Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

Finding a Disappearing Nontimber Forest Resource: Using Grounded Visualization to Explore Urbanization Impacts on Sweetgrass Basketmaking in Greater Mt. Pleasant, South Carolina

Despite growing interest in urbanization and its social and ecological impacts on formerly rural areas, empirical research remains limited. Extant studies largely focus either on issues of social exclusion and enclosure or ecological change. This article uses the case of sweetgrass basketmaking in Mt. Pleasant, South Carolina, to explore the implications of urbanization, including gentrification, for the distribution and accessibility of sweetgrass, an economically important nontimber forest product (NTFP) for historically African American communities, in this rapidly growing area. We explore the usefulness of grounded visualization for research efforts that are examining the existence of fringe ecologies associated with NTFP. Our findings highlight the importance of integrated qualitative and quantitative analyses for revealing the complex social and ecological changes that accompany both urbanization and rural gentrification