Unveil the unseen: Exploit information hidden in noise

TL;DR

This paper introduces a machine learning approach that leverages noise and uncertainty in data to enhance predictions, demonstrated through applications in material science and diffraction analysis.

Contribution

It presents a novel formalism that exploits uncertainty in data to improve predictive accuracy across different physical science applications.

Findings

Successfully predicted phase transition in crystal using noise-derived uncertainty.

Improved diffraction amplitude predictions by utilizing particle count and its uncertainty.

Demonstrated broad applicability of the approach in physical sciences.

Abstract

Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learning architecture that extracts crucial information out of the noise itself to improve the predictions. The phenomenology computes and then utilizes uncertainty in one target variable to predict a second target variable. We apply this formalism to PbZr$_{0.7}$Sn$_{0.3}$O$_{3}$ crystal, using the uncertainty in dielectric constant to extrapolate heat capacity, correctly predicting a phase transition that otherwise cannot be extrapolated. For the second example -- single-particle diffraction of droplets -- we utilize the particle count together with its uncertainty to extrapolate the ground truth diffraction amplitude, delivering better predictions than when we utilize only the particle count. Our generic formalism enables the exploitation of uncertainty in machine learning, which has a broad range of applications in the physical sciences and beyond.