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High-throughput Screening: CALPAHD for Material Designs

Mon, 05 July, 2021

The blossom of compositionally complex alloys (CCAs), which are also known as high-entropy alloys consisting of five or more principal elements [1], is initiating a novel concept for new material development based on the paradigm of process-microstructure-property-performance correlation. CCAs contain a host of scientifically and technologically important alloy systems and corresponding sub-systems, including Cantor alloys, CoCrNi alloys [2]. Although researchers around the world are endeavouring to perform experiments to understand their mechanical and thermodynamic properties, there are many obstacles for a systematic search due to the extraordinary scale of multi-element combinations as well as certain experimental deviations. However, progressive developments of many computational tools are becoming mature and advanced, the efficiency in screening materials from vast datasets tends to be viable as a result of increasing computation power and growing thermodynamic data.

Integrated Computational Materials Engineering (ICME) framework is associated with establishment proper models and simulation tools that enable exploitation in terms of above paradigm. It is a forward mode of exploration, which has potential to design material based on specific needs for performance linking computational models across the range of length scales. For instance, the general strategy for designing corrosion resistance alloys is to either develop homogeneous solid solution alloys with phase and compositional stability minimising structural and chemical non-uniformities [3], or multi-phase materials with balanced alloying elements in formed phases for optimal corrosion properties [4]. It is challenging to define corrosion-resistance features and attributes that strength ability against degradation. In addition, there is no unifying model with calculable fundamental parameters applying computational tools for corrosion resistance alloys despite success in new materials developments for improved mechanical or electrical properties [5]. Thus, it is imperative to investigate process-microstructure-property-performance for developing appropriate corrosion-resistance models in CCA systems. Integration of CALculation of PHAse Diagrams (CALPHAD) method [6] into ICME approach tends to accelerate discovery of novel materials via high-throughput screening, which is a promising method for processing and calculating vast volume of datasets in a good efficiency.

Fig.1 shows workflow of high-throughput calculation using CALPHAD method. The compositions in datasets [7] can be extracted independently and converted into atomic or weight fraction depending on unit employed in following calculations. Subsequently, thermodynamic properties, including phases and corresponding fractions, Gibbs energy, enthalpy etc., are calculated in CALPHAD programme in terms of inputting compositions. Collections of thermodynamic data can then be aggregated the data frame manipulated attributes of processing conditions and microstructures as shown in bottom right table to differentiate clusters that will be visualised the groups selectively in either single scatter plot or grid plots in matrix, which is illustrated in Fig.2 in form of four thermodynamic properties against elements contained in AlCoCrCuFeNi system. Based on distribution of data points, further machine learning approach, such as k-nearest neighbours algorithm, can be implemented to investigate correlations among variables and derive analytical algorithm for discovering key features of CALPHAD results to optimise models under process-microstructure-property-performance framework.

Article courtesy: ULEIC 

Figure 1. Workflow and data aggregation/visualisation processes for high-throughput calculations using the CALPHAD method
Figure 1. Workflow and data aggregation/visualisation processes for high-throughput calculations using the CALPHAD method
Figure 2. Grid plots of elements vs thermodynamic properties for CALPHAD results in AlCoCrCuFeNi
Figure 2. Grid plots of elements vs thermodynamic properties for CALPHAD results in AlCoCrCuFeNi



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