Duccio Fanelli, Luca Bindi, Lorenzo Chicchi, Claudio Pereti, Roberta Sessoli, Simone Tommasini
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A short introduction to Neural Networks and their application to Earth and Materials Science Science
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.