Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

As a result of global sustainable development, natural fiber composites (NFCs) have
become increasingly attractive due to their remarkable performance, novel functionality, and …

The methodology development and applications of ReaxFF molecular dynamics (ReaxFF
MD) in unraveling the complex reactions and kinetics for pyrolysis and oxidation of organic …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

The focused ion beam (FIB) is a powerful tool for fabrication, modification, and
characterization of materials down to the nanoscale. Starting with the gallium FIB, which was …

The reactive force field (ReaxFF) model bridges the gap between traditional classical
models and quantum mechanical (QM) models by incorporating dynamic bonding and …

Currently, computational materials science involves human–computer interaction through
coding in software or neural networks. There is still no direct way for human intelligence …

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing
properties has attracted intense attention. However, predicting peptide assembly at atomic …

Machine learning (ML) is having an increasing impact on the physical sciences,
engineering, and technology and its integration into molecular simulation frameworks holds …

A thorough understanding of the kinetics and dynamics of combusting mixtures is of
considerable interest, especially in regimes beyond the reach of current experimental …