An AI assistant for material discovery Posted on : Feb 15 - 2022

When Tony Stark needs to travel to space in the original Iron Man movie, he asks his artificial intelligent (AI) assistant J.A.R.V.I.S. to make a suit that can survive harsh conditions.

As AI specialist Kamal Choudhary explains: "The way I see it, what J.A.R.V.I.S. did is, it had a database of materials, scanned the database, found a suitable material, tested it, then synthesized an alloy that could survive space conditions.

"That's what we want our system to do, and that's why we called it JARVIS."

Choudhary, a researcher at the National Institute of Standards and Technology (NIST), is the founder and developer of JARVIS (Joint Automated Repository for Various Integrated Simulations)—an open dataset designed to automate materials discovery and optimization.

Writing in npj Computational Materials in December 2021, Choudhary and Brian DeCost (NIST) described the latest enhancements to JARVIS that apply AI to speed discovery. Combining graph neural networks with chemical and structural knowledge about materials, their Atomistic Line Graph Neural Network (ALIGNN) outperforms previously reported models on atomistic prediction tasks with very high accuracy and better or comparable model training speed.

"ALIGNN can predict characteristics in seconds instead of months," Choudhary said.

Beyond the inspiration from Iron Man, there was the Materials Genome Initiative. Originated in 2011 under President Obama, the initiative is a multi-federal agency effort to discover, manufacture, and deploy advanced materials twice as fast and at a fraction of the cost of traditional methods.

NIST's original contribution to the initiative was the creation of a database of materials and their characteristics, obtained rigorously, using standardized, cutting-edge computing methods.

Several such databases have been established, but "what's particular about the JARVIS database is that it contains modules for various kinds of computational approaches," according to David Vanderbilt, professor of physics at Rutgers University, member of the National Academy of Sciences, and a contributor to the project. "There are many different theoretical levels on which you can approach the field. JARVIS is unusual in that it spans more levels than other databases."

The original data for JARVIS was drawn from density function theory (or DFT) calculations. "DFT is the standard way that most people compute properties of a material at an atomistic level," Vanderbilt explained. "They're first-principal calculations, where there's no experimental input and the results are derived from theory from the ground up according to the laws of quantum mechanics." View more