Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laboratories are currently limited by human intuition in their ability to efficiently design and interpret experiments in high-dimensional spaces, throttling scientific discovery. We present AutoSciLab, a machine learning framework for driving autonomous scientific experiments, forming a surrogate researcher purposed for scientific discovery in high-dimensional spaces. AutoSciLab autonomously follows the scientific method in four steps: (i) generating high-dimensional experiments (x) using a variational autoencoder (ii) selecting optimal experiments by forming hypotheses using active learning (iii) distilling the experimental results to discover relevant low-dimensional latent variables (z) with a ‘directional autoencoder’ and (iv) learning a human interpretable equation connecting the discovered latent variables with a quantity of interest (y = f (z)), using a neural network equation learner. We validate the generalizability of AutoSciLab by rediscovering a) the principles of projectile motion and b) the phase-transitions within the spin-states of the Ising model (NP-hard problem). Applying our framework to an open-ended nanophotonics problem, AutoSciLab discovers a new way to steer incoherent light emission beyond current state-of-the-art, defining a new structure(material)-property(light-emission) relationship governing the physical process using closed-loop noisy experimental feedback.

Spatiotemporal control over incoherent light sources is critically important for applications such as displays, remote sensing, clean energy, and illumination. Incoherent light emission made up of randomized wavefronts is incompatible with known beam steering techniques that rely on coherent electromagnetic wave interference. The emerging field of tunable dielectric metasurfaces consisting of sub-wavelength arrays of optical nanoresonators has recently enabled active re-direction of incoherent light (photoluminescence, PL) emission. This was achieved by illuminating (pumping) the metasurface with a pump laser reflecting off a programmable spatial light modulator (SLM) with sawtooth grating patterns as input. Achieving efficient beam steering requires the generation of optimal pump patterns programmed into the SLM to maximize the PL emitted towards a given direction. Given the innumerable possibilities and the lack of a theoretical physical framework to guide the exploration of pump patterns, we use an active learning algorithm running a closed loop optical experiment with a generative model to explore and optimize novel pump patterns. We achieve up to an order of magnitude enhancement in the steering efficiency by using pump patterns that are generated by a variational auto-encoder, with minimal number of experiments. The results presented in this paper highlight the unique ability of generative models and active learning to dramatically improve steering efficiency by finding novel optical pump patterns that are beyond human intuition. Our combination of advanced machine learning techniques driving closed loop nanophotonic experiments might pave the way to derive the underlying physics of emergent light-matter phenomena.