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Natalie Jones

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Unravelling fine-tunings in nuclear physics

Unravelling fine-tunings in nuclear physics

Reducing the spread of multidrug-resistant bacteria

Reducing the spread of multidrug-resistant bacteria

A very successful first year for the new group Batteries – Calorimetry and Safety at the KIT

A successful first year for the Batteries – Calorimetry and Safety...

Innovative calorimetric methods for safer batteries in the renewable energy sector

Innovative calorimetric methods for safer batteries in the renewable energy sector

Three routes towards a circular economy of packaging materials

Three routes towards a circular economy of packaging materials

Uncovering the nature of Dark Matter particles to better understand the Universe

Uncovering the nature of dark matter to better understand the Universe

VascTech materials – the possibilities are endless

VascTech materials – the possibilities are endless

The Great Green Wall: Protecting Sahel through science-led intervention

The Great Green Wall: Protecting Sahel through science-led intervention

Cleaning rivers to clean the seas

Cleaning rivers to clean the seas – part II

The science of tactile melodies

The science of tactile melodies

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

iCUPE Datasets for Polar Regions

iCUPE Datasets for Polar Regions

Drone usage in flood science and beyond

Drone usage in flood science and beyond

Sustainable agriculture: Nutrient removal from agricultural runoff

Could financial incentives help preserve tropical forests?

Could financial incentives help preserve tropical forests?

How PilotEdge is changing the way pilots interact with flight simulations

How PilotEdge is changing the way pilots interact with flight simulations

The importance of trust and regulation in flood disaster response

The importance of trust and regulation in flood disaster response

Battery calorimetry: an innovative approach for ageing studies

Battery calorimetry: an innovative approach for ageing studies

Genitourinary cancer treatment made personal

Genitourinary cancer treatment made personal

The surprising effects of the pandemic on atmospheric scientists

Observing the atmosphere during the pandemic

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