549 citations · 1.7k across the 44 of their papers we have counts for
35 papers · 1 filter
Physics-informed time-series forecasting of perovskite photoluminescence stability
Alexander Wieczorek, Manuel Kober-Czerny, Fábio Lopes +5
Accelerated ageing using elevated temperatures and illumination is one of the most common methods to rapidly study the stability of novel semiconductor materials. However, as the p…
Building a physics-aware AI ecosystem for solid-state hydrogen storage materials
Seong-Hoon Jang, Yiwen Yao, Chuanyu Liu +66
Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evo…
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…
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
Daniel Persaud, Mike Werezak, Mark Xu +9
Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Ma…
Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
Benhour Amirian, Ashley S. Dale, Sergei Kalinin +1
Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human…
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…
Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images
Sima Zeinali Danalou, Hooman Chamani, Arash Rabbani +3
A major limitation of two-dimensional scanning electron microscopy (SEM) in imaging porous membranes is its inability to resolve three-dimensional pore architecture and interconnec…
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
Yutong Liu, Mehrad Ansari, Robert Black +1
Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, t…
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…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
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…
Artificial Intelligence-Enabled Optimization of Battery-Grade Lithium Carbonate Production
S. Shayan Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius +4
By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from a chloride into lithium…
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…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…
Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
Daniel Persaud, Logan Ward, Jason Hattrick-Simpers
The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards ope…
Multi-principal element alloy discovery using directed energy deposition and machine learning
Phalgun Nelaturu, Jason R. Hattrick-Simpers, Michael Moorehead +4
Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify prom…
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…
Why is EXAFS analysis for multicomponent metals so hard? Challenges and opportunities for measuring ordering in complex concentrated alloys using x-ray absorption spectroscopy
Howie Joress, Bruce Ravel, Elaf Anber +5
Short range order is a critical driver of properties (e.g. corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended…
A High Throughput Aqueous Passivation Testing Methodology for Compositionally Complex Alloys using Scanning Droplet Cell
Debashish Sur, Howie Joress, Jason Hattrick-Simpers +1
Compositionally complex alloy systems containing more than five principal elements allow exploring a wide range of compositions, processing, and structural variables with the hope…