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The Cluster Expansion (CE) Method encounters significant computational challenges in multicomponent systems due to the computational expense of generating training data through density functional theory (DFT) calculations. This work aims to refine the cluster and structure selection processes to mitigate these challenges. We introduce a novel method that significantly reduces the computational load associated with the calculation of fitting parameters. This method employs a Graph Neural Network (GNN) model, leveraging the M3GNet network, which is trained using a select subset of DFT calculations at each ionic step. The trained surrogate model excels in predicting the volume and energy of the final structure for a relaxation run. By employing this model, we sample thousands of structures and fit a CE model to the energies of these GNN-relaxed structures. This approach, utilizing a large training dataset, effectively reduces the risk of overfitting, yielding a CE model with a root-mean-square error (RMSE) below 10 meV/atom. We validate our method's effectiveness in two test cases: the (Cr,Hf,Mo,Ta,Ti,Zr)B$_2$ diboride system and the Refractory High-Entropy Alloy (HEA) AlHfNbTaTiZr system. Our findings demonstrate the significant advantages of integrating a GNN model, specifically the M3GNet network, with CE methods for the efficient predictive analysis of chemical ordering in High Entropy Materials. The accelerating capabilities of the hybrid ML-CE approach to investigate the evolution of Short Range Ordering (SRO) in a large number of stoichiometric systems. Finally, we show how it is possible to correlate the strength of chemical ordering to easily accessible alloy parameters., Comment: 20 pages, 11 figures, presented at TMS 2024

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of the elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. We also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions., Comment: 17 Pages; 10 Figures; 3 Tables

ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHAD-based elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg$_2$(Si$_x$Sn$_{1-x}$) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at \href{http://microstructures.net}{http://microstructures.net}.

MAX phases have attracted increased attention due to their unique combination of ceramic and metallic properties. Point-defects are known to play a vital role in the structural, electronic and transport properties of alloys in general and this system in particular. As some MAX phases have been shown to be stable in non-stoichiometric compositions, it is likely that such alloying effects will affect the behavior of lattice point defects. This problem, however, remains relatively unexplored. In this work, we investigate the alloying effect on the structural-stability, energy-stability, electronic-structure, and diffusion barrier of point defects in MAX phase alloys within a first-principles density functional theory framework. The vacancy (V$_{M}$, V$_{A}$, V$_{X}$) and antisite (M-A; M-X) defects are considered with M and A site disorder in (Zr-M)$_{2}$(AA${'}$)C, where M=Cr,Nb,Ti and AA${'}$=Al, Al-Sn, Pb-Bi. Our calculations suggest that the chemical disorder helps lower the V$_{A}$ formation energies compared to V$_{M}$ and V$_{X}$. The V$_{A}$ diffusion barrier is also significantly reduced for M-site disorder compared to their ordered counterpart. This is very important finding because reduced barrier height will ease the Al diffusion at high-operating temperatures, which will help the formation of passivating oxide layer (i.e., Al$_{2}$O$_{3}$ in aluminum-based MAX phases) and will slow down or stop the material degradation. We believe that our study will provide a fundamental understanding and an approach to tailor the key properties that can lead to the discovery of new MAX phases., Comment: 26 pages, 18 figures, 2 Tables