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Haoren Wang

Publications and source records attributed to Haoren Wang.

13 recordsLinked to original sources
arXiv2026

Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features

Many material systems exhibit complex spatial and temporal interactions across multiple length scales and modalities that govern macroscopic behavior. Although Machine Learning (ML) is widely used in materials science to predict this behavior, most approaches still rely on handcrafted descriptors or aggregated representations that overlook spatial organization, limiting insight into governing mechanisms. We introduce Materials Spatial Intelligence (MSI), a framework inspired by spatial intelligence that learns directly from multimodal spatial observations of material systems. MSI encodes high-resolution microstructural and deformation data into shared latent representations that preserve spatial relationships while supporting property prediction, interpretation, and optimization. By combining multimodal representation learning, MSI identifies the key features governing mechanical behavior and property trade-offs in structural alloys. Beyond prediction, MSI enables feature-driven microstructure optimization and mechanism discovery. More broadly, MSI establishes a foundation for applying spatial intelligence to materials science, leveraging interpretable ML systems to accelerate scientific discovery and materiel design

cond-mat.mtrl-sci↗
arXiv2025

Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction

Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in metals, latent space features describing plasticity of a small region are spatially mapped across a large field of view while maintaining the same spatial relationships as the experimental measurements. Latent space feature maps capture the complexity and heterogeneity of metal plasticity as a low-dimensional representation. These feature maps are then used to train a convolutional neural network-based model to predict monotonic and cyclic macroscopic properties. The approach is demonstrated on a large set of face-centered cubic metals, enabling rapid and accurate property prediction. The effects of hyperparameters and training strategies are analyzed, and the extension of the proposed approach to a broader range of metallic materials and loading conditions is discussed.

cond-mat.mtrl-sci↗
arXiv2025

Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features

To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together these steps offer a method offers a novel means to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.

cond-mat.mtrl-sci↗
arXiv2024

The Role of Allotropy on Phase Formation in High Entropy Alloys

Identifying single phase, high-entropy systems has been a prominent research focus of materials engineering over the past decade. The considerable effort in computational modeling and experimental verification has yielded several methods and descriptors for predicting if a single phase will form; however, the details surrounding the resulting crystal structure have largely remained a mystery. Here, we present a compelling argument for the role of allotropy in determining the crystal structure of a single-phase, high-entropy alloy. High entropy alloys can contain 5 or more elements and must achieve a configurational entropy greater than 1.5R. This study shows that when these high entropy material conditions are met, the majority crystal structure of the non-allotrope forming element plays a dominant role in crystal structure determination. The theory is demonstrated via several approaches, including analysis of 434 unique known single-phase compositions from the literature, thermodynamic modeling of more than 1,400 compositions, and experimental synthesis of nine specific alloys that test this hypothesis. The results demonstrate allotropy can identify a subset of compositions unlikely to form a single phase and predict the crystal structure with a high degree of accuracy for a wide range of simple (e.g., 5 equiatomic elements) and more complex (e.g., Al0.3B0.6CoCrFeNiCu0.7Si0.1) high entropy alloys. Allotropy provides new insight into the underlying physics governing the resultant crystal structure in materials without a principle element. As high entropy materials continue to be an area of focus for developing materials with unique properties, this study is expected to serve as a significant tool in the screening of materials for specific crystal structures.

cond-mat.mtrl-sci↗
arXiv2024

Illustrating an Effective Workflow for Accelerated Materials Discovery

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated Materials Discovery Framework as a part of the High-Throughput Materials Discovery for Extreme Conditions Initiative. Our BIRDSHOT Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms.

cond-mat.mtrl-sci↗
arXiv2021

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

cs.HC↗
arXiv2021

Bulk High-Entropy Hexaborides

For the first time, a group of CaB6-typed cubic rare earth high-entropy hexaborides have been successfully fabricated into dense bulk pellets (>98.5% in relative densities). The specimens are prepared from elemental precursors via in-situ metal-boron reactive spark plasma sintering. The sintered bulk pellets are determined to be single-phase without any detectable oxides or other secondary phases. The homogenous elemental distributions have been confirmed at both microscale and nanoscale. The Vickers microhardness are measured to be 16-18 GPa at a standard indentation load of 9.8 N. The nanoindentation hardness and Young's moduli have been measured to be 19-22 GPa and 190-250 GPa, respectively, by nanoindentation test using a maximum load of 500 mN. The material work functions are determined to be 3.7-4.0 eV by ultraviolet photoelectