Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

Nowadays, the research on materials science is rapidly entering a phase of data‐driven
age. Machine learning, one of the most powerful data‐driven methods, have been being …

The rising demand for energy has accelerated the search for clean and renewable sources
and newer approaches towards efficient energy management. One of the most promising …

With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …

Energy harvesting technology is attracting attention as" enabling technology" that expands
the use and opportunities of IoT utilization, enriches lives and enhances social resilience …

This study combines machine learning (ML) and high-throughput calculations to uncover
new ternary electrides in the A 2 BC 2 family of compounds with the P 4/mbm space group …

Nanomaterials (NMs) have developed quickly and cover various fields, but research on
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …

The spin Seebeck effect (SSE) refers to the generation of a spin current as a result of a
temperature gradient in a magnetic material, which can be detected electrically via the …

Data-driven models are central to scientific discovery. In efforts to achieve state-of-the-art
model accuracy, researchers are employing increasingly complex machine learning …

Thermoelectric conversion technology based on thermoelectric materials can directly
convert heat and electricity and is extensively used in waste heat recovery, semiconductor …