Machine learning tagged posts

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited data. This can aid in the discovery of materials with desired properties, such as semiconductors.

In recent years, materials engineers have turned to machine learning models to predict which types of materials can possess specific properties such as electronic band gaps, formation energies, and mechanical properties, in order to design new materials. However, data on material properties—which is needed to train these models—is limited because testing materials is expensive and time consuming.

This prompted researchers led by Sai Gautam Gopalakrishnan, Assistant Professor at the D...

In recent years, roboticists have developed a wide range of systems designed to tackle various real-world tasks, ranging from completing household chores to delivering packages or finding target objects in delineated environments.

A key objective in the field has been to develop algorithms that allow the reliable transfer of specific skills across robots with different bodies and characteristics, which would help to rapidly train robots on new tasks, broadening their capabilities.

Researchers at UC Berkeley have developed RoVi-Aug, a new computational framework designed to augment robotic data and facilitate the transfer of skills across different robots...

Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method much simpler than previous approaches.

Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT. However, these complex tasks require increasingly complex neural networks; some with many billion parameters...

Researchers look to machine learning to optimize the design and control of stellarators and tokamaks. Researchers are using artificial intelligence to perfect the design of the vessels surrounding the super-hot plasma, optimize heating methods and maintain stable control of the reaction for increasingly long periods. A new article explains how a researcher team used machine learning to avoid magnetic perturbations, or disruptions, which destabilize fusion plasma.

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S...