AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery

• URL: http://arxiv.org/abs/2412.12347v1
• Date: Mon, 16 Dec 2024 20:41:46 GMT
• Title: AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery
• Authors: Saaketh Desai, Sadhvikas Addamane, Jeffrey Y. Tsao, Igal Brener, Laura P. Swiler, Remi Dingreville, Prasad P. Iyer,
• Abstract summary: AutoSciLab is a machine learning framework for driving autonomous scientific experiments. It forms a surrogate researcher purposed for scientific discovery in high-dimensional spaces. Applying our framework to an open-ended nanophotonics challenge, AutoSciLab uncovers a fundamentally novel method for directing incoherent light emission.
• Score: 1.1740681158785793
• License:
• 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).

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