The paradigm of advanced materials has grown exponentially over the last decade, with
their new dimensions including digital design, dynamics, and functions. Materials modeling …

By virtue of its tunable electrical conductivity, remarkable solution processing capability, and
great biocompatibility, polyaniline (PANI) has been recognized as an attractive active …

Alloys of sulfur, selenium and tellurium, often referred to as chalcogenide semiconductors,
offer a highly versatile, compositionally-controllable material platform for a variety of passive …

For over three decades, the materials tetrahedron has captured the essence of materials
science and engineering with its interdependent elements of processing, structure …

Polymer materials are widely used in biomedicine, chemistry and material science, whose
traditional preparations are mainly based on experience, intuition and conceptual insight …

Abstract Recently, the NMBP-35 Horizon 2020 projects-NanoMECommons, CHARISMA,
and Easi-stress-organised a collaborative workshop to increase awareness of their …

We developed a PyTorch-based architecture called HydraGNN that implements graph
convolutional neural networks (GCNNs) to predict the formation energy and the bulk …

The hafnia-based ferroelectric oxides with excellent negative-capacitance properties offer a
great opportunity to develop high-performance integrated circuits. The nanosized …

In this study, we show the transferability of graph convolutional neural network (GCNN)
predictions of the formation energy of the nickel-platinum solid solution alloy across atomic …

Machine learning techniques have been widely employed as effective tools in addressing
various engineering challenges in recent years, particularly for the challenging task of …