2017
DOI: 10.1016/j.actamat.2016.10.007
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Influence of grain boundaries on the radiation-induced defects and hydrogen in nanostructured and coarse-grained tungsten

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Cited by 81 publications
(79 citation statements)
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“…Tungsten, the metal with the highest melting point, has many unique physical properties such as high density, high hardness, low vapor pressure, low thermal expansion coefficient, and excellent thermal conductivity . Due to these outstanding properties, tungsten and its alloys have been widely applied in the fields of defense, medical, and nuclear fusion.…”
Section: Introductionmentioning
confidence: 99%
“…Tungsten, the metal with the highest melting point, has many unique physical properties such as high density, high hardness, low vapor pressure, low thermal expansion coefficient, and excellent thermal conductivity . Due to these outstanding properties, tungsten and its alloys have been widely applied in the fields of defense, medical, and nuclear fusion.…”
Section: Introductionmentioning
confidence: 99%
“…To date, it is generally believed that hydrogen bubble nucleation by self-clustering like the case of helium in metals [5,6] would be impossible given the H-H strong repulsion or very weak attraction in metals [7][8][9][10][11][12][13][14], and that, as suggested by many previous studies [4,7,8,10,11], hydrogen bubble nucleation would require the presence of lattice defects. The hydrogen bubble nucleation can be either heterogeneous, relating to grain boundaries, [15][16][17][18] dislocations, [18] and impurities, [7,19] or homogeneous, arising from the aggregation of vacancies or vacancy-hydrogen complexes. [20,21] However, hydrogen bubble formation in metals with extremely low concentration of the lattice defects has been clearly observed in many experiments.…”
Section: Introductionmentioning
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%