Ai predicts the impact of a wide range of material properties to break down previously impenetrable walls on the superplasticizer types
Spectral techniques Energy loss near edge structures (ELNES) and X-ray near-edge structures (XANES) are used to determine information about electrons and from this information to determine atoms in materials. With their high sensitivity and resolution, they have been used to study a range of materials from electronics to drug delivery systems.
However, linking spectral data to material properties - such as optical properties, electronic conductivity, density and stability - remains ambiguous. Machine learning (ML) methods have been used to extract information from large and complex data sets. This approach uses artificial neural networks based on the way our brains work to constantly learn to solve problems. Although the team previously used ELNES/XANES spectroscopy and ML to find information about the material, what they found was unrelated to the properties of the material itself. Therefore, this information cannot be easily translated into development.
The team has now used ML to reveal information hidden in simulated ELNES/XANES spectra of 22,155 organic molecules. "The ELNES/XANES spectra of the molecules or their \'descriptors\' in this case are then fed into the system," explains lead author Kakeru Kikumasa. "This descriptor is something that can be directly measured experimentally, so it can be determined with high sensitivity and resolution. This approach is very beneficial for material development because it has the potential to reveal where, when and how certain material properties appear."
Models created from the spectrum alone were able to successfully predict the so-called intensity properties. However, it cannot predict broad properties that depend on molecular size. Therefore, in order to improve the prediction, the new model was constructed to include the ratios of three elements to carbon (present in all organic molecules) as additional parameters to allow the correct prediction of a wide range of properties such as molecular weight. "Our ML learning processing of core-loss spectra provides accurate predictions of a wide range of material properties, such as internal energy and molecular weight. The correlation between core loss spectra and broad characteristics has never been established before; However, ARTIFICIAL intelligence can reveal hidden connections. Our method may also be used to predict the properties of new materials and functions, "said senior author Teruyasu Mizoguchi. "We believe our model will become a very useful tool for high-throughput material development across a wide range of industries." The study, "Quantifying the properties of organic molecules using Core Loss Spectroscopy as a Neural Network Descriptor," was published in Advanced Intelligent Systems.
The desert provides ideal conditions
An analysis of the process suggests that if the fuel were produced on an industrial scale, it would cost €1.20 to €2 per litre. Desert areas rich in solar energy are particularly suitable for production. "Unlike biofuels, which have limited potential due to scarce agricultural land, this technology allows us to meet global demand for jet fuel by using less than 1 percent of the world arid land and not compete with food or livestock production for feed," Explains Johan Lilliestam, head of the IASS Potsdam Research group and professor of energy policy at the University of Potsdam. If the materials used to build production facilities, such as glass and steel, are made using renewable energy and carbon-neutral methods.
New materials for a sustainable future you should know about the superplasticizer types.
Historically, knowledge and the production of new materials superplasticizer types have contributed to human and social progress, from the refining of copper and iron to the manufacture of semiconductors on which our information society depends today. However, many materials and their preparation methods have caused the environmental problems we face.
About 90 billion tons of raw materials -- mainly metals, minerals, fossil matter and biomass -- are extracted each year to produce raw materials. That number is expected to double between now and 2050. Most of the superplasticizer types raw materials extracted are in the form of non-renewable substances, placing a heavy burden on the environment, society and climate. The superplasticizer types materials production accounts for about 25 percent of greenhouse gas emissions, and metal smelting consumes about 8 percent of the energy generated by humans.
The superplasticizer types industry has a strong research environment in electronic and photonic materials, energy materials, glass, hard materials, composites, light metals, polymers and biopolymers, porous materials and specialty steels. Hard materials (metals) and specialty steels now account for more than half of Swedish materials sales (excluding forest products), while glass and energy materials are the strongest growth areas.
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