Role of thermal history on atomic structure and ductility of ion-irradiated metallic glasses

Maximizing the structural rejuvenation and ductility is one of the most heated debates in the field of metallic glasses (MGs). In this work, molecular dynamics (MD) simulation was implemented to model the ion irradiation effects on the Cu60Zr40 MG with different thermal histories and varied structural heterogeneities. The initial results indicated that the performance of an annealing-quench treatment on the MG induces the atomic configurations with different heterogeneities and potential energy values. The subsequent ion irradiation process also demonstrated that an optimized atomic structure was occurred for achieving maximum rejuvenation and ductility in the CuZr glassy alloy. It was unveiled that the intermediate initial heterogeneity provides an efficient pathway for maximizing the atomic rearrangements under the ion irradiation. It was also suggested that the medium population of Cu-centered clusters in the initial state facilitated the atomic rearrangements during the ion irradiation process. The structural characteristics and atomic reconfigurations for attaining the optimum ductility is discussed in details.

Pre-Crystallization Criteria and Triple Crystallization Kinetic Parameters of Amorphous-Crystalline Phase Transition of As40S45Se15 Alloy

This framework focuses mainly on a detailed study of the pre-crystallization criteria that characterize the As40S45Se15 glassy alloy in various heating rates ranging from 5 to 40 (K/min.) by DSC thermo-grams in the range of (300-575 K). These criteria aim to clarify the relationship of the tendency of glass-forming by the heating rate for the investigated glassy alloy. As well, the present framework demonstrates the criteria of thermal stability. Continuously, the various nucleation and growth pathways. The transformation in activation energy with the volume of the crystalline portion was deduced and, through this, we were able to determine the surface resistance of the analyzed bulk alloy in the crystallization region. The crystalline structure of the study sample was recognized by X-ray diffraction (XRD) and electron scanning microscope (SEM).

Theoretical and Experimental Parameters of the Structure and Crystallization Kinetics of Melt-Quenched As30Te64Ga6 Glassy Alloy

The present framework reports the structural, fundamental parameters, and crystallization kinetics of the melt-quenched As30Te64Ga6 chalcogenide glass. The energy dispersive X-ray analysis of the As30Te64Ga6 glassy system reveals that the constituent element ratio of the investigated bulk sample agrees with the nominal composition. Also, X-ray diffraction (XRD) and Differential Scanning Calorimetry (DSC) were used to characterize crystallization kinetics, and structural properties; respectively. Four characteristic temperatures related to various phenomena are observed in the investigated DSC traces. The first one is Tg that corresponds to the glass transition temperature. The second one is TC1, and TC2 that corresponds to the onset of the double crystallization temperatures. The third one TP1, and TP2 identifies the double peak crystallization temperatures. The last characteristic temperature Tm is the melting point. The XRD analysis indicates the amorphous structure of the as-prepared glassy alloy, while the annealed samples are polycrystalline. The crystallization kinetics of the As30Te64Ga6 bulk are studied under non-isothermal conditions. In addition, the values of various kinetic parameters such as the glass transition activation energy, weight stability standard, and Avrami support were determined. The activation energy of the crystallization process for As30Te64Ga6 glass alloy was calculated using classical methods. The results indicated that the rate of crystallization is related to thermal stability and the ability to form glass. Kinetic parameters have been estimated with some conventional methods and found to be dependent on heating rates (β).

Prediction of Glass Forming Ability of Bulk Metallic Glasses-Machine-Learning

Bulk-Metallic-Glass has been a fascinating class of metallic systems with remarkable corrosion resistance, elastic modulus and wear resistance, while evaluating the glass forming ability has been a very interesting aspect for decades. Machine learning techniques viz., artificial neural networks and random-forest based models have been developed in this work to predict the glass forming ability, given the composition of the bulk metallic glassy alloy. A new criterion of classification of atoms present in a bulk metallic glassy alloy is proposed. Feature importance analysis confirmed that the accuracy of the prediction depends mainly on change in enthalpy of mixing and change in entropy of mixing. However, among the artificial neural network random forest models developed, the former showed a promising accuracy in prediction of the glass formation ability (critical thickness). It has been successfully demonstrated and validated with experimental critical thickness that the glass forming ability can be predicted using an artificial neural network given the elemental composition alone. A computational algorithm was also developed to classify the atoms as big/ small in a given alloy. The outcome of this algorithm was used by models developed by training with experimental data.