To develop faster computers, batteries with higher capacities, and lighter and stronger automobiles and airplanes, scientists are working to discover and synthesize new materials with high-performance properties. But the number of atom combinations that could compose new materials is nearly unlimited, which makes the discovery process time consuming and expensive. To speed up the process, scientists are using theoretical models and computers to explore all the possibilities and disregard materials that are not desirable. Ali Davariashtiyani, a PhD student working under the direction of Assistant Professor Sara Kadkhodaei in the Computational Materials Research Lab at UIC, has developed a data-driven deep-learning model to help researchers identify easily synthesizable materials. Their findings were recently published in the journal Communications Materials.