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
Filter Research Highlights
Area of Research
- (-) Building Technologies (1)
- (-) Climate and Environmental Systems (3)
- (-) Materials (7)
- (-) Materials for Computing (4)
- (-) Mathematics (11)
- Advanced Manufacturing (1)
- Biological Systems (3)
- Clean Energy (6)
- Computational Biology (1)
- Computational Chemistry (3)
- Computational Engineering (8)
- Computer Science (111)
- Data (8)
- Energy Sciences (3)
- Engineering Analysis (1)
- Geographic Information Science and Technology (1)
- Quantum information Science (10)
- Renewable Energy (2)
- Sensors and Controls (1)
- Supercomputing (35)
- Visualization (3)
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
In this work we focus on dynamics problems described by waves, i.e. by hyperbolic partial differential equations.
A research team from ORNL, Pacific Northwest National Laboratory, and Arizona State University has developed a novel method to detect out-of-distribution (OOD) samples in continual learning without forgetting the learned knowledge of preceding tasks.
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
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.
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.