The dynamic of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotational
Research Highlights
Quantum Monte Carlo (QMC) methods are used to find the structure and electronic band gap of 2D GeSe, determining that the
Quantum Monte Carlo simulations reveal that Cooper pairs in the cuprate high-T
Quantum Monte Carlo simulations reveal that Cooper pairs in the cuprate high-T
Single atom impurities in graphene diffuse under e-beam irradiation. This phenomenon has been used to direct defect diffusion site-by-site with focused high-energy e-beams found in STEMs and stable defect arrays and heterostructures h
Metal halide perovskites are promising materials for optoelectronic and sensing applications.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN)
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Direct experimental evidence of gas-phase methyl radicals in propane oxidative dehydrogenation (ODHP) combined with density functional theory (DFT) calculations uncovers the mechanism behind the exception
Dendritic solidification and microstructure evolution play a vital role in determining the material properties. Capturing the morphology of the solidification front becomes critical in predicting the final dendritic structure.