Hydrogen storage materials with different crystal configurations have been extensively
investigated for hydrogen promotion. To escape the dilemma of traditional trial-and-error …

Scientists have extensively studied metallic glasses (MGs) for their excellent properties and
potential applications. However, the limited glass forming ability (GFA) of MGs poses a …

In this review, we explore the integration of intelligent algorithms in chemistry and materials
science. We begin by delineating the core principles of Machine Learning, Deep Learning …

The tribological properties of materials exhibit a complex and non-linear correlation under
varying operational conditions. Therefore, prioritizing a data-driven approach to predict …

Prediction of the pretreatment efficiency of lignocellulosic biomass with ternary deep eutectic
solvents (DES) containing Lewis acids by machine learning (ML). Principal component …

The goals of this Perspective are threefold:(1) to inform a broad audience, including
machine learning (ML) and artificial intelligence (AI) academics and professionals, about …

In recent years, many models have been developed to describe the gas–liquid
microdispersion process, which mainly rely on mechanistic analysis and may not be …

The operational stability of the blue quantum dot light-emitting diode (QLED) has been one
of the most important obstacles to initialize its industrialization. In this work, we demonstrate …

We introduce ODEFormer, the first transformer able to infer multidimensional ordinary
differential equation (ODE) systems in symbolic form from the observation of a single …

Polymers with exceptional heat resistance are critically valuable in numerous domains,
particularly as essential components of flexible organic light-emitting diodes. Among these …