Generative models have gained widespread attention in recent years due to their inverse design capabilities and their potential to accelerate the molecular design and discovery processes. This ...
The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth's climate. Computer scientists have now used this ...
Hosted on MSN
Deep learning method enables efficient Boltzmann distribution sampling across a continuous temperature range
A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature ...
A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen Graduate School has developed a topology-based variational autoencoder framework ...
A research team led by Prof. PAN Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. LI Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong ...
A study published in Advanced Energy Materials ("Deep Generative Models Regulate Ultralow‐Pt Catalyst Layer in Fuel Cells") takes a different approach. A team based at the Eastern Institute of ...
Artificial intelligence can solve problems at remarkable speed, but it's the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists ...
OpenAI’s o3: AI Benchmark Discrepancy Reveals Gaps in Performance Claims Your email has been sent The FrontierMath benchmark from Epoch AI tests generative models on difficult math problems. Find out ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results