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New framework applies machine learning to atomistic modeling

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Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials the guiding rules describing how atoms interact in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.
Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. The process to establish materials interatomic potential called parameterization has required significant chemical and physical intuition, leading to less accurate prediction of new materials design. ....

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New Framework Applies Machine Learning to Atomistic Modeling | News | Northwestern Engineering

New Framework Applies Machine Learning to Atomistic Modeling | News | Northwestern Engineering
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New framework applies machine learning to atomistic modeling

New framework applies machine learning to atomistic modeling
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New AI-Based Device Mimics Neural Activity of the Human Brain

New AI-Based Device Mimics Neural Activity of the Human Brain
Written by AZoRoboticsMar 3 2021
Artificial intelligence (AI) needs a large amount of computing power and also multipurpose hardware to support this computing power.
The collaborative research team utilized the powerful X-ray nanoprobe imaging tool to study the NdNiO₃ device showing neuron tree-like memory. A scanning electron microscope image of the NdNiO₃ device is shown at the bottom. The red rectangle shows the scanned area of the X-ray imaging. Image Credit: by Argonne National Laboratory.
However, the majority of the AI-supportive hardware is based around the same ancient technology and is still a long way from simulating the neural activity in the human brain. ....

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Argonne scientists help explain phenomenon in hardware that could revolutionize AI | US Department of Energy Science News

DOE/Argonne National Laboratory
Artificial intelligence, or AI, requires a huge amount of computing power, and versatile hardware to support that power. But most AI-supportive hardware is built around the same decades-old technology, and still a long way from emulating the neural activity in the human brain.
In an effort to solve this problem, a group of scientists from around the country, led by Prof. Shriram Ramanathan of Purdue University, has discovered a way to make the hardware more efficient and sustainable.
We re creating hardware that is smart enough to keep up (with advancements in AI) and also doesn t use too much energy. In fact, the energy demand will be cut significantly using this technology. Argonne physicist Hua Zhou ....

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