Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of
solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML …

Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate
computation, automate data analysis, and predict materials properties. The representation of …

In this work, we introduce a computational polymer discovery framework that efficiently
designs polymers with tailored properties. The framework comprises three core components …

Solid polymer electrolytes offer promising advancements for next-generation batteries,
boasting superior safety, enhanced specific energy, and extended lifespans over liquid …

Open material databases storing thousands of material structures and their properties have
become the cornerstone of modern computational materials science. Yet, the raw simulation …

Machine learning (ML), material genome, and big data approaches are highly overlapped in
their strategies, algorithms, and models. They can target various definitions, distributions …

In recent years, artificial intelligence (AI) has emerged as a transformative tool for
addressing scientific and technical challenges across various disciplines. AI enables data …