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Purpose While science, technology, engineering and math (STEM) postdoctoral scholars often enter their positions with strong science identities, racially marginalized scholars are often not treated as scientists, which can weaken their science identities. This study aims to examine how racial discrimination negatively affects their science identities in STEM and the importance of community recognition in mitigating these effects. Design/methodology/approach The authors use reflected appraisals and identity theory to theoretically guide this work. The data are based on a survey of 215 postdoctoral scholars in STEM disciplines. Findings The authors find that community recognition mediates the negative relationship between perceived discrimination and postdoctoral scholars’ science identities. Research limitations/implications The study shows the importance of recognizing the achievements and identities of underrepresented STEM scholars to counteract the chronic and cumulative identity nonverification that leaves talent unrecognized and disrupts scholars’ science identities. Originality/value The authors explore the negative impact of discriminatory experiences on the importance individuals place on their identities as scientists and if this can be affected by the degree to which they feel that other scientists recognize them as competent scientists among a group of scholars who have earned the highest of academic degrees, and who are also relatively understudied: postdocs. 

Research has shown that work-life conflicts exist among all kinds of workers, including academics, and these conflicts are a key contributor to workers’ reports of poor well-being. Very little research has been done on work-life conflict among post-baccalaureate PhD trainees (e.g., graduate students and postdoctoral trainees) who reside in an important liminal stage in the professoriate pipeline. In this study, we examine the degree to which postdocs believe they suffer from conflicts between their work responsibilities and their home responsibility and the relationship between those conflicts and postdoc’s mental health. We argue that, like other workers, postdocs suffer (in numerical terms and its relationship to health) more from the work-to-life imbalances than from life-to-work imbalances; life matters more than work, ultimately. Our results, based on a survey of 215 STEM postdoctoral trainees, reveal that a majority of postdocs say they have work-life conflicts and these work-life conflicts are associated with negative mental health outcomes. We discuss the potential impact of these findings on attempts to broaden participation in STEM careers and diversify the professoriate. 

We contend that the work scientists do is entrepreneurial because they are in the business of discovering, evaluating, and exploiting opportunities to create new knowledge. In this article, we examine the relationship between Science, Technology, Engineering, and Mathematics (STEM) scholars’ holdings of traits associated with entrepreneurial activity and the degree to which these scientists consider being a scientist important to their sense of self. In particular, we argue that optimism, an innovative mindset, and competitiveness should be associated, positively, with STEM scholars’ science identity. Our results, based on a survey of 215 postdoctoral trainees in STEM disciplines, show that the more academic scientists have of each entrepreneurial disposition, the greater their science identity centrality. 

Hardness, or the quantitative value of resistance to permanent or plastic deformation, plays a crucial role in materials design for many applications, such as ceramic coatings and abrasives. Hardness testing is an especially useful method because it is nondestructive and simple to implement and gauge the plastic properties of a material. In this study, I proposed a machine, or statistical, learning approach to predict hardness in naturally occurring ceramic materials, which integrates atomic and electronic features from composition directly across a wide variety of mineral compositions and crystal systems. First, atomic and electronic features, such as van der Waals, covalent radii, and the number of valence electrons, were extracted from composition. The results showed that this proposed method is very promising for predicting Mohs hardness with F1-scores >0.85. The dataset in this study included modeling across a larger set of materials and hardness values, which have never been predicted in previous studies. Next, feature importances were used to identify the strongest contributions of these compositional features across multiple regimes of hardness. Finally, the models that were trained on naturally occurring ceramic minerals were applied to synthetic, artificially grown single crystal ceramics.