Collaborative Research: Predictive Framework for Harnessing Order-Disorder Phenomena in Mixed-Chalcogen Solids

Non-Technical Summary. Semiconductors are critical components in a wide range of technologies including smart phones, solar cells, and radiation detectors, among many others. Mixed-chalcogen solids are an important class of semiconductors that have at least two negatively charged atoms, or anions, which can arrange themselves in ordered or disordered ways within a crystal. The arrangement of anions strongly impacts the electrical and thermal properties of semiconductors, but scientists do not have reliable tools to predict when or how ordering occurs. To overcome this challenge, researchers at Pennsylvania State University and Portland State University, with support from the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, combine computer modeling, machine learning, and laboratory experiments to develop a framework that can predict atomic ordering. The project outcomes advance scientific knowledge and can be used to design materials with improved properties and atomic-scale precision that is needed for next-generation applications. Additionally, this collaborative project includes hands-on training for graduate students, generates new content for college courses and promotes participation in science through mentoring and community interactions, thereby helping to develop a highly skilled workforce in the chemical and materials sciences. Technical Summary. Metal chalcogenides are a highly tunable class of materials due to the high miscibility of chalcogen anions in the solid state, which allows their properties to be fine-tuned using anion alloying strategies. Although chalcogens tend to form solid solutions in simple crystals, ordering does occur in structures with more than one anion site. Order-disorder phenomena affects their properties in numerous ways, but often goes unnoticed and is poorly understood. With this project, supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, the research team establish a deeper understanding of the fundamental mechanisms that define atomic-scale ordering in metal chalcogenides and other extended solids. This interdisciplinary approach, unifying experiment and theory, establishes chemical models for predicting local and long-range ordering phenomena. The integration of machine learning with density functional theory enables efficient screening of thousands of potential materials, creating a powerful platform for materials discovery. Beyond chalcogenides, the methodology provides a template for studying ordering phenomena in other multicomponent systems. Investigations of how ordering influences transport properties reveal hidden mechanisms governing macroscopic behavior, establishing new design principles that bridge atomic structure and functional performance in multianion solids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.