers will create a periodic table of materials that could be twisted, investigate the feasibility of twisting them, and grow samples of all realistic twistable materials. Ultimately, they aim to establish Princeton University as the global hub for computational analyses and material growth: a place where researchers worldwide can access comprehensive predictions and calculations and obtain high-quality twisted materials for their experiments. 

Achieving economical proton-boron11 fusion

• Nathaniel Fisch, professor of astrophysical sciences

This project will explore a method for achieving what is considered the “holy grail” of controlled nuclear fusion: using proton-boron11 fusion to produce cleaner, safer and more economical energy. Doing so is extraordinarily challenging, given the ultrahigh temperatures required to achieve sufficient fusion reactions. The research team has theorized an approach that keeps boron ions and electrons relatively cool while efficiently generating hot protons to fuse with the boron, with a mechanism for recovering the energy. If proven possible, the concept would unlock a limitless source of sustainable power for the world.

Automating scientific discovery in the behavioral sciences

• Jonathan Cohen, Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience; professor of psychology and neuroscience; and associate director, Natural and Artificial Minds Initiative
• Thomas Griffiths, Henry R. Luce Professor of Information Technology, Consciousness, and Culture of Psychology and Computer Science and director, Princeton Lab for Artificial Intelligence 

This project aims to harness the power of artificial intelligence and the advantages of an online platform to further the study of human cognitive function. The research team will build on their previous advances, including an experimental design framework and computational tools for model design and behavioral data collection, to create an automated computational system that uses AI to articulate hypotheses, design experiments, deploy the experiments to collect data on the internet, and use the results to refine the hypotheses. The project involves two phases: first, the development of a closed-loop discovery system and second, the application of this system to the study of cognitive control. Ultimately, the goal is for this project to result in an automated, open-source system for behavioral discovery that can drive further advances in both cognitive science and AI systems.

Building a new platform to discover cyclic peptide drugs 

• A. James Link, professor of chemical and biological engineering

Peptides have emerged as a promising new modality in drug design, with applications in market-leading GLP-1 medications and in drugs under development to treat high cholesterol. This project builds on previous work with naturally occurring cyclic peptides with the aim of accelerating drug discovery. The research team will develop large libraries of peptide variants that can be screened against a variety of drug targets. They will test the properties of fuscimiditide (a cyclic peptide with a unique stem-loop structure that may be effective in targeting challenging protein-protein interactions) and use mass spectrometry techniques to search for other cyclic peptides that are amenable to engineering with this drug discovery platform. This approach could lay the groundwork for others in academia and in the pharmaceutical and biotech industries to discover new molecules that would serve as the starting point for new drugs. 

Developing an ultrafast microscope

• Aditya Sood, assistant professor of mechanical and aerospace engineering and the Princeton Materials Institute
• Barry Rand, professor of electrical and computer engineering and the Andlinger Center for Energy and the Environment 

Advances in nanoscience require a detailed understanding of the motion of charge, energy and matter with nanometer spatial and picosecond temporal resolution. The research team will develop a novel dynamic microscope that can capture short-lived processes in nano materials on ultrafast timescales. By integrating ideas from ultrafast science, bioimaging and non-linear optics, the team will build a multimodal imaging platform that can visualize the movement of electrons, heat and ions within operating devices. The proposed instrument has the potential to transform a broad range of technologies, including optoelectronics such as solar cells, computing and data storage devices, and batteries.   

Folding flows: Connecting origami and fluidics

• Glaucio Paulino, Margareta Engman Augustine Professor of Engineering, professor of civil and environmental engineering and the Princeton Materials Institute
• Howard A. Stone, Neil A. Omenn ’68 University Professor of Mechanical and Aerospace Engineering

Working at the intersection of origami engineering and fluid mechanics, the research team has developed the “folding flows” approach, a novel solution to the challenge of controlling fluid transport through morphing structures. The researchers will show how reconfiguration of origami lattices governs capillary pressure, permeability and fluid flow behaviors, and they will construct origami structures that control fluid directionality and enhance absorption and evaporation. By merging these structures with fluidic platforms, they aim to automate control of fluids at the microscale. These systems will have applications across fields, including energy, robotics, medicine, chemistry and environmental engineering. The theoretical models and design principles will provide an open-source framework for the research community, enabling cross-disciplinary innovation in engineering, materials science and applied physics.

Imaging and engineering the rhizosphere

• Joshua Shaevitz, professor of physics and the Lewis-Sigler Institute for Integrative Genomics and director, Graduate Program in Biophysics

This project will develop a system to transform the study of the rhizosphere, the narrow zone in soil surrounding plant roots. The rhizosphere is a highly dynamic environment where plant activity, microbial behavior and soil structure interact to regulate nutrient exchange, shaping agricultural productivity, soil fertility and global biogeochemical cycles. Understanding it requires studying three tightly coupled components: how plants reshape their surrounding soil, how microbes respond to these changes, and how soil structure and chemistry constrain these interactions. The research team will use a transparent soil system that closely mimics the structure of natural soils while allowing for direct three-dimensional imaging of root and microbial activity. Making the rhizosphere directly observable will shift the field from descriptive surveys toward quantitative, causal understanding and establish a platform in which key parameters — including soil structure, microbial movement and chemical gradients — can be systematically controlled and tested.

Learning the language of mass spectrometry to revolutionize proteomics quantification

• Martin Wühr, associate professor of molecular biology and the Lewis-Sigler Institute for Integrative Genomics
• Michael Skinnider, assistant professor of the Lewis-Sigler Institute for Integrative Genomics

This project aims to address the challenge of measuring absolute protein concentration in cells. Proteins play a vital role in virtually all cellular functions, but researchers have yet to develop a reliable method for measuring their concentration. The research team will use machine learning to develop a Bayesian generative model that accurately yields protein concentration from peptide signals in mass spectrometry data. This model will allow researchers to compare mass spectrometry results across different experiments, instruments and species, enabling better integration of proteomics with quantitative models in systems biology and translational research. It also has the potential to unlock the value of existing data; reanalyzing publicly available data and lab archives using this model could dramatically increase the return on decades of investment in proteomics while enabling direct, species-agnostic comparisons that are not currently possible.