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We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs)
for predicting the synthesizability of inorganic compounds and the selection of precursors …

In the present review, the merits and demerits of machine learning (ML) in materials science
are discussed, compared with first principles calculations (PDE (partial differential …

Ab initio molecular dynamics (AIMD) is an important technique for studying ion transport
within solid electrolyte and interface effects between electrode and electrolyte, which is …

High-throughput abinitio calculations are the indispensable parts of data-driven discovery of
new materials with desirable properties, as reflected in the establishment of several online …

Nanozymes are unique materials with many valuable properties for applications in
biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed …

Multi-objective machine learning (ML) methods are widely used in the field of materials
because material optimizations are always multi-objective. Traditional multi-objective …

Directly applying big language models for material and molecular design is not
straightforward, particularly for real-scenario cases, where experimental validation accuracy …

The electronic properties of 36 ternary III-IB-VI monolayers have been investigated using a
correlation study, in which a comprehensive study of the band structure of these alloys is …

Molecules and materials are the foundation for the development of modern advanced
industries such as energy storage systems and semiconductor devices. However, traditional …