Technologies have driven big data collection across many fields, such as genomics and
business intelligence. This results in a significant increase in variables and data points …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

Abstract Machine learning is increasingly important in microbiology where it is used for tasks
such as predicting antibiotic resistance and associating human microbiome features with …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

The remarkable advances in biotechnology and health sciences have led to a significant
production of data, such as high throughput genetic data and clinical information, generated …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

The lack of reliable methods for identifying descriptors—the sets of parameters capturing the
underlying mechanisms of a material's property—is one of the key factors hindering efficient …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Size is the first, and at times, the only dimension that leaps out at the mention of big data.
This paper attempts to offer a broader definition of big data that captures its other unique and …