Siya Zhu

Publications

Research Output

Publications in alloy thermodynamics, phase diagram prediction, metallic glasses, cluster expansion, and machine-learning-driven materials discovery.

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First-author publications

Lead-author Work

  1. Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling

    Zhu, S. and Arroyave, R. Computational Materials Science, 270, 114752, 2026.

    Introduces PAIPAI, a parallel Monte Carlo and machine-learning-potential framework for resolving low-energy atomic arrangements in defect-rich high-entropy alloys. Surface segregation, interstitial clustering, and grain-boundary chemistry are treated within one workflow, with selected configurations validated by first-principles calculations.

    Article arXiv PAIPAI

  2. Computational Study of Density Fluctuation-Facilitated Shear Bands Formation in Bulk Metallic Glasses

    Zhu, S., Eckert, H., Curtarolo, S., Schroers, J. and van de Walle, A. npj Computational Materials, 12, 157, 2026.

    Uses controlled atomistic density fluctuations to isolate how low-density regions promote shear localization in bulk metallic glasses. The calculations connect fluctuation size and magnitude to the critical strain for shear-band formation, providing a structural route for understanding free volume and rejuvenation.

    Open access article

  3. Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: Opportunities and challenges

    Zhu, S., Sariturk, D. and Arroyave, R. Acta Materialia, 286, 120747, 2025.

    Benchmarks universal machine-learning interatomic potentials in an atomistic-to-CALPHAD workflow across binary and ternary alloys. The study demonstrates speedups beyond three orders of magnitude relative to DFT, while identifying the accuracy requirements and failure modes that matter for phase stability.

    Article arXiv

  4. Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy

    Zhu, S., Sariturk, D. and Arroyave, R. npj Computational Materials, 11, 340, 2025.

    Presents PhaseForge, an automated workflow that connects machine-learning potentials, ATAT, molecular dynamics, vibrational calculations, and CALPHAD-compatible databases. Case studies from binary to quinary alloys show how phase-diagram topology can serve as an application-level benchmark for interatomic potentials.

    Open access article PhaseForge

  5. Special glass structures for first principles studies of bulk metallic glasses

    Zhu, S., Schroers, J., Curtarolo, S., Eckert, H. and van de Walle, A. Acta Materialia, 262, 119456, 2024.

    Develops Special Glass Structures: compact atomistic cells optimized to reproduce the local geometric statistics of much larger metallic-glass models. Implemented in MAST, the approach makes accurate first-principles calculations of amorphous mechanical and electronic properties substantially more tractable.

    Article arXiv MAST

  6. Probing phase stability in CrMoNbV using cluster expansion method, CALPHAD calculations and experiments

    Zhu, S., Shittu, J., Perron, A., Nataraj, C., Berry, J., McKeown, J. T., van de Walle, A. and Samanta, A. Acta Materialia, 255, 119062, 2023.

    Combines cluster expansion, Monte Carlo sampling, CALPHAD, and targeted experiments to map the stability of the BCC solid solution in the four-component Cr–Mo–Nb–V system. The resulting high-dimensional phase picture identifies composition and temperature windows relevant to refractory high-entropy-alloy design.

    Article

  7. Computational Assessment of Novel Predicted Compounds in Ni-Re Alloy System

    Zhu, S. and van de Walle, A. Journal of Phase Equilibria and Diffusion, 42(2), 315-320, 2021.

    Bridges high-throughput ground-state predictions and finite-temperature CALPHAD modeling to test two proposed Ni–Re intermetallic compounds. The analysis confirms their stability at practical synthesis temperatures and explains why earlier thermodynamic assessments could have missed them.

    Open access article

  8. A simple method for understanding the triangular growth patterns of transition metal dichalcogenide sheets

    Zhu, S. and Wang, Q. AIP Advances, 5(10), 2015.

    Introduces an efficient matrix description for micron-scale transition-metal-dichalcogenide flakes. Applied to MoS2, the model links edge energetics and sulfur chemical potential to the triangular morphologies observed in synthesis.

    Open access article

Manuscripts

Under Review

  1. Influence of Coherent Elastic Strain on Phase Separation in BCC Nb-V Alloys

    Zhu, S. and Arroyave, R. Submitted to Acta Materialia, 2026. arXiv:2605.01031.

