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cosmological constant

Solving the mystery behind dark energy and the cosmological constant

A team of Columbian researchers suggest that an early model could solve the mystery behind the relationship between dark energy and the cosmological constant. The...
The science of tactile melodies

The science of tactile melodies

Yasuhiro Suzuki, an associate professor at Nagoya University, Japan, explains time related variations in tactile sensations and tactile melodies. Tactile science has not dealt with...
nuclear astrophysics

New tools for nuclear astrophysics experiments

Professor Arthur Champagne, from the University of North Carolina – Chapel Hill and the Triangle Universities Nuclear Laboratory, describes a new accelerator facility for...
extraction of thorium

New technology to improve the extraction of thorium

A Russian research consortium has improved the technology used in the alkaline extraction of thorium, increasing the sustainability and safety of nuclear power. The consortium...
physics in China

High energy physics in China

Professor Yifang Wang spoke to The Innovation Platform about high energy physics in China and the multifaceted work taking place at the Institute of...
laser wavelengths

New cost-effective way of tuning laser wavelengths to infrared

A team of researchers at Institut national de la recherche scientifique (INRS) has discovered a cost-effective way of tuning laser wavelengths to infrared. The INRS...
Material Mind Inc. (MM), a start-up from Silicon Valley, took this idea and both implemented it and expanded it. They are the first company to combine the ideas of using AI tools to mine DFT patterns for signature features outlined by physical models to construct datasets of innovative materials with predicted physical properties, enabling ML training and discovery. Material Mind took their database of ~90,000 band structures, pertaining to ~50,000 materials, and created AI tools to automate detection of ‘gapped anticrossings’ and quantify the SHE for each material. With this ranked list of predicted SHE materials, they used part of the dataset as training data for ML algorithms to discover completely new SHE candidates. This success sets the stage, for the first time, for AI+ML to be used to discover materials for electronic (and in the future, magnetic and other) applications where experimental data is sparse. MM in particular is leading the charge with its prediction engine for spintronics and expects to expand in several more verticals in the near future. This approach of targeted materials and material’s property discovery may disrupt the current state of innovative materials research and revolutionise the field, dramatically increasing the rate at which technology enabling materials advancements are made. Learn more about the Ali group at the Max Planck Institute here.

Creating innovative materials with AI and fundamental physics

Dr Mazhar Ali of the Ali Group at the Max Planck Institute discusses the role of AI and fundamental physics in the creation of...
printed neutrino detector

CERN discusses its new 3D printed neutrino detector SuperFGD

Albert De Roeck, senior research scientist at CERN, spoke to Innovation News Network’s digital editor, Caitlin Magee, about the EP-Neutrino group and its 3D...
neutrino activity

Understanding neutrino activity at SNOLAB

The Executive Director of SNOLAB, Dr Nigel Smith, spoke to The Innovation Platform about the facility’s science programme, most notably its neutrino activities. A world-class...
high energy Universe

CTAO and the high energy Universe

CTAO Project Scientist, Dr Roberta Zanin, spoke to The Innovation Platform about the Cherenkov Telescope Array’s role in areas such as dark matter detection,...

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