1.2k citations · 4.5k across the 50 of their papers we have counts for
53 papers
ALIGNN 2.0: A Unified Line-Graph Neural Network Framework for Materials Screening, Force Fields, Inverse Design, Spectroscopy, and Microscopy
Jaehyung Lee, Charles Rhys Campbell, Akshaya Ajith +3
Graph neural networks are central to materials property prediction and machine-learning interatomic potentials, yet their reliance on specialized graph libraries hampers portabilit…
Hybrid DiffractGPT-Rietveld Refinement Framework for Automated X-ray Diffraction Analysis
Charles Rhys Campbell, Justin Ely, Jaehyung Lee +2
X-ray diffraction (XRD) is fundamental to structural materials characterization, yet transforming a raw powder pattern into a refined crystal structure still demands considerable d…
Hallucination Detector: A hybrid LLM and Semantic Scholar tool calling for detecting hallucination in scientific literature on AtomGPT.org
Harichandana Neralla, Jaehyung Lee, Aldo H. Romero +1
Large language models are now commonly used as partners in scientific writing, and this shift has brought a subtler type of failure: made-up references. Fabricated authors, bogus D…
BatteryMat: a hierarchical machine-learning and DFT framework for average-voltage screening of lithium-ion cathode materials
Jaehyung Lee, Charles Rhys Campbell, Kent Zhang +1
Density functional theory (DFT) predicts cathode voltages accurately but does not scale to the combinatorial chemical spaces of modern materials databases, while pure machine-learn…
SlaKoNet-VQD: A universal Slater-Koster tight-binding Hamiltonian for variational quantum band-structure calculations on near-term hardware
Akshaya Ajith, Jaehyung Lee, Charles Rhys Campbell +1
Variational quantum algorithms such as VQE and VQD are promising for near-term electronic structure calculations, but for periodic solids their reach is limited by the cost of buil…
Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries
Josiah D. Kunz, Kamal Choudhary
Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios. M…