An active learning approach for improving the performance of equilibrium based chemical simulations

TL;DR

This paper introduces an active learning method that efficiently improves equilibrium chemical simulations by selectively sampling data points based on Gaussian process uncertainty, reducing the number of costly function evaluations.

Contribution

It presents a novel sequential active learning approach tailored for equilibrium-based chemical simulations, leveraging Gaussian process uncertainty for data selection.

Findings

Significantly reduces the number of function evaluations needed.

Demonstrates effectiveness on complex geoscience chemical systems.

Provides a parameter-free, scalable active learning framework.

Abstract

In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach called active learning. The underlying idea of our approach is to consider the function to estimate as a sample of a Gaussian process which allows us to compute the global uncertainty on the function estimation. Thanks to this estimation and with almost no parameter to tune, the proposed method sequentially chooses the most relevant input data at which the function to estimate has to be evaluated to build a surrogate model. Hence, the number of evaluations of the function to estimate is dramatically limited. Our active learning method is validated through numerical experiments and applied to a complex chemical system commonly used in geoscience.