Learning to shine: Neuroevolution enables optical control of phase transitions
We address the problem of active optical steering of structural phase transitions in solids. We demonstrate that existing reinforcement learning approaches can derive optimal time-dependent electric fields in optically-driven dissipative classical systems far beyond the harmonic regime, enabling the stabilization of non-thermal structural phases. Our approach relies on experimentally extractable metrics of the phase-space evolution and physically-interpretable Fourier Neural Network surrogates of the time-dependent electric field. Using first-principles simulations, we demonstrate the stabilization of a symmetric phase in bismuth through impulsive Raman scattering under continuous and pulsed light sources in the presence of dissipation. Importantly, the method is gradient-free, which enables optimization loops based solely on experimental data, such as the measures of half-periods of oscillations in transient spectroscopy. Our framework thus provides a practical route for controlling non-equilibrium structural dynamics with light, opening pathways to stabilize hidden and metastable phases in quantum materials.