Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

Nowadays, the research on materials science is rapidly entering a phase of data‐driven
age. Machine learning, one of the most powerful data‐driven methods, have been being …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in
materials science to build predictive models and accelerate discovery. For selected …

There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate
surrogate models of materials properties. In recent years, a broad array of materials property …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Based on the growing power of computational capabilities and algorithmic developments,
with the help of data-driven and high-throughput calculations, a new paradigm accelerating …

Intelligent agents must be able to communicate intentions and explain their decision-making
processes to build trust, foster confidence, and improve human-agent team dynamics …

A quantitative uncertainty metric controls error in neural network-driven chemical discovery -
Chemical Science (RSC Publishing) DOI:10.1039/C9SC02298H Royal Society of Chemistry …