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The SML model was trained on both direct experimental and indirect physics-informed features to predict graphene quality synthesized from Flash Joule heating. With anR²of 0.81, the model performs better compared to 0.73 without indirect features. 

Abstract The development of next‐generation in‐memory and neuromorphic computing can be realized with memory transistors based on 2D ferroelectric semiconductors. Among these, In₂Se₃is the interesting since it possesses ferroelectricity in 2D quintuple layers. Synthesis of large amounts of In₂Se₃crystals with the desired phase, however, has not been previously achieved. Here, the gram‐scale synthesis of α‐In₂Se₃crystals using a flash‐within‐flash Joule heating method is demonstrated. This approach allows the synthesis of single‐phase α‐In₂Se₃crystals regardless of the conductance of precursors in the inner tube and enables the synthesis of gram‐scale quantities of α‐In₂Se₃crystals. Then, α‐In₂Se₃flakes are fabricated and used as a 2D ferroelectric semiconductor FET artificial synaptic device platform. By modulating the degree of polarization in α‐In₂Se₃flakes according to the gate electrical pulses, these devices exhibit distinct essential synaptic behaviors. Their synaptic performance shows excellent and robust reliability under repeated electrical pulses. Finally, it is demonstrated that the synaptic devices achieve an estimated learning accuracy of up to ≈87% for Modified National Institute of Standards and Technology patterns in a single‐layer neural network system.