This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

This article reviews recent developments in the applications of machine learning, data-
driven modeling, transfer learning, and autonomous experimentation for the discovery …

Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

This work represents the first step towards the application of machine learning techniques in
the prediction of statistical design allowables of composite laminates. Building on data …

Machine learning that generates biological hypotheses has transformative potential, but
most learning algorithms are susceptible to pathological failure when exploring regimes …

Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP)
obtains increased attention. However, most previous studies barely considered the large …

Gaussian processes (GPs) provide statistically optimal predictions in the sense of
unbiasedness and maximal precision. Although the modern implementation of GPs as a …

Our objective is to search a large candidate set of covalent organic frameworks (COFs) for
the one with the largest equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at …

Many modern machine learning algorithms have a large number of hyperparameters. To
effectively use these algorithms, we need to pick good hyperparameter values. In this article …

The unpredictability of battery degradation behavior is a challenging issue impeding the
development of battery applications, due to the complexity of the degradation and the …