A computational scientist with a background in chemical engineering. I started research during my Master's degree for the Dow Chemical Company, where I collected experimental kinetic data and developed multiphase reactor engineering models using Bayesian statistics. For my Ph.D., I joined the research groups of Professors Sharon Glotzer and Nicholas Kotov to develop physics-informed, graph-based models for property predictions for complex materials [1-3]. I later released the code I developed as an open-source Python API, along with conda-distributed binaries, documentation, and Jupyter Notebook tutorials [4]. I continue to maintain the code as a developer for the Center for Complex Particle Systems, where the code is hosted.
Additional research projects of mine include
- ParQC: GPU phason strain algorithms for constructing the hyperlattice matrix from molecular simulations.
- NetHP: Graph Markov Chain Monte Carlo algorithms for simulating assembly of complex chiral nanodendrimers.
Outside of my own projects, I contribute to the Glotzer Group software stack and any other open-source repositories that I can identify improvements for.
[1] Wu, W., Kadar, A., et al., “Layer-by-Layer Assembled Nanowire Networks Enable Graph Theoretical Design of Multifunctional Coatings”, Matter, 2025 vol. 8, no. 1, pp. 101870.
[2] Kuznetsova, V.A., Kadar, A., et al., “Graph–Property Relationships for Complex Chiral Nanodendrimers”, ACS Nano, 2025, vol. 19, no. 6, pp. 6095.
[3] Reyez-Martinez, M.A., Kadar, A., et al., “Graph-Theoretical Description and Continuity Problems for Stress Propagation Through Complex Strut Lattices”, npj Soft Matter, 2025, vol. 1, no. 3.
[4] Kadar, A., et. al., “StructuralGT: A Python API for graph-based material network design”, Journal of Open Source Software, in review.