137 citations · 440 across the 16 of their papers we have counts for
17 papers
Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis
Shehroz Ahmad Shoaib, Kangming Li
Final predictive accuracy is the standard basis for comparing graph neural networks (GNNs) in materials-property prediction, but it does not show how strongly performance depends o…
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes proper…
High-throughput validation of phase formability and simulation accuracy of Cantor alloys
Changjun Cheng, Daniel Persaud, Kangming Li +7
High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast composit…
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Ashley S. Dale, Kangming Li, Brian DeCost +4
Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty…
Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
Qiuyu Shi, Kangming Li, Yao Fehlis +4
Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the…
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Hongchen Wang, Kangming Li, Scott Ramsay +3
Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently exp…
An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy
Runze Zhang, Debashish Sur, Kangming Li +5
Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate e…
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost +2
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…
Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
Jiahui Zhang, Runbo Jiang, Kangming Li +11
The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced d…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy
Runze Zhang, Robert Black, Debashish Sur +5
Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-sourc…
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…
Magnetochemical coupling effects on thermodynamics, point-defect formation and diffusion in Fe-Ni alloys: a theoretical study
Kangming Li
This thesis is a theoretical study of thermodynamic, point-defect formation and diffusion properties in Fe-Ni alloys with a focus on the magnetochemical effects. The results are de…
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian DeCost, Kamal Choudhary +2
Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score…
Predicting magnetization of ferromagnetic binary Fe alloys from chemical short range order
Van-Truong Tran, Chu-Chun Fu, Kangming Li
Among the ferromagnetic binary alloys, body centered cubic (bcc) Fe-Co is the one showing the highest magnetization. It is known experimentally that ordered Fe-Co structures show a…