natural environment

UK commits to protecting 30% of natural environment by 2030

Today, the British Prime Minister will commit to protecting 30% of the UK’s natural environment and biodiversity by 2030. National parks, ‘areas of outstanding natural...
CHEOPS

The first result from ESA’s CHEOPS mission

The results of CHEOPS, the first ESA mission dedicated to characterising known exoplanets, have been published in Astronomy & Astrophysics. CHEOPS was developed as part...
Capturing microplastics

Capturing microplastics using acoustics

Scientists at Shinshu University, Japan, have developed a method of capturing microplastics from wastewater using specialist acoustic technologies. Scientists estimate that the amount of plastic...

RetinaRisk allows you to calculate the risk of diabetic retinopathy

Professor Einar Stefánsson explains how the RetinaRisk app offers a truly personalised approach by identifying patients’ individualised risk of developing sight-threatening diabetic retinopathy. The global...
chemical industry

European chemical industry: Increasing sustainability and digitisation

Cefic’s Executive Director for Innovation, Pierre Barthelemy, spoke to The Innovation Platform about the clear role of the chemical industry in enabling Europe to...
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...
synthetic biology

New algorithm to improve the efficiency of synthetic biology experiments

Researchers at the Department of Energy's Lawrence Berkeley National Laboratory have created a new tool that adapts machine learning algorithms to the needs of...
clinical trials for COVID-19

Can machine learning create faster clinical trials for COVID-19?

A recent research paper, published in Statistics in Biopharmaceutical Research, describes how advances in machine learning can provide an opportunity for researchers to create...
ALMA

Utilising ALMA to search for planet formation in disks orbiting young stars

Anne Dutrey, from the Laboratory of Astrophysics of Bordeaux at Université de Bordeaux – CNRS, outlines new research utilising ALMA to search for planet...
green research projects

European Commission launch a €1bn call for green research projects

The European Commission has launched a €1bn call for research projects that respond to issues caused by the current climate crisis. The Commission is...
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