Lei Zhang^1,2,*, Hangyuan Deng^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1-11, 2025, DOI:10.32604/cmc.2025.062666 - 26 March 2025

Abstract NJmat is a user-friendly, data-driven machine learning interface designed for materials design and analysis. The platform integrates advanced computational techniques, including natural language processing (NLP), large language models (LLM), machine learning potentials (MLP), and graph neural networks (GNN), to facilitate materials discovery. The platform has been applied in diverse materials research areas, including perovskite surface design, catalyst discovery, battery materials screening, structural alloy design, and molecular informatics. By automating feature selection, predictive modeling, and result interpretation, NJmat accelerates the development of high-performance materials across energy storage, conversion, and structural applications. Additionally, NJmat serves as an… More >

Hideki Mori^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012935

Abstract To design the mechanical strength of iron, it is very important to clarify the detail of dislocation dynamics in Body-Centered Cubic (BCC) Iron. The dislocation core structures are typically confined to the nanometer scale.
This implies that the resistance force from discrete atomic columns has a direct bearing on dislocation mobility.
Recently, we've developed a high-fidelity inter-atomic potential leveraging neural networks built upon density functional theory (DFT) data. By conducting dislocation dynamics simulations, we've addressed shortcomings inherent in classical inter-atomic potential approaches. Nonetheless, a significant challenge persists: a three- to four-fold deviation exists between More >

Shihao Zhu¹, Fanshun Meng¹, Shihao Zhang¹, Shigenobu Ogata^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012058

Abstract Hydrogen dissolves in most metallic materials and causes hydrogen embrittlement (HE). This is particularly relevant to iron, a widely used material in engineering applications, which can degrade when exposed to high-pressure hydrogen gas under high temperature. As the hydrogen concentration is a primary factor controls defects properties in metals [1], it is crucial to understand the hydrogen solubility under high H2 pressure, but this aspect remains unclear. At low H2 pressures, the solubility of hydrogen can be predicted using Sieverts’ law [2], which states that the solubility increases proportionally to the square root of H2… More >