most citedThermodynamic assessment of machine learning models for solid-state synthesis prediction

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cond-mat.mtrl-sci2026★ 1 cited

Thermodynamic assessment of machine learning models for solid-state synthesis prediction

Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski +1

Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles model…

cond-mat.mtrl-sci2025

Origins of chalcogenide perovskite instability

Adelina Carr, Talia Glinberg, Nathan Stull +2

Chalcogenide perovskites, particularly II-IV ABS3 compounds, are a promising class of materials for optoelectronic applications. However, these materials frequently exhibit instabi…