基于量子力学基本原理的密度泛函理论(DFT)可以有效预测材料性质,如今它在物理、化学、材料、生物等领域的研究工作中已得到广泛应用。随着对材料领域的深入研究,进一步提升DFT的精度和效率已成为迫切需求,但精度和效率往往不可兼得。近年来,在AI for Science理念的引领下,基于深度学习的电子结构计算方法迅速发展,有望解决电子结构计算中精度和效率不能两全的困境。然而,只有稳定可靠的DFT软件平台才能保证深入研究和持续推动AI辅助电子结构计算方法的广泛应用,这既充满挑战又蕴含机遇。在此背景下,本文主要从物理模型、深度学习算法和软件开发3方面介绍国产开源DFT软件ABACUS(atomic-orbital based ab-initio computation at UStc,中文名原子算筹)从开发2.2版本(2022年4月发布)到发布3.7版本(2024年7月发布)期间的进展及其与深度学习算法的融合和应用。
The Hastelloy C22 coatings on Q235 steel substrate were produced by high power diode laser cladding technique. Their corrosion behaviors in static and cavitation hydrochloric, sulfuric and nitric acid solutions were i...
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The Hastelloy C22 coatings on Q235 steel substrate were produced by high power diode laser cladding technique. Their corrosion behaviors in static and cavitation hydrochloric, sulfuric and nitric acid solutions were investigated. The electrochemical results show that corrosion resistance of coatings in static acid solutions is higher than that in cavitation ones. In each case, coating corrosion resistance in descending order is in nitric, sulfuric and hydrochloric acid solutions. Obvious erosion-corrosion morphology and serious intercrystalline corrosion of coating are noticed in cavitation hydrochloric acid solution. This is mainly ascribed to the aggressive ions in hydrochloric acid solution and mechanical effect from cavitation bubbles collapse. While coating after corrosion test in cavitation nitric acid solution shows nearly unchanged surface morphology. The results indicate that the associated action of cavitation and property of acid solution determines the corrosion development of coating. Hastelloy C22 coating exhibits better corrosion resistance in oxidizing acid solution for the stable formation of dense oxide film on the surface.
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