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Hard machine learning can predict hard materials

#artificialintelligence

Superhard materials are in high demand by industry, for use in applications ranging from energy production to aerospace, but finding suitable new materials has largely been a matter of trial and error, based on classical hard materials such as diamonds. In a paper in Advanced Materials, researchers from the University of Houston (UH) and Manhattan College report a machine-learning model that can accurately predict the hardness of new materials, allowing scientists to more readily find compounds suitable for use in a variety of applications. Materials that are superhard – defined as those with a hardness value exceeding 40 gigapascals on the Vickers scale, meaning it would take more than 40 gigapascals of pressure to leave an indentation on the material's surface – are rare. "That makes identifying new materials challenging," said Jakoah Brgoch, associate professor of chemistry at UH and corresponding author of the paper. "That is why materials like synthetic diamond are still used even though they are challenging and expensive to make."


ICYMI: We check out Android 12's visual refresh

Engadget

This week, in addition to covering all the Cyber Week deals we could find, we also reviewed some unique gadgets. Steve Dent and a licensed drone pilot toured the French countryside with the help of the DJI Mavic 3 drone, while Terrence O'Brien played with the Animoog Z app, a sequel ten years in the making. Also, Cherlynn Low played around with Android 12 to check out its new Material You design. Steve Dent spent some time with the DJI Mavic 3 and a licensed drone pilot in the French countryside to see what the new device is capable of. He reports that not only is the Mavic 3 the easiest DJI drone to fly, but the large 4/3 sensor and dual camera system produce incredible footage – and the 46 minutes of range is double the time that the previous model could capture.


Materials: 'Super jelly' made from 80 per cent water can survive being run over by a CAR

Daily Mail - Science & tech

No, it's'super jelly' -- a bizarre new material that can survive being run over by a car even though it's composed of 80 per cent water. The'glass-like hydrogel' may look and feel like a squishy jelly, but when compressed it acts like shatterproof glass, its University of Cambridge developers said. It is formed using a network of polymers held together by a series of reversible chemical interactions that can be tailored to control the gel's mechanical properties. This is the first time that a soft material has been produced that is capable of such significant resistance to compressive forces. Super jelly could find various applications, the team added, from use for building soft robotics and bioelectronics through to replacement for damaged cartilage.


AI Generates Hypotheses Human Scientists Have Not Thought Of

#artificialintelligence

Electric vehicles have the potential to substantially reduce carbon emissions, but car companies are running out of materials to make batteries. One crucial component, nickel, is projected to cause supply shortages as early as the end of this year. Scientists recently discovered four new materials that could potentially help--and what may be even more intriguing is how they found these materials: the researchers relied on artificial intelligence to pick out useful chemicals test from a list of more than 300 options. And they are not the only humans turning to A.I. for scientific inspiration. Creating hypotheses has long been a purely human domain.


Machine-learning system accelerates discovery of new materials for 3D printing

#artificialintelligence

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.


AI Tool Leads to Discovery of Four New Materials

#artificialintelligence

A team of researchers at the University of Liverpool has developed a collaborative artificial intelligence (AI) tool that reduces the time and effort needed to discover new materials. The new AI has already led to the discovery of four new materials.  The research was published last month in the journal Nature Communications.  Discovering New Materials […]


MIT Uses AI To Accelerate the Discovery of New Materials for 3D Printing

#artificialintelligence

Researchers at MIT and BASF have developed a data-driven system that accelerates the process of discovering new 3D printing materials that have multiple mechanical properties. A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods. The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste.


Accelerating the discovery of new materials for 3D printing

#artificialintelligence

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.


A Big Bet on Nanotechnology Has Paid Off

#artificialintelligence

We're now more than two decades out from the initial announcement of the National Nanotechnology Initiative (NNI), a federal program from President Bill Clinton founded in 2000 to support nanotechnology research and development in universities, government agencies and industry laboratories across the United States. It was a significant financial bet on a field that was better known among the general public for science fiction than scientific achievement. Today it's clear that the NNI did more than influence the direction of research in the U.S. It catalyzed a worldwide effort and spurred an explosion of creativity in the scientific community. And we're reaping the rewards not just in medicine, but also clean energy, environmental remediation and beyond. Before the NNI, there were people who thought nanotechnology was a gimmick. I began my research career in chemistry, but it seemed to me that nanotechnology was a once-in-a-lifetime opportunity: the opening of a new field that crossed scientific disciplines.


New Artificial Intelligence Tool Accelerates Discovery of Truly New Materials

#artificialintelligence

The new artificial intelligence tool has already led to the discovery of four new materials. Researchers at the University of Liverpool have created a collaborative artificial intelligence tool that reduces the time and effort required to discover truly new materials. Reported in the journal Nature Communications, the new tool has already led to the discovery of four new materials including a new family of solid state materials that conduct lithium. Such solid electrolytes will be key to the development of solid state batteries offering longer range and increased safety for electric vehicles. Further promising materials are in development.