Funding

Our Approach to Funding

The CoReACTER needs to maintain sufficient funding to continue operation. The funding process is morally fraught, and there are very few options for sustainable research funding that do not require some kind of ethical compromise. Understanding this, we are generally open to any funding source, though we try to move towards funding sources that are as aligned with our principles as possible.

There are, however, some areas that we will not consider because they are radically opposed to our principles, the good of the scientific community, and/or the wellbeing of society. The CoReACTER will not pursue or accept funding or other resources:

• From military sources (e.g., the United States Department of Defense), organizations whose major product or service relates to military technologies (i.e., members of the military-industrial complex), or from projects with direct military applications

• For projects that will further the extraction and utilization of fossil fuels, or that otherwise are likely to significantly increase greenhouse gas emissions and/or accelerate anthropogenic climate change

• That impede us from sharing our finding freely and openly, or that limit our ability to effectively collaborate

We note that "other resources" includes computational resources. This means that we will not, for instance, pursue access to or access supercomputers owned and operated by the Department of Defense.

Current Funding Sources

Synthesis Advanced Research Challenge, Toyota Research Institute (2024–2026) Project: Direct Introduction of Competition and Kinetics to Materials Mechanism and Reaction Network Prediction Description: Solid-state synthesis continues to be driven by trial-and-error experimentation, with no coherent design rules or underlying theory. Though there has been considerable interest in predicting the outcomes of solid-state reactions and automating the selection of precursors and synthesis conditions, most approaches developed to date rely entirely on bulk thermodynamics, ignoring the kinetics of nucleation and growth. Our proposed work provides a new approach for predictive materials synthesis, combining machine learning, molecular dynamics simulations, and chemical reaction networks to calculate solid-state reaction kinetics, rationally explain synthesis outcomes, and select precursors that are likely to lead to efficient formation of desired product phases.

Faculty Startup Funding, Carnegie Mellon University Department of Chemical Engineering (2025–2028) Project: TO BE DETERMINED Description: We initially intend to use this funding for two projects: one related to multiscale modeling in electrochemistry, and one using high-throughput experiments to study polymer recycling.

Previous Funding Sources