Jason Hattrick-Simpers

According to our database, Jason Hattrick-Simpers authored at least 17 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Building informative materials datasets beyond targeted objectives.

Rafael Espinosa Castañeda, Ashley S. Dale, Hongchen Wang, Yonatan Kurniawan, Hao Wan, Runze Zhang, Adji Bousso Dieng, Kangming Li, Jason Hattrick-Simpers

CoRR, May, 2026

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data.

Ali Jaberi, Yonatan Kurniawan, Robert Black, Shayan S. Mousavi Masouleh, Kabir Verma, Zoya Sadighi, Santiago Miret, Jason Hattrick-Simpers

CoRR, April, 2026

2025

Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores.

Benhour Amirian, Ashley S. Dale, Sergei Kalinin, Jason Hattrick-Simpers

CoRR, December, 2025

Training-Free Active Learning Framework in Materials Science with Large Language Models.

Hongchen Wang, Rafael Espinosa Castañeda, Jay Werber, Yao Fehlis, Edward Kim, Jason Hattrick-Simpers

CoRR, November, 2025

High-throughput validation of phase formability and simulation accuracy of Cantor alloys.

Changjun Cheng, Daniel Persaud, Kangming Li, Michael J. Moorehead, Natalie Page, Christian Lavoie, Beatriz Diaz Moreno, Adrien Couet, Samuel E. Lofland, Jason Hattrick-Simpers

CoRR, November, 2025

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem.

Ashley S. Dale, Kangming Li, Brian DeCost, Hao Wan, Yuchen Han, Yao Fehlis, Jason Hattrick-Simpers

CoRR, November, 2025

Assessment of different loss functions for fitting equivalent circuit models to electrochemical impedance spectroscopy data.

Ali Jaberi, Amin Sadeghi, Runze Zhang, Zhaoyang Zhao, Qiuyu Shi, Robert Black, Zoya Sadighi, Jason Hattrick-Simpers

CoRR, October, 2025

Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes.

Hongchen Wang, Sima Zeinali Danalou, Jiahao Zhu, Kenneth Sulimro, Chaewon Lim, Smita Basak, Aimee Tai, Usan Siriwardana, Jason Hattrick-Simpers, Jay Werber

CoRR, August, 2025

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization.

Shayan S. Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Cara Cronin, Jason E. Hein, Jason Hattrick-Simpers

CoRR, July, 2025

Exploring the Frontiers of kNN Noisy Feature Detection and Recovery for Self-Driving Labs.

Qiuyu Shi, Kangming Li, Yao Fehlis, Daniel Persaud, Robert Black, Jason Hattrick-Simpers

CoRR, July, 2025

AutoEIS: Automated equivalent circuit modeling from electrochemical impedance spectroscopy data using statistical machine learning.

Mohammad Amin Sadeghi, Runze Zhang, Jason Hattrick-Simpers

J. Open Source Softw., May, 2025

Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel.

Yutong Liu, Mehrad Ansari, Robert Black, Jason Hattrick-Simpers

CoRR, March, 2025

LLM4Mat-bench: benchmarking large language models for materials property prediction.

Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers, Adji Bousso Dieng

Mach. Learn. Sci. Technol., 2025

High- T c superconductor candidates proposed by machine learning.

Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim, O. Anatole von Lilienfeld

Mach. Learn. Sci. Technol., 2025

2024

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions.

Hongchen Wang, Kangming Li, Scott Ramsay, Yao Fehlis, Edward Kim, Jason Hattrick-Simpers

CoRR, 2024

Accurate predictions of keyhole depths using machine learning-aided simulations.

Jiahui Zhang, Runbo Jiang, Kangming Li, Pengyu Chen, Xiao Shang, Zhiying Liu, Jason Hattrick-Simpers, Brian J. Simonds, Qianglong Wei, Hongze Wang, Tao Sun, Anthony D. Rollett, Yu Zou

CoRR, 2024

2020

Scientific AI in materials science: a path to a sustainable and scalable paradigm.

Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt, Aaron Gilad Kusne, Eva Campo, Martin Green

Mach. Learn. Sci. Technol., 2020