Artificial Intelligence as a Service (AIaaS) Market to Experience Growth as Researchers Develop a Model that has Unparalleled Training Speed

Posted On April 16, 2022     

Joint Automated Repository for Various Integrated Simulations (J.A.R.V.I.S.) is an open dataset that automates the discovery and optimization of materials. Researchers described the most recent J.A.R.V.I.S. enhancements that use A.I. to speed up discovery. Their A.L.I.G.N.N. (Atomistic Line Graph Neural Network) is a huge development for the Artificial Intelligence as a Service (AIaaS) Market as it surpasses previously published models on atomistic prediction tasks. The system combines graph neural networks with chemical and structural knowledge about materials. It has very high accuracy and faster or equivalent model training speed.

There have been other such databases built. But what's unique about the J.A.R.V.I.S. database is that it has modules for all sorts of computational techniques. There are many distinct theoretical levels on which the area might be approached. J.A.R.V.I.S. is different from other databases in that it spans multiple levels.

This approach has shown to be quite productive. However, within the periodic table, there are billions of different combinations of elements—far more than we can ever generate data for. Here artificial intelligence can help.

The team reasoned that quantum mechanical calculations could be used to screen physical experiments. Similarly, machine learning can be used to screen expensive measures. However, such a system must first be trained.

To be effective, neural networks like A.L.I.G.N.N. require a large amount of training data. DFT simulations of 70,000 materials and counting stand behind the cutting-edge A.I. model. The neural network was trained using this growing library. Thus, it can now quickly characterize new materials or filter for materials with specific features.

The team intends to speed up the discovery pipeline by creating a database of potential materials and developing tools to automate screening. This would bring Iron Man-like powers closer to reality.

Imagine a model that can anticipate a new material, a new treatment, and say, Out of a million molecules, try this one first. It's the golden age of materials research at the moment.

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