A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
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Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge, considering that they can generate millions of lines of log data in a very short time.
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
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.