AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery

TL;DR

AutoSciLab is an autonomous machine learning framework that conducts scientific experiments, discovers interpretable low-dimensional representations, and uncovers new scientific principles in high-dimensional spaces.

Contribution

It introduces a comprehensive autonomous framework combining experiment generation, hypothesis selection, latent variable discovery, and interpretable modeling for scientific discovery.

Findings

Successfully rediscovered principles of projectile motion.

Uncovered phase transitions in the Ising model.

Discovered a novel nanophotonics method surpassing state-of-the-art.

Abstract

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 \in R^D) 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 \in R^d, with d << D) 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 challenge, AutoSciLab uncovers a fundamentally novel method for directing incoherent light emission that surpasses the current state-of-the-art (Iyer et al. 2023b, 2020).