214 citations · 231 across the 11 of their papers we have counts for
11 papers
BenchQC: A Benchmarking Toolkit for Quantum Computation
Nia Pollard, Kamal Choudhary
The Variational Quantum Eigensolver (VQE) is a promising algorithm for quantum computing applications in chemistry and materials science, particularly in addressing the limitations…
AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and Inverse Materials Design
Kamal Choudhary
Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials desig…
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
Francesca Tavazza, Kamal Choudhary, Brian DeCost
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used…
Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks
Md Habibur Rahman, Prince Gollapalli, Panayotis Manganaris +5
Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semicon…
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example
Xinyu Jiang, Haofan Sun, Kamal Choudhary +2
Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression…
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…