arXiv subjects

Siya Zhu

Publications and source records attributed to Siya Zhu.

11 recordsLinked to original sources

arXiv2026

From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions

Identifying regions of design space subject to spinodal decomposition is a critical component of alloy design in high-dimensional composition spaces. In cases where designers are seeking to exploit spinodal microstructures to tailor alloy properties, prediction of microstructure evolution and morphology is also needed. In this work, we present a Machine Learning Interatomic Potential (MLIP)-trained, CALPHAD-based, open-source workflow for high-throughput microstructure stability analysis and visualization. In this workflow, coherent strain contributions are captured via high-throughput MLIP elastic constant calculations. To predict microstructure morphology for compositions of interest, MLIP-generated thermodynamic models are fed into an elasto-chemical phase field simulation. Both stability analyses and phase-field simulations utilize analytically-derived Gibbs energy Hessians to improve computational efficiency and accuracy over finite difference approximations. We demonstrate this workflow by investigating microstructure stability in the Hf-Nb-Ti-V quaternary system.

cond-mat.mtrl-sci↗

arXiv2026

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

Coherent elastic strain is an important but often neglected contribution to phase-separation thermodynamics in alloy systems where decomposed phases have appreciable lattice mismatch. We develop a thermodynamic framework that incorporates coherent elastic compatibility directly into phase-diagram calculations alongside conventional CALPHAD chemical free energies. Applied to the BCC Nb-V system, the framework shows that coherent elasticity substantially suppresses phase separation, narrows the miscibility gap, and lowers the critical temperature toward experimentally observed values. Beyond these quantitative effects, the coherent constraint qualitatively alters the interpretation of phase equilibria: the equilibrium decomposition compositions become functions of both temperature and overall alloy composition, so the two-phase boundary no longer represents unique coexistence compositions. These results establish coherent elasticity as a key thermodynamic factor in lattice-mismatched systems and provide a general framework for coherent phase-diagram modeling.

cond-mat.mtrl-sci↗

arXiv2026

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling

Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational space and the limitations of existing methods. Here we introduce PAIPAI (Package for Alloy Interstitial Predictions using Artificial Intelligence), a Monte Carlo framework coupled with machine-learning interatomic potentials (MLIPs) that searches for ground-state atomic configurations in HEAs with defects and interstitials. PAIPAI employs a dual-worker architecture-fast workers for rapid configurational screening and slow workers for high-accuracy refinement-coordinated through a shared waiting pool, enabling efficient parallel sampling. We demonstrate PAIPAI through three case studies: (i) surface segregation in a Ti-V-Cr-Re slab; (ii) interstitial oxygen and boron aggregation in bulk BCC Nb-Ti-Ta-Hf; and (iii) coupled metallic and interstitial segregation at grain boundaries in Nb-Ti-Ta-Hf. In all cases, Monte Carlo-optimized structures are significantly lower in energy than any configuration obtained by random sampling, and MLIP energy rankings are validated against density functional theory calculations. PAIPAI provides a general and efficient framework for predicting atomic ordering, segregation, and interstitial behavior in complex, defective HEA systems.

cond-mat.mtrl-sci↗

arXiv2025

Predicting interstitial elements in Refractory Complex Concentrated Alloys

Refractory complex concentrated alloys, composed of multiple principal refractory elements, are promising candidates for high-temperature structural applications due to their exceptional thermal stability and high melting points. However, their mechanical performance is often compromised by interstitial impurities, particularly oxygen, nitrogen, and carbon, which segregate to grain boundaries and promote embrittlement. In this study, we investigate the solubility and thermodynamic behavior of oxygen interstitials in a model NbTiHfTa RCCA system. We synthesized NbTiHfTa alloys with varying oxygen contents via plasma arc melting and characterized their phase evolution and microstructure using XRD, SEM, and TEM. Complementary computational modeling was performed using machine-learning interatomic potentials integrated with Monte Carlo simulations to probe oxygen interactions at the atomic scale. Our results reveal a solubility limit for oxygen between 0.8 and 1.0 atomic percentage, beyond which HfO2 formation is energetically favorable. This combined experimental-computational framework provides a predictive approach for managing interstitial behavior in RCCAs, enabling improved alloy design strategies for enhanced mechanical performance.

