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.

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  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.

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  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.

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  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.

    Scholar search 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.

    Scholar search 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.

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  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.

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  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.

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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.

    arXiv Scholar search

  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.

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  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.

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  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.

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  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.

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  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.

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  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.

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