Hydrogen is considered a clean and efficient energy carrier crucial for sha** the net-zero
future. Large-scale production, transportation, storage, and use of green hydrogen are …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas
identifying optimal synthesis conditions has been challenging due to numerous synthesis …

There has been a growing effort to replace manual extraction of data from research papers
with automated data extraction based on natural language processing, language models …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …

Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …

With rapid population growth and increased water pollution, the global water situation is
deteriorating. Due to this risk, it is now crucial to efficiently remove organic contaminants …