    Extends phase-diagram thermodynamics by explicitly enforcing coherent elastic compatibility. In Nb–V, coherency narrows the miscibility gap, lowers the critical temperature, and makes decomposition compositions depend on both temperature and overall alloy composition.

    arXiv

  2. Thermodynamically metastable Cu-V solid solution enabled by pseudomorphism

    Tavakolzadeh, M., Sheu, E., Motallebi, R., Zhu, S., Arroyave, R., Xie, K. and Demkowicz, M. Submitted to Nano Letters, 2026.

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Other publications

Collaborative Work

  1. Learning Materials Interatomic Potentials via Hybrid Invariant-Equivariant Architectures

    Yan, K., Bohde, M., Kryvenko, A., Xiang, Z., Zhao, K., Zhu, S., Kolachina, S., Sariturk, D., Xie, J., Arroyave, R., Qian, X., Qian, X. and Ji, S. Transactions on Machine Learning Research, 2026.

    OpenReview

  2. Predicting Interstitial Solutes in Refractory Complex Concentrated Alloys via a Combined Experimental and Computational Workflow

    Huang, A., Zhu, S., Belcher, C., Rigsby, R., Apelian, D., Arroyave, R. and Lavernia, E. J. Acta Materialia, 308, 122019, 2026.

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  3. Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data

    Kunselman, C., Zhu, S., Sariturk, D. and Arroyave, R. Journal of Phase Equilibria and Diffusion, 2025.

    Scholar search

  4. Soliquidy: a descriptor for atomic geometrical confusion

    Eckert, H., Kube, S. A., Divilov, S., Guest, A., Zettel, A. C., Hicks, D., Griesemer, S. D., Hotz, N., Campilongo, X., Zhu, S. and van de Walle, A. npj Computational Materials, 11, 40, 2025.

    Scholar search

  5. Bayesian active machine learning for Cluster expansion construction

    Chen, H., Samanta, S., Zhu, S., Eckert, H., Schroers, J., Curtarolo, S. and van de Walle, A. Computational Materials Science, 231, 112571, 2024.

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  6. Revisiting the SGTE lattice stability of bcc aluminum

    van de Walle, A., Samanta, S., Nataraj, C., Zhu, S., Chen, H., Liu, H. and Arroyave, R. Calphad, 83, 102628, 2023.

    Scholar search

  7. Accurate parameterization of the kinetic energy functional

    Kumar, S., Borda, E. L., Sadigh, B., Zhu, S., Hamel, S., Gallagher, B., Bulatov, V., Klepeis, J. and Samanta, A. The Journal of Chemical Physics, 156(2), 2022.

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  8. Accurate parameterization of the kinetic energy functional for calculations using exact-exchange

    Kumar, S., Sadigh, B., Zhu, S., Suryanarayana, P., Hamel, S., Gallagher, B., Bulatov, V., Klepeis, J. and Samanta, A. The Journal of Chemical Physics, 156(2), 2022.

    Scholar search

  9. Interactive exploration of high-dimensional phase diagrams

    van de Walle, A., Chen, H., Liu, H., Nataraj, C., Samanta, S., Zhu, S. and Arroyave, R. JOM, 74(9), 3478-3486, 2022.

    Scholar search

  10. Transformation of monolayer MoS2 into multiphasic MoTe2: Chalcogen atom-exchange synthesis route

    Fang, Q., Zhang, Z., Ji, Q., Zhu, S., Gong, Y., Zhang, Y., Shi, J., Zhou, X., Gu, L., Wang, Q. and Zhang, Y. Nano Research, 10(8), 2761-2771, 2017.

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  11. Rhodanine flanked indacenodithiophene as non-fullerene acceptor for efficient polymer solar cells

    Jia, B., Wu, Y., Zhao, F., Yan, C., Zhu, S., Cheng, P., Mai, J., Lau, T. K., Lu, X., Su, C. J. and Wang, C. Science China Chemistry, 60(2), 257-263, 2017.

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  12. A planar electron acceptor for efficient polymer solar cells

    Wu, Y., Bai, H., Wang, Z., Cheng, P., Zhu, S., Wang, Y., Ma, W. and Zhan, X. Energy and Environmental Science, 8(11), 3215-3221, 2015.

    Scholar search