Formation of galvanic cells between constituent phases is largely responsible for corrosion in Mg-based alloys.We develop a methodology to calculate the electrochemical potentials of intermetallic compounds and alloys...Formation of galvanic cells between constituent phases is largely responsible for corrosion in Mg-based alloys.We develop a methodology to calculate the electrochemical potentials of intermetallic compounds and alloys using a simple model based on the Born-Haber cycle.Calculated electrochemical potentials are used to predict and control the formation of galvanic cells and minimize corrosion.We demonstrate the applicability of our model by minimizing galvanic corrosion in Mg-3wt%Sr-x Zn alloy by tailoring the Zn composition.The methodology proposed in this work is applicable for any general alloy system and will facilitate efficient design of corrosion resistant alloys.展开更多
We present a deep-learning framework,CrysXPP,to allow rapid and accurate prediction of electronic,magnetic,and elastic properties of a wide range of materials.CrysXPP lowers the need for large property tagged datasets...We present a deep-learning framework,CrysXPP,to allow rapid and accurate prediction of electronic,magnetic,and elastic properties of a wide range of materials.CrysXPP lowers the need for large property tagged datasets by intelligently designing an autoencoder,CrysAE.The important structural and chemical properties captured by CrysAE from a large amount of available crystal graphs data helped in achieving low prediction errors.Moreover,we design a feature selector that helps to interpret the model’s prediction.Most notably,when given a small amount of experimental data,CrysXPP is consistently able to outperform conventional DFT.A detailed ablation study establishes the importance of different design steps.We release the large pre-trained model CrysAE.We believe by fine-tuning the model with a small amount of property-tagged data,researchers can achieve superior performance on various applications with a restricted data source.展开更多
Inspired by the controversy over tensile deformation modes of single-crystalline 〈110〉/{111} Au nanowires, we investigated the dependency of the deformation mode on diameters of nanowires using the molecular dynamic...Inspired by the controversy over tensile deformation modes of single-crystalline 〈110〉/{111} Au nanowires, we investigated the dependency of the deformation mode on diameters of nanowires using the molecular dynamics technique. A new criterion for assessing the preferred deformation mode-slip or twin propagation--of nanowires as a function of nanowire diameter is presented. The results demonstrate the size-dependent transition, from superplastic deformation mediated by twin propagation to the rupture by localized slips in deformed region as the nanowire diameter decreases. Moreover, the criterion was successfully applied to explain the superplastic deformation of Cu nanowires.展开更多
基金the Technology Innovation Program(20012502)funded by the Ministry of Trade,Industry and Energy and National Research Foundation of Korea(NRF)Grant funded by Ministry of Science and ICT(MSIT)(NRF-2019R1A2C1089593,NRF2020M3H4A3106736,NRF-2021M3H4A6A01045764)。
文摘Formation of galvanic cells between constituent phases is largely responsible for corrosion in Mg-based alloys.We develop a methodology to calculate the electrochemical potentials of intermetallic compounds and alloys using a simple model based on the Born-Haber cycle.Calculated electrochemical potentials are used to predict and control the formation of galvanic cells and minimize corrosion.We demonstrate the applicability of our model by minimizing galvanic corrosion in Mg-3wt%Sr-x Zn alloy by tailoring the Zn composition.The methodology proposed in this work is applicable for any general alloy system and will facilitate efficient design of corrosion resistant alloys.
文摘We present a deep-learning framework,CrysXPP,to allow rapid and accurate prediction of electronic,magnetic,and elastic properties of a wide range of materials.CrysXPP lowers the need for large property tagged datasets by intelligently designing an autoencoder,CrysAE.The important structural and chemical properties captured by CrysAE from a large amount of available crystal graphs data helped in achieving low prediction errors.Moreover,we design a feature selector that helps to interpret the model’s prediction.Most notably,when given a small amount of experimental data,CrysXPP is consistently able to outperform conventional DFT.A detailed ablation study establishes the importance of different design steps.We release the large pre-trained model CrysAE.We believe by fine-tuning the model with a small amount of property-tagged data,researchers can achieve superior performance on various applications with a restricted data source.
文摘Inspired by the controversy over tensile deformation modes of single-crystalline 〈110〉/{111} Au nanowires, we investigated the dependency of the deformation mode on diameters of nanowires using the molecular dynamics technique. A new criterion for assessing the preferred deformation mode-slip or twin propagation--of nanowires as a function of nanowire diameter is presented. The results demonstrate the size-dependent transition, from superplastic deformation mediated by twin propagation to the rupture by localized slips in deformed region as the nanowire diameter decreases. Moreover, the criterion was successfully applied to explain the superplastic deformation of Cu nanowires.
