Competing Constraints on Superconductivity in Thick FeSe films

TL;DR

This study uses combinatorial synthesis and machine learning to identify key constraints affecting the maximum superconducting transition temperature in thick FeSe films, revealing a narrow optimization window.

Contribution

It introduces a high-throughput off-center pulsed laser deposition method combined with machine learning to explore and understand the complex constraints on superconductivity in FeSe films.

Findings

Maximum Tc does not occur at the plume center but shifts off-center.

C-axis lattice parameter, stoichiometry, and disorder scattering critically constrain Tc.

The framework establishes a 17.1 K transition temperature in thick FeSe films.

Abstract

Superconducting films emerge from the complex interplay of multiple growth parameters, making their optimization challenging. In iron-based superconductors, compressive strain is known to enhance the transition temperature (Tc) of FeSe films, yet reported Tc values vary widely even on identical substrates, indicating factors beyond strain are critical. Here, we develop a high-throughput off-center pulsed laser deposition strategy that transforms plume inhomogeneity into combinatorial FeSe film libraries with continuous gradients in lattice parameter, composition, and disorder. We discover that the maximum Tc does not coincide with the plume center but can shift off-center, revealing a competition between favorable c-axis expansion, stoichiometry, and defect scattering. Systematic characterization of 80 thick films (>50 nm), combined with interpretable machine learning, shows that besides the strong correlate of c-axis lattice parameter to Tc, the stoichiometry and disorder scattering impose critical constraints on the achievable transition temperature, defining a narrow optimization window rather than a simple monotonic relationship. This framework yields Tconset=17.1 K in thick FeSe films and establishes a general framework combining combinatorial synthesis with machine learning to uncover constrained optimization landscapes in complex functional materials.