Interfacial dynamics within chemical systems such as electron and ion transport processes
have relevance in the rational optimization of electrochemical energy storage materials and …

Abstract Mechanical Metamaterials (MMs) are artificially designed structures with
extraordinary properties that are dependent on micro architectures and spatial tessellations …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

We introduce an interpretable stochastic integrated machine learning based multiscale
approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene …

Multiscale modeling is an effective approach for investigating multiphysics systems with
largely disparate size features, where models with different resolutions or heterogeneous …

Materials modelling and design using computational quantum and classical approaches is
by now well established as an essential pillar in condensed matter physics, chemistry and …

CO2 can be converted into value-added products such as fuels, chemicals, and building
materials, adding an economic incentive for CO2 capture and green economy, while also …

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 …

By integrating multi-scale computational simulation with photo-regulated macromolecular
synthesis, this study presents a new paradigm for smart design while customizing polymeric …

Abstract Machine learning has proven to be technically flexible in recent years, which allows
it to be successfully implemented in problems in various areas of knowledge. Carbon …