450 citations · 453 across the 5 of their papers we have counts for
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cond-mat.mtrl-sci2024★ 1 cited
Bridging deep learning force fields and electronic structures with a physics-informed approach
Yubo Qi, Weiyi Gong, Qimin Yan
This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional l…
cond-mat.mtrl-sci2024★ 1 cited
Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
Zhenyao Fang, Qimin Yan
The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient an…
cond-mat.mtrl-sci2014★ 450 cited
First-principles theory of nonradiative carrier capture via multiphonon emission
Audrius Alkauskas, Qimin Yan, Chris G. Van de Walle
We develop a practical first-principles methodology to determine nonradiative carrier capture coefficients at defects in semiconductors. We consider transitions that occur via mult…