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

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

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.str-el2025

Antibonding and Electronic Instabilities in GdRu2X2 (X = Si, Ge, Sn): A New Pathway Toward Developing Centrosymmetric Skyrmion Materials

Dasuni N. Rathnaweera, Xudong Huai, K. Ramesh Kumar +4

Chemical bonding is key to unlocking the potential of magnetic materials for future information technology. Magnetic skyrmions are topologically protected nano-sized spin textures…