A short introduction to Neural Networks and their application to Earth and Materials Science Science

TL;DR

This paper provides an accessible overview of neural networks and highlights their recent applications in Earth and Materials Science, including geothermobarometry and the discovery of new superconducting materials.

Contribution

It introduces neural network fundamentals and reviews recent innovative applications in geoscience and materials discovery, demonstrating their practical utility.

Findings

Neural networks can reliably predict magma pressure and temperature conditions.

Machine learning aids in identifying novel superconducting materials.

The paper showcases successful case studies in Earth and Materials Science.

Abstract

Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Here, we provide a simple pedagogical account of the basic functioning of a feedforward neural network. Then we move forward to reviewing two recent applications of machine learning to Earth and Materials Science. We will in particular begin by discussing a neural network based geothermobarometer, which returns reliable predictions of the pressure/temperature conditions of magma storage. Further, we will turn to illustrate how machine learning tools, tested on the list of minerals from the International Mineralogical Association, can help in the search for novel superconducting materials.