Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

The adaptability of natural organisms in altering body shapes in response to the
environment has inspired the development of artificial morphing matter. These materials …

Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing
catalytic processes and develo** new, better‐performing catalysts. Machine learning (ML) …

Green hydrogen production is crucial for a sustainable future, but current catalysts for the
oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce …

Develo** efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a
formidable research challenge. The chain growth and CO insertion requirements demand …

Zero-dimensional (0D) nano-carbons, including graphene quantum dots, nanodiamonds,
and carbon dots, represent the new generation of carbon-based nanomaterials with …

A new class of membranes that can separate ions of similar size and charge is highly
desired for resource recovery, water reuse and energy storage technologies. These …