A new artificial intelligence (AI) framework that can accelerate discovery of materials for important technologies, such as fuel cells and carbon capture devices, has been created by a team at the College of Engineering. This interesting news came to us from Lab Manager from their article, “Artificial Intelligence to Advance Energy Technologies.”

The new approach is called TinNet—short for theory-infused neural network—that combines machine learning algorithms and theories for identifying new catalysts. It is based on deep learning and uses algorithms to mimic how human brains work. Deep learning has played a major role in the development of technologies such as self-driving cars.

Many companies are using AI and machine learning to take deep dives into data that can drive better decision making, cost advantages and predictions that can stave off energy disasters and expensive downtime. 

Most organizations have little knowledge on how AI systems make the decisions they do and that is no different in the energy field. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases.

Melody K. Smith

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