Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product
design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Agriculture 4.0 represents the fourth agriculture revolution that uses digital technologies and
moves toward a smarter, more efficient, environmentally responsible agriculture sector …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …

De novo drug design is a computational approach that generates novel molecular structures
from atomic building blocks with no a priori relationships. Conventional methods include …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

Kohn–Sham Density Functional Theory (KSDFT) is the most widely used electronic structure
method in chemistry, physics, and materials science, with thousands of calculations cited …

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent
years. In this Review, we studied the growth and distribution of AI-related chemistry …