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.
| | School of Chemistry Startup Funding, University College Dublin (2026–2030)
Project: Multi-Scale Modelling of Metal-Ion Battery Dynamics
Description: Batteries are extremely complex, with important processes occurring at the negative electrode, positive electrode, and electrolyte over a wide range of length (nm to mm) and time scales (ps to hours). Extending EWCSS' previous work in multi-scale modelling of solid-electrolyte interphase (SEI) formation, we are interested in developing full-cell models of metal-ion batteries, considering not only SEI formation but oxidative decomposition on the positive electrode, electrode decomposition (e.g., transition metal dissolution, oxygen evolution), and transport of decomposition products through the electrolyte. PhD student Anna Giraldo Neira will lead this project, which will involve coupling atomistic simulations with continuum-scale multiphysics models. |
| | European Research Council Proposal Preparation Support, Enterprise Ireland (2026–2028)
Project: Directed Hypergraph Representations for Network Analysis and Machine Learning on Chemical Reaction Networks
Description: This project through Enterprise Ireland supports our upcoming application to the European Research Countil Starting Grant. Shonali Dixit, funded through this grant, will be developing software for hypergraph machine learning which we hope to eventually deploy to study chemical reaction networks. |
| | Ad Astra Fellow Startup Funding, University College Dublin (2026–2030)
Project: Leveraging Directed Hypergraphs to Analyze Chemical Reaction Networks
Description: Chemical reaction networks (CRNs) are collections of interrelated species and reactions. Traditionally, theoretical and practical investigations of CRNs have focused on systems of coupled differential equations describing the time evolution of species concentrations. However, more recently, methods leaning on graph theory and network science have attracted increasing attention. In this project, led by PhD student Zhenya Barannik, we will represent CRNs as directed hypergraphs, which can naturally model many-to-many relations like chemical reactions, and use recent developments in directed hypergraph theory to study the structure and dynamics of CRNs. |
| | 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. |
| | Zulip
Description: Zulip is an organized team chat app designed for efficient communication. The CoReACTER is sponsored as an educational non-profit organization. |
