Effect of solid-solution strengthening on deformation mechanisms and strain hardening in medium-entropy V_(1-x)Cr_(x)CoNi alloys

High-and medium-entropy alloys(HEAs and MEAs)possess high solid-solution strength.Numerous investigations have been conducted on its impact on yield strength,however,there are limited reports regarding the relation between solid-solution strengthening and strain-hardening rate.In addition,no attempt has been made to account for the dislocation-mediated plasticity;most works focused on twinning-or transformation-induced plasticity(TWIP or TRIP).In this work we reveal the role of solidsolution strengthening on the strain-hardening rate via systematically investigating evolutions of deformation structures by controlling the Cr/V ratio in prototypical V_(1-x)Cr_(x)CoNi alloys.Comparing the TWIP of CrCoNi with the dislocation slip of V_(0.4)Cr_(0.6)CoNi,the hardening rate of CrCoNi was superior to slip-band refinements of V_(0.4)Cr_(0.6)CoNi due to the dynamic Hall-Petch effect.However,as V content increased further to V_(0.7)Cr_(0.3)CoNi and VCoNi,their rate of slip-band refinement in V_(0.7)Cr_(0.3)CoNi and VCoNi with high solid-solution strength surpassed that of CrCoNi.Although it is generally accepted in conventional alloys that deformation twinning results in a higher strain-hardening rate than dislocation-mediated plasticity,we observed that the latter can be predominant in the former under an activated huge solid-solution strengthening effect.The high solid-solution strength lowered the cross-slip activation and consequently retarded the dislocation rearrangement rate,i.e.,the dynamic recovery.This delay in the hardening rate decrease,therefore,increased the strain-hardening rate,results in an overall higher strain-hardening rate of V-rich alloys.

Towards understanding structure–property relations in materials with interpretable deep learning

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties.To address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships.The proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials datasets.Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art models.Furthermore,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties.These properties encompass molecular orbital energies and the formation energies of crystals.The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

Analysis of molten metal spreading and solidification behaviors utilizing moving particle full-implicit method

To retrieve the fuel debris in Fukushima Daiichi Nuclear Power Plants(1F),it is essential to infer the fuel debris distribution.In particular,the molten metal spreading behavior is one of the vital phenomena in nuclear severe accidents because it determines the initial condition for further accident scenarios such as molten core concrete interaction(MCCI).In this study,the fundamental molten metal spreading experiments were performed with different outlet diameters and sample amounts to investigate the effect of the outlet for spreading-solidification behavior.In the numerical analysis,the moving particle full-implicit method(MPFI),which is one of the particle methods,was applied to simulate the spreading experiments.In the MPFI framework,the melting-solidification model including heat transfer,radiation heat loss,phase change,and solid fraction-dependent viscosity was developed and implemented.In addition,the difference in the spreading and solidification behavior due to the outlet diameters was reproduced in the calculation.The simulation results reveal the detailed solidification procedure during the molten metal spreading.It is found that the viscosity change and the solid fraction change during the spreading are key factors for the free surface condition and solidified materials.Overall,it is suggested that the MPFI method has the potential to simulate the actual nuclear melt-down phenomena in the future.