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Using artificial intelligence to advance energy technologies

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Hongliang Xin, an associate professor of chemical engineering in the College of Engineering, and his collaborators have devised a new artificial intelligence framework that can accelerate discovery of materials for important technologies, such as fuel cells and carbon capture devices. Titled "Infusing theory into deep learning for interpretable reactivity prediction," their paper in the journal Nature Communications details a new approach called TinNet--short for theory-infused neural network--that combines machine-learning algorithms and theories for identifying new catalysts. Catalysts are materials that trigger or speed up chemical reactions. TinNet is based on deep learning, also known as a subfield of machine learning, which uses algorithms to mimic how human brains work. The 1996 victory of IBM's Deep Blue computer over world chess champion Garry Kasparov was one of the first advances in machine learning.



Unlocking the secrets of chemical bonding with machine learning

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In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that "goldilocks zone" is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose. Bayeschem works using Bayesian learning, a specific machine learning algorithm for inferring models from data.


Unlocking the secrets of chemical bonding with machine learning

#artificialintelligence

A new machine learning approach offers important insights into catalysis, a fundamental process that makes it possible to reduce the emission of toxic exhaust gases or produce essential materials like fabric. In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that'goldilocks zone' is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose.


Machine Learning for faster Drug Discovery

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Artificial intelligence (AI) and machine-learning (ML) approaches are the present-day buzzwords finding applications in a host of domains affecting our lives. These approaches use known datasets to train and build models that can predict, or sometimes, make decisions about a task. In one such case, researchers at the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, have in a recent study, developed ML approaches using molecular descriptors for certain types of catalysis that could find use in several therapeutic applications. Traditionally, drug discovery and formulation is an elaborate process. Biological molecules have different properties, the knowledge of which are crucial for binding drug molecules that target proteins.