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.. index:: research

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Research and software
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Overview
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I am excited to showcase two research projects prior to joining Stanford University. These projects remain on-going. They advance scientific software development and materials informatics under the braod themes of open science and reproducibility.

Project 1: Enabling scientists to share useful code
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Working with Prof. Simon Billinge, I led a project helping scientists share useful code with the broader scientific community. The project, **scikit-package**, provides a pedagogical framework for scientists to write and share code to maximize impact.

- **Paper**: https://arxiv.org/abs/2507.03328
- **Getting started guide**: https://scikit-package.github.io/scikit-package
- **GitHub**: https://github.com/scikit-package/scikit-package

Academic software developed using scikit-package
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- PDFfit2 and PDFgui: Computer programs for studying nanostructure in crystals (`J. Phys. Condens. Matter <https://doi.org/10.1088/0953-8984/19/33/335219>`__)

- cifkit: A Python package for coordination geometry and atomic site analysis (`JOSS <https://doi.org/10.21105/joss.07205>`__)

- Composition and structure analyzer/featurizer for explainable machine learning models to predict solid state structures (`Digital Discovery <https://doi.org/10.1039/D4DD00332B>`__)

- Stretched non-negative matrix factorization (`npj Comput. Mater. <https://doi.org/10.1038/s41524-024-01377-5>`__)

- Real-space texture and pole-figure analysis using the 3D pair distribution function on a platinum thin film (`IUCrJ <https://doi.org/10.1107/S2052252522006674>`__)


Project 2: Access to physics-chemistry elemental data
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Working with Prof. Anton Oliynyk, I developed machine learning models using an elemental-compositional database for materials informatics. These models enable prediction and discovery of novel materials with targeted properties.

- **Paper**: https://doi.org/10.1016/j.dib.2024.110178
- **Python API**: https://bobleesj.github.io/bobleesj.utils

Papers published using OLED (Oliynyk Elemental Data)
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- Copper Gallium Aluminum mixed metal oxides as alternative catalyst candidates for efficient conversion of carbon dioxide to methanol and dimethyl ether (`ACS Catal. <https://pubs.acs.org/doi/10.1021/acscatal.5c03876>`__)

- Design and implementation of sintered NdFeB performance prediction system based on machine learning (`ISRIMT 2024 <https://doi.org/10.1109/ISRIMT63979.2024.10875235>`__)

- Multi-objective optimization of material properties for enhanced battery performance using artificial intelligence (`Expert Syst. Appl. <https://doi.org/10.1016/j.eswa.2025.128179>`__)

- Machine learning based investigation of atomic packing effects: chemical pressures at the extremes of intermetallic complexity (`JACS <https://pubs.acs.org/doi/10.1021/jacs.4c10479>`__)

- Machine learning predictions of thermopower for thermoelectric material screening (`ACS Appl. Energy Mater. <https://doi.org/10.1021/acsaem.5c02609>`__)

- CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications (`J. Mater. Res. <https://doi.org/10.1557/s43578-024-01489-0>`__)

- Machine learning assisted discovery of Cr³⁺-based near-infrared phosphors (`Chem. Mater. <https://doi.org/10.1021/acs.chemmater.5c01208>`__)

- Thermoelectric material performance (zT) predictions with machine learning (`ACS Appl. Mater. Interfaces <https://pubs.acs.org/doi/10.1021/acsami.4c19149>`__)

- Explainable recommendation engines to predict complex intermetallics: synthesis and characterization of Gd₁₀RuCd₃, a neutron absorption material (`JACS <https://pubs.acs.org/doi/10.1021/jacs.5c11646>`__)

What am I working on now?
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With Prof. Colin Ophus (https://colab.stanford.edu/) at Stanford, I am working on GPU accelerated algorithms for electron microscopy and ptychography. The goal is to enable real-time imaging at atomic resolution.