Andrew J. Medford

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 17

Top Topics

Smoothness Constraint

Machine Learning

Temporal Analysis

Conformal Prediction

Top Venues

Mach. Learn. Sci. Technol.

Publications

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
SPARC v2.0.0: Spin-orbit coupling, dispersion interactions, and advanced exchange-correlation functionals. Softw. Impacts 20 (2024)
Gabriel S. Gusmão, Andrew J. Medford
Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems. Comput. Chem. Eng. 181 (2024)
Dingqi Nai, Gabriel S. Gusmão, Zachary A. Kilwein, Fani Boukouvala, Andrew J. Medford
Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks. CoRR (2024)
Mostafa Faghih Shojaei, John E. Pask, Andrew J. Medford, Phanish Suryanarayana
Soft and transferable pseudopotentials from multi-objective optimization. Comput. Phys. Commun. 283 (2023)
Adam C. Yonge, Gabriel S. Gusmão, Rebecca Fushimi, Andrew J. Medford
Model-based design of temporal analysis of products (TAP) reactors: A simulated case study in oxidative propane dehydrogenation. CoRR (2023)
Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary W. Ulissi, Matt Uyttendaele, Andrew J. Medford, David S. Sholl
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture. CoRR (2023)
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
AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification. J. Open Source Softw. 8 (87) (2023)
Gabriel S. Gusmão, Andrew J. Medford
Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems. CoRR (2023)
Yuge Hu, Joseph Musielewicz, Zachary W. Ulissi, Andrew J. Medford
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials. Mach. Learn. Sci. Technol. 3 (4) (2022)
Xiangyun Lei, Andrew J. Medford
A Universal Framework for Featurization of Atomistic Systems. CoRR (2021)
M. Ross Kunz, Adam Yonge, Rakesh Batchu, Zongtang Fang, Yixiao Wang, Gregory Yablonsky, Andrew J. Medford, Rebecca Fushimi
A Priori Calibration of Transient Kinetics Data via Machine Learning. CoRR (2021)
Qimen Xu, Abhiraj Sharma, Benjamin Comer, Hua Huang, Edmond Chow, Andrew J. Medford, John E. Pask, Phanish Suryanarayana
SPARC: Simulation Package for Ab-initio Real-space Calculations. SoftwareX 15 (2021)
Christopher Rose, Andrew J. Medford, C. Franklin Goldsmith, Tejs Vegge, Joshua Weitz, Andrew A. Peterson
Population Susceptibility Variation and Its Effect on Contagion Dynamics. CoRR (2020)
Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha, Andrew J. Medford
Kinetics-Informed Neural Networks. CoRR (2020)
Adam Yonge, M. Ross Kunz, Rakesh Batchu, Zongtang Fang, Tobin Issac, Rebecca Fushimi, Andrew J. Medford
TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments. CoRR (2020)
Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features. CoRR (2019)
Xiangyun Lei, Fred Hohman, Duen Horng (Polo) Chau, Andrew J. Medford
ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. IEEE VIS (Short Papers) (2019)