arXiv subjects

Diego Ibarra Hoyos

Publications and source records attributed to Diego Ibarra Hoyos.

4 recordsLinked to original sources

arXiv2025

Quantum-Annealing Enhanced Machine Learning for Interpretable Phase Classification of High-Entropy Alloys

High entropy alloys (HEAs) offer unprecedented compositional flexibility for designing advanced materials, yet predicting their crystallographic phases remains a key bottleneck due to limited data and complex phase formation behavior. Here, we present a quantum-enhanced machine learning framework that leverages quantum annealing to enhance phase classification in HEAs. Our pipeline integrates Quantum Boosting (QBoost) for interpretable feature selection and classification, with Quantum Support Vector Machines (QSVM) that use quantum-enhanced kernels to capture nonlinear relationships between physical descriptors. By reformulating both models as Quadratic Unconstrained Binary Optimization (QUBO) problems, we exploit the efficient sampling capabilities of quantum annealers to achieve rapid training and robust generalization, demonstrating notable runtime reductions relative to classical baselines in our setup. We target six key phases: FCC, BCC, Sigma, Laves, Heusler, and AlXY B2, and benchmark model performance using both cross-validation and a rigorously curated test set of prior experimentally synthesized HEAs. The results confirm strong alignment between predicted and measured phases. Our findings demonstrate that quantum-enhanced classifiers match or exceed classical models in accuracy and offer insights grounded in interpretable physical descriptors. This work constitutes an important step toward practical quantum acceleration in materials discovery pipelines.

cond-mat.mtrl-sci↗

arXiv2024

High Strength Refractory AlHfNbTiV B2 High Entropy Alloys with High Fracture Strains

We demonstrate the development of a series of refractory high-entropy alloys containing aluminum AlRHEAs in the ordered BCC-B2 phase by varying the aluminum content within 10 to 25 atomic percent, with the goal of high strength and good ductility synergy. The AlRHEAs obtained are found to show promising potential for high-temperature applications. The incorporation of Al lowers the density and promotes the long-range atomic ordering, which in turn stabilizes the B2 formation, and strengthens the material but usually deteriorates ductility. Several B2 AlRHEAs that contain a combination of Ti, Hf, Nb, and V with moderate to high Poisson ratios are investigated for high strength and ductility. Furthermore, through statistical analysis, we identify a valley around the valence electron concentration VEC of 6 where low ductility is prominently observed. Machine-learning models are employed to screen the vast compositional space of AlRHEA alloys to predict B2 formation and toughness indicated by the yield strength and fracture strain. High prediction accuracies are achieved. As the Al content decreases, the B2 atomic ordering decreases, compression yield strengths decrease from 1500 MPa to 1200 MPa, and compression fracture strains increase from 0.06 to over 0.5. Notably, Al10Hf20Nb22Ti33V15 retains a compression yield strength exceeding 800 MPa up to 700 C, tensile yield strength of 1100 MPa, and fracture strain of 0.083. Our findings on enhancing ductility in pure B2 alloys pave the way for further research on Al-RHEA superalloys, striving to achieve high strength and ductility, reduced density, and improved oxidation resistance.

cond-mat.mtrl-sci↗

arXiv2023

Integrated Design of Aluminum-Containing High-entropy Refractory B2 Alloys with Synergy of High Strength and Ductility

Refractory high-entropy alloys, RHEAs, are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations to effectively navigate phase-selection and mechanical-properties predictions, developing aluminum-containing RHEAs in single-phase ordered B2 alloys demonstrating both high strength and ductility. These aluminum-containing RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.6 GPa, fracture strains exceeding 50 percent, and significant high-temperature strength retention. They also demonstrate a tensile yield strength of 1.1 GPa with a tension ductility of 6.3 percent. Besides, we identify a valence-electron-count domain for alloy brittleness with the explanation from density-functional theory and provide crucial insights into elements' influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles that govern the mechanical properties of advanced structural alloys.

cond-mat.mtrl-sci↗

arXiv2023

Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases

The design of high-entropy alloys (HEA) with desired properties is challenging due to their large compositional space. While various machine learning (ML) models can predict specific HEA solid-solution phases (SS), predicting high-entropy intermetallic phases (IM) is underdeveloped due to limited datasets and inadequate ML features. This paper introduces feature engineering-assisted ML models that achieve detailed phase classification and high accuracy. By combining phase-diagram-based and physics-based features, it is found that the ML models trained on the Random Forest (RF) and Support Vector Machine (SVM) regressors, are able to classify individual SS and common IM (Sigma, Laves, Heusler, and refractory B2 phases) with accuracies ranging from 80 - 94%. The machine-learned features also enable the interpretation of IM formation. Furthermore, the efficacies of the RF, SVM, and neural network (NN) models are critically evaluated. The phase classification accuracies are found to decrease upon utilizing the NN model to train the datasets. The accuracy of the model prediction is validated by synthesizing 86 new alloys. This approach provides a practical and robust framework for guiding HEA phase design, particularly for technologically significant IM phases.

cond-mat.mtrl-sci↗