2015
DOI: 10.1088/0029-5515/55/11/113009
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H trapping and mobility in nanostructured tungsten grain boundaries: a combined experimental and theoretical approach

Abstract: The trapping and mobility of hydrogen in nanostructured tungsten grain boundaries (GBs) have been studied by combining experimental and density functional theory (DFT) data. Experimental results show that nanostructured W coatings with a columnar grain structure and a large number of (1 1 0)/(2 1 1) interfaces retain more H than coarsed grained W samples. To investigate the possible influence of GBs on H retention, a complete energetic analysis of a non-coherent W(1 1 0)/W(1 1 2) interface has been performed e… Show more

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Cited by 36 publications
(30 citation statements)
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References 65 publications
(90 reference statements)
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“…In intimate connection with artificial intelligence concepts, appropriately configured computational programming and access to high-throughput resources allows a machine to extract patterns and learn from pre-existing data bases much faster and more accurately than ever before, iterating the processes until fully satisfying relationships and results have been obtained and, in doing so, reducing human intervention to a minimum. Interatomic potential fitting for ulterior MD modelling (Atomistica [311], Atomicrex [312], Potfit [313], OpenKIM [314]), hybrid DFT(Density Functional Theory)-MD simulations (Gaussian approximation potential (GAP) [315], SNAP [316]) or DFT-KMC [317,318] and finite element [319][320][321] multiscale modelling approaches are paradigmatic examples of advanced materials simulation methodologies that make use of machine learning-related techniques. The scaling up from ab-initio and atomistic time and lengths to mesoscopic and macroscopic scales benefits enormously from these kinds of procedures, as stated in several different recent reviews ( Figure 20).…”
Section: Machine Learningmentioning
confidence: 99%
“…In intimate connection with artificial intelligence concepts, appropriately configured computational programming and access to high-throughput resources allows a machine to extract patterns and learn from pre-existing data bases much faster and more accurately than ever before, iterating the processes until fully satisfying relationships and results have been obtained and, in doing so, reducing human intervention to a minimum. Interatomic potential fitting for ulterior MD modelling (Atomistica [311], Atomicrex [312], Potfit [313], OpenKIM [314]), hybrid DFT(Density Functional Theory)-MD simulations (Gaussian approximation potential (GAP) [315], SNAP [316]) or DFT-KMC [317,318] and finite element [319][320][321] multiscale modelling approaches are paradigmatic examples of advanced materials simulation methodologies that make use of machine learning-related techniques. The scaling up from ab-initio and atomistic time and lengths to mesoscopic and macroscopic scales benefits enormously from these kinds of procedures, as stated in several different recent reviews ( Figure 20).…”
Section: Machine Learningmentioning
confidence: 99%
“…However, the formation of brittle tungsten silicide may be disadvantageous for the powder metallurgical production of bulk W-Cr-Si alloys if a good workability is needed [49,50]." Several oxidation tests have been made with different W-based alloys at a temperature of 1073 K for 3 hours (see Table 1 taken from [69]). Table 1 shows good results for Tungsten-Chromium-Titanium alloys.…”
Section: Materials Based On Berylliummentioning
confidence: 99%
“…The value of formation energy of a vacancy from a GB in W has been published in Refs. [161,162]. This value (E f (V) GB = 1.6 eV) is much lower than the formation energy of a vacancy in bulk tungsten (E f (V) Bulk = 3.23 eV).…”
Section: Discussionmentioning
confidence: 81%
“…GBs with a migration energy lower than in the bulk [161,162]. Thus, GBs might act as preferential diffusion path for H atoms [163].…”
Section: Introductionmentioning
confidence: 99%
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