Artificial neurons exhibiting volatile threshold switching and action potential-like oscillations are crucial for brain-inspired computing. While Complimentary Metal-Oxide-Semiconductor (CMOS)-based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro-thermal devices due to nonlinearities like the Mott transition in VO2. Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under-explored, preventing predictive neuron design and rational materials selection. In this work, a physics-aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS2. CMOS-compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS-compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro-thermal neuron devices, a necessary pre-condition for inverse design from desired neuronal performance to required materials properties.