ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. - Xiangyun Lei; Fred Hohman; Duen Horng (Polo) Chau; Andrew J. Medford (IEEE VIS (Short Papers), 2019)

TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments. - Adam Yonge; M. Ross Kunz; Rakesh Batchu; Zongtang Fang; Tobin Issac; Rebecca Fushimi; Andrew J. Medford (CoRR, 2020)

Population Susceptibility Variation and Its Effect on Contagion Dynamics. - Christopher Rose; Andrew J. Medford; C. Franklin Goldsmith; Tejs Vegge; Joshua Weitz; Andrew A. Peterson (CoRR, 2020)

Kinetics-Informed Neural Networks. - Gabriel S. Gusmão; Adhika P. Retnanto; Shashwati C. da Cunha; Andrew J. Medford (CoRR, 2020)

A Universal Framework for Featurization of Atomistic Systems. - Xiangyun Lei; Andrew J. Medford (CoRR, 2021)

A Priori Calibration of Transient Kinetics Data via Machine Learning. - M. Ross Kunz; Adam Yonge; Rakesh Batchu; Zongtang Fang; Yixiao Wang; Gregory Yablonsky; Andrew J. Medford; Rebecca Fushimi (CoRR, 2021)

SPARC: Simulation Package for Ab-initio Real-space Calculations. - Qimen Xu; Abhiraj Sharma; Benjamin M. Comer; Hua Huang; Edmond Chow; Andrew J. Medford; John E. Pask; Phanish Suryanarayana (SoftwareX, 2021)

Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials. - Yuge Hu; Joseph Musielewicz; Zachary W. Ulissi; Andrew J. Medford (Mach. Learn. Sci. Technol., 2022)

Model-based design of temporal analysis of products (TAP) reactors: A simulated case study in oxidative propane dehydrogenation. - Adam C. Yonge; Gabriel S. Gusmão; Rebecca Fushimi; Andrew J. Medford (CoRR, 2023)

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture. - Anuroop Sriram; Sihoon Choi; Xiaohan Yu; Logan M. Brabson; Abhishek Das; Zachary W. Ulissi; Matt Uyttendaele; Andrew J. Medford; David S. Sholl (CoRR, 2023)

Soft and transferable pseudopotentials from multi-objective optimization. - Mostafa Faghih Shojaei; John E. Pask; Andrew J. Medford; Phanish Suryanarayana (Comput. Phys. Commun., 2023)

AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification. - Muhammed Shuaibi; Yuge Hu; Xiangyun Lei; Benjamin M. Comer; Matt Adams; Jacob Paras; Rui Qi Chen; Eric Musa; Joseph Musielewicz; Andrew A. Peterson; Andrew J. Medford; Zachary W. Ulissi (J. Open Source Softw., 2023)

Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks. - Dingqi Nai; Gabriel S. Gusmão; Zachary A. Kilwein; Fani Boukouvala; Andrew J. Medford (CoRR, 2024)

SPARC v2.0.0: Spin-orbit coupling, dispersion interactions, and advanced exchange-correlation functionals. - Boqin Zhang; Xin Jing; Qimen Xu; Shashikant Kumar; Abhiraj Sharma; Lucas Erlandson; Sushree Jagriti Sahoo; Edmond Chow; Andrew J. Medford; John E. Pask; Phanish Suryanarayana (Softw. Impacts, 2024)

Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems. - Gabriel S. Gusmão; Andrew J. Medford (Comput. Chem. Eng., 2024)

Spectral scheme for atomic structure calculations in density functional theory. - Sayan Bhowmik; John E. Pask; Andrew J. Medford; Phanish Suryanarayana (Comput. Phys. Commun., 2025)

SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations. - Tian Tian; Lucas R. Timmerman; Shashikant Kumar; Benjamin M. Comer; Andrew J. Medford; Phanish Suryanarayana (J. Open Source Softw., 2025)