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 rotationally invariant
Quantum Monte Carlo (QMC) methods are used to find the structure and electronic band gap of 2D GeSe, determining that the gap and its nature are highly tunable by strain.
Quantum Monte Carlo simulations reveal that Cooper pairs in the cuprate high-Tc superconductors are composed of electron holes on the Cu-d orbital and on the bonding molecular orbital constructed from the four surrounding O-p orbitals.
Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
A team at ORNL has demonstrated that the combination of transfer learning and semi-supervised learning can significantly reduce the amount of labeled data required to obtain strong performance in biomedical named entity recognition (NER) tasks.
Researcher proved that quantum resources are capable of revealing the magnetic structure and properties of magnetic materials such as rare earth tetraborides.
Estimating complex, non-linear model states and parameters from uncertain systems of equations and noisy observation data with current filtering methods is a key challenge in mathematical modeling.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
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 have been
A team of researchers from 91°µÍø (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.