cond-mat.mtrl-sci↗

arXiv2025

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System

This work introduces PhaseForgePlus -- a computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting. Using the Pt-W system as an example, we show that the integration of Machine Learning Potentials into the Alloy Theoretic Automated Toolkit can produce physically grounded Gibbs energy descriptions requiring only slight adjustments to produce accurate phase diagrams. Employing the Jansson derivative method in the context of experimental observations, such adjustments can be efficiently and robustly determined through gradient-informed optimization procedures.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy

High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase stability play a crucial role, making phase diagram calculations significantly important. However, phase diagram calculations with conventional CALPHAD assessments based on experimental or ab-initio data can be expensive. With the emergence of machine-learning interatomic potentials (MLIPs), we have developed a program named PhaseForge, which integrates MLIPs into the Alloy Theoretic Automated Toolkit (ATAT) framework using our MLIP calculation library, MaterialsFramework, to enable efficient exploration of alloy phase diagrams. Moreover, our workflow can also serve as a benchmarking tool for evaluating the quality of different MLIPs.

cond-mat.mtrl-sci↗

arXiv2025

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures

Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet.

cs.LG↗

arXiv2025

Computational Study of Density Fluctuation-Induced Shear Bands Formation in Bulk Metallic Glasses

Seemingly identical Bulk Metallic Glasses (BMG) often exhibit strikingly different mechanical properties despite having the same composition and fictive temperature. A postulated mechanism underlying these differences is the presence of "defects". Here we investigate this hypothesis through the study of the effect of density fluctuations on shear band formation under an applied stress. We find that the critical shear stress is strongly dependent on the magnitude and size of the fluctuations. This finding also elucidates why, historically, critical shear stresses obtained in simulations have differed so much from those found experimentally, as typical simulations setups might favor unrealistically uniform geometries.

cond-mat.dis-nn↗

arXiv2025

Soliquidy: a descriptor for atomic geometrical confusion

Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin-films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges

Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-intensive and limited by experimentally assessed data. This work explores the use of machine learning interatomic potentials (MLIPs) such as M3GNet, CHGNet, MACE, SevenNet, and ORB to significantly accelerate phase diagram calculations by using the Alloy Theoretic Automated Toolkit (ATAT) to map calculations of the energies and free energies of atomistic systems to CALPHAD-compatible thermodynamic descriptions. Using case studies including Cr-Mo, Cu-Au, and Pt-W, we demonstrate that MLIPs, particularly ORB, achieve computational speedups exceeding three orders of magnitude compared to DFT while maintaining phase stability predictions within acceptable accuracy. Extending this approach to liquid phases and ternary systems like Cr-Mo-V highlights its versatility for high-entropy alloys and complex chemical spaces. This work demonstrates that MLIPs, integrated with tools like ATAT within a CALPHAD framework, provide an efficient and accurate framework for high-throughput thermodynamic modeling, enabling rapid exploration of novel alloy systems. While many challenges remain to be addressed, the accuracy of some of these MLIPs (ORB in particular) are on the verge of paving the way toward high-throughput generation of CALPHAD thermodynamic descriptions of multi-component, multi-phase alloy systems.

cond-mat.mtrl-sci↗

arXiv2023

Special Glass Structures for First Principles Studies of Bulk Metallic Glasses

The atomic-level structure of bulk metallic glasses is a key determinant of their properties. An accurate representation of amorphous systems in computational studies has traditionally required large supercells that are unfortunately computationally demanding to handle using the most accurate ab initio calculations. To address this, we propose to specifically design small-cell structures that best reproduce the local geometric descriptors (e.g., pairwise distances or bond angle distributions) of a large-cell simulation. We rely on molecular dynamics (MD) driven by empirical potentials to generate the target descriptors, while we use reverse Monte Carlo (RMC) methods to optimize the small-cell structure. The latter can then be used to determine mechanical and electronic properties using more accurate electronic structure calculations. The method is implemented in the Metallic Amorphous Structures Toolkit (MAST) software package.

cond-mat.mtrl-sci↗