Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …

The increasing global need for energy supply in modern society has created a pressing
need to explore new materials for renewable energy technologies. However, conventional …

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …

Achieving the 2016 Paris agreement goal of limiting global warming below 2° C and
securing a sustainable energy future require materials innovations in renewable energy …

With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …

We present a proof of concept that machine learning techniques can be used to predict the
properties of CNOHF energetic molecules from their molecular structures. We focus on a …

Abstract Machine learning (ML) is a versatile technique to rapidly and efficiently generate
insights from multidimensional data. It offers a much‐needed avenue to accelerate the …

The discovery of novel materials with desired properties is essential to the advancements of
energy-related technologies. Despite the rapid development of computational infrastructures …