This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

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 …

We introduce our proprietary Materials Informatics (MI) technologies and our chemistry-
oriented methodology for exploring new inorganic functional materials. Using machine …

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

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 …

Despite many accessible AI models that have been developed, it is an open challenge to
fully exploit interpretable insights to enable effective materials design and develop materials …

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

Partnerships and collaboration drive innovative graduate training in materials informatics |
Science Advances news careers commentary Journals Science Science brought to you byGoogle …

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 …

Nanotechnology has elucidated scientific phenomena of various materials at the nano-level.
The next step in materials developments is to build up materials, especially condensed …