Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
Filter Research Highlights
Area of Research
- (-) Computational Biology (1)
- (-) Data (8)
- (-) Materials (7)
- (-) Quantum information Science (10)
- Advanced Manufacturing (1)
- Biological Systems (3)
- Building Technologies (1)
- Clean Energy (6)
- Climate and Environmental Systems (3)
- Computational Chemistry (3)
- Computational Engineering (8)
- Computer Science (111)
- Energy Sciences (3)
- Engineering Analysis (1)
- Geographic Information Science and Technology (1)
- Materials for Computing (4)
- Mathematics (11)
- Renewable Energy (2)
- Sensors and Controls (1)
- Supercomputing (35)
- Visualization (3)
Researcher proved that quantum resources are capable of revealing the magnetic structure and properties of magnetic materials such as rare earth tetraborides.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
Researchers built a deep neural network to estimate the compressibility of scientific data.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN) approach. This approach has great potential to identify structural parameters for gas adsorption.
A team from Oak Ridge and Los Alamos National Laboratories led a demonstration of quantum key distribution systems that harness the power of quantum mechanics to authenticate data and encrypt messages with a secret ”key” to securely transmit “locked” in
The upcoming Square Kilometre Array (SKA) will be the largest radio telescope in the world. An international team recently used Summit, the world’s most powerful supercomputer, to simulate the massive amounts of data the SKA will produce.
ORNL researchers have developed a quantum chemistry simulation benchmark to evaluate the performance of quantum devices and guide the development of applications for future quantum computers.
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.