Topic: Data-Driven Discovery and Optimization of Nanomaterials

This Special Issue provides a platform for publishing research on data-driven discovery and optimization of nano-sized or nanostructured materials, including low-dimensional materials (nanoparticles, nanowires, two-dimensional materials, thin films), nanocomposites, and bulk nanostructured solids. These include advanced materials—such as high-entropy alloys, high-entropy ceramics, materials for electrochemical energy storage, semiconductors, multiferroic materials, photovoltaic materials, and catalysts for oxygen reduction, oxygen evolution, or hydrogen evolution reactions—for structural or functional applications. Machine learning methods are combined with datasets obtained from conventional computational simulations (ab initio simulation, molecular dynamics, Monte Carlo methods) and/or high-throughput experiments to design and optimize nanomaterials with enhanced performance.

Original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

● First-principles prediction on novel physical and chemical properties of nanomaterials;

● Data-driven discovery of structural-property relationships in nanomaterials;

● Atomistic simulation of nanomaterials based on deep-learning interatomic potentials;

● Studies on physical and chemical properties of novel nanomaterials using machine learning.

Machine learning, data-driven materials discovery, deep-learning interatomic potentials, first-principles prediction, structural-property relationships