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Andrew J. Medford
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2020 – today
- 2024
- [j6]Gabriel S. Gusmão, Andrew J. Medford:
Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems. Comput. Chem. Eng. 181: 108547 (2024) - [j5]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: 100649 (2024) - [i10]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 abs/2406.13904 (2024) - 2023
- [j4]Mostafa Faghih Shojaei, John E. Pask, Andrew J. Medford, Phanish Suryanarayana:
Soft and transferable pseudopotentials from multi-objective optimization. Comput. Phys. Commun. 283: 108594 (2023) - [j3]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): 5035 (2023) - [i9]Gabriel S. Gusmão, Andrew J. Medford:
Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems. CoRR abs/2304.05991 (2023) - [i8]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 abs/2309.15786 (2023) - [i7]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 abs/2311.00341 (2023) - 2022
- [j2]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): 45028 (2022) - 2021
- [j1]Qimen Xu, Abhiraj Sharma, Benjamin M. Comer, Hua Huang, Edmond Chow, Andrew J. Medford, John E. Pask, Phanish Suryanarayana:
SPARC: Simulation Package for Ab-initio Real-space Calculations. SoftwareX 15: 100709 (2021) - [i6]Xiangyun Lei, Andrew J. Medford:
A Universal Framework for Featurization of Atomistic Systems. CoRR abs/2102.02390 (2021) - [i5]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 abs/2109.15042 (2021) - 2020
- [i4]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 abs/2008.13584 (2020) - [i3]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 abs/2009.09150 (2020) - [i2]Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha, Andrew J. Medford:
Kinetics-Informed Neural Networks. CoRR abs/2011.14473 (2020)
2010 – 2019
- 2019
- [c1]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: 196-200 - [i1]Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford:
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features. CoRR abs/1908.08381 (2019)
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last updated on 2024-10-07 22:09 CEST by the dblp team
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