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Researcher profile

Tong-Yi Zhang

Tong-Yi Zhang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciArtificial IntelligenceMachine Learningphysics.app-ph

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Published work

5 published item(s)

preprint2026arXiv

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Efficient exploration of vast compositional and processing spaces is essential for accelerated materials discovery. Bayesian optimization (BO) provides a principled strategy for identifying optimal materials with minimal experiments, yet its adoption in materials science is hindered by implementation complexity and limited domain-specific tools. Here, we present Bgolearn, a comprehensive Python framework that makes BO accessible and practical for materials research through an intuitive interface, robust algorithms, and materials-oriented workflows. Bgolearn supports both single-objective and multi-objective Bayesian optimization with multiple acquisition functions (e.g., expected improvement, upper confidence bound, probability of improvement, and expected hypervolume improvement etc.), diverse surrogate models (including Gaussian processes, random forests, and gradient boosting etc.), and bootstrap-based uncertainty quantification. Benchmark studies show that Bgolearn reduces the number of required experiments by 40-60% compared with random search, grid search, and genetic algorithms, while maintaining comparable or superior solution quality. Its effectiveness is demonstrated not only through the studies presented in this paper, such as the identification of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, but also by numerous publications that have proven its impact in material discovery. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical user interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical and reliable platform for Bayesian optimization in materials science, and is openly available at https://github.com/Bin-Cao/Bgolearn.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction

Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixtures whose constituent phases (components) cannot be reliably disentangled. While recent advances in representation-based crystal retrieval and generation suggest the possibility of inferring structures directly from PXRD, existing approaches largely assume single-phase inputs and break down in multiphase settings. Here, we present XDecomposer, a prior-free framework for joint decomposition and identification of multiphase XRD patterns without requiring candidate phase lists, structural templates, or prior knowledge of phase number. We formulate multiphase diffraction analysis as a set prediction problem, where the model infers an unordered set of phase-resolved components, their mixture proportions, and corresponding structural representations within a unified architecture. A phase-query-driven decomposition mechanism, together with diffraction-consistent physical reconstruction, enables accurate source separation while preserving crystallographic fidelity. Extensive experiments on both simulated and experimental datasets show that XDecomposer substantially improves reconstruction accuracy and phase identification across diverse chemical systems, while maintaining strong generalization to unseen mixtures. These results provide a practical route toward data-driven, source-resolved multiphase XRD analysis and reduce long-standing dependence on prior-guided iteratively phase matching. The code is openly available at https://github.com/Licht0812/XDecomposer

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Phase field simulation of grain size effects in nanograined Ti-Nb shape memory alloys

Titanium-based shape memory alloys, such as Ti2448, have attracted enormous attention owing to their unique thermomechanical properties and potential biomedical applications. In this study, we develop a polycrystalline phase field to investigate the grain size dependence of the martensitic transformation and associated mechanical properties of nanograined Ti-Nb alloys. It is shown that a reduction of the average grain size strengthens the suppression of the martensitic transformation (MT), leading to an increase of the transformation stress, shrinkage of the stress hysteresis, and elimination of residual strain. The time-temperature-transformation curves of nano-grained Ti-Nb alloys with different average grain sizes are obtained and the validity of Hall-Petch relation is also confirmed in all studied grain sizes. Furthermore, when the average grain size becomes ultrasmall, both the temperature- and stress-induced MTs show the continuous second-order phase transition behavior. These superior transformation characteristics are attributed to the high density of grain boundaries and the related dominant role of the gradient energy at the nanoscale. Our results have profound implications for the design and control of the properties in nano-grained shape memory alloys.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Machine Learning of Mechanical Properties of Steels

The mechanical properties are essential for structural materials. The analyzed 360 data on four mechanical properties of steels, viz. fatigue strength, tensile strength, fracture strength, and hardness, are selected from the NIMS database, including carbon steels, and low-alloy steels. Five machine learning algorithms were applied on the 360 data to predict the mechanical properties and random forest regression illustrates the best performance. The feature selection was conducted by random forest and symbolic regressions, leading to the four most important features of tempering temperature, and alloying elements of carbon, chromium, and molybdenum to the mechanical properties of steels. Besides, mathematic expressions were generated via symbolic regression, and the expressions explicitly predict how each of the four mechanical properties varies quantitatively with the four most important features. The present work demonstrates the great potential of symbolic regression in the discovery of novel advanced materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-dimensional metallic ferroelectricity in PbTe monolayer by electrostatic doping

Polar metals characterized by the simultaneous coexistence of ferroelectric distortions and metallicity have attracted tremendous attention. Developing such materials at low dimensions remains challenging since both conducting electrons and reduced dimensions are supposed to quench ferroelectricity. Here, based on first-principles calculations, we report the discovery of ferroelectric behavior in two-dimensional (2D) metallic materials with electrostatic doping, even though ferroelectricity is unconventional at the atomic scale. We reveal that PbTe monolayer is intrinsic ferroelectrics with pronounced out-of-plane electric polarization originated from its non-centrosymmetric buckled structure. The ferroelectric distortions can be preserved with carriers doping in the ferroelectric monolayer, which thus enables the doped PbTe monolayer to act as a 2D polar metal. With an effective Hamiltonian extracted from the parametrized energy space, we found that the elastic-polar mode interaction is of great importance for the existence of robust polar instability in the doped system. The application of this doping strategy is not specific to the present crystal, but is rather general to other 2D ferroelectrics to bring about the fascinating metallic ferroelectric properties. Our findings thus change conventional acknowledge in 2D materials and will facilitate the development of multifunctional material in low dimensions.

0 citations0 reviewsNo code linkedOpen access