Siya Zhu

论文

研究成果

研究涵盖合金热力学、相图预测、金属玻璃、团簇展开与机器学习驱动的材料发现。

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第一作者论文

主要作者成果

  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.

    提出 PAIPAI：面向含缺陷高熵合金的并行蒙特卡洛与机器学习原子势框架，用于搜索低能原子构型。该方法在同一工作流中处理表面偏聚、间隙原子团簇及晶界化学，并以第一性原理计算验证关键构型。

    文章 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.

    通过可控的原子尺度密度涨落，分离并量化低密度区域对块体金属玻璃剪切局域化的促进作用。研究建立了涨落尺度、幅度与剪切带临界应变之间的联系，为理解自由体积与结构再年轻化提供了原子图景。

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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.

    在从原子计算到 CALPHAD 的完整链条中，对多种通用机器学习原子势进行系统评估。二元与三元合金算例表明，相比 DFT 可获得超过三个数量级的加速，同时明确了可靠预测相稳定性所需的精度与潜在失效模式。

    文章 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.

    发布自动化工作流 PhaseForge，将机器学习原子势、ATAT、分子动力学、振动自由能与 CALPHAD 数据库连接起来。从二元到五元合金的案例展示了如何用相图拓扑对原子势进行面向实际应用的评价。

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  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.

    提出“特殊玻璃结构”：以小尺寸原子胞复现大型金属玻璃模型的局部几何统计。该方法已在 MAST 中实现，使非晶材料力学与电子性质的高精度第一性原理计算更具可行性。

    文章 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.

    结合团簇展开、蒙特卡洛采样、CALPHAD 与针对性实验，系统刻画四元 Cr–Mo–Nb–V 中 BCC 固溶体的稳定区间。所得高维相稳定图揭示了耐火高熵合金设计所需的成分与温度窗口。

    文章

  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.

    把高通量基态预测与有限温度 CALPHAD 建模连接起来，检验 Ni–Re 体系中两个预测金属间化合物。结果确认它们在实际合成温度下的稳定性，并解释了早期热力学评估为何可能遗漏这些相。

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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.

    提出可高效描述微米尺度过渡金属硫族化合物薄片的矩阵方法。以 MoS2 为例，建立边缘能、硫化学势与实验中三角形生长形貌之间的联系。

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稿件

审稿中

  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.

    在相图热力学中显式引入共格弹性相容条件。对于 Nb–V，共格应变显著缩小互溶间隙、降低临界温度，并使分解后两相成分同时依赖温度与合金总成分。

    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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其他论文

合作成果

  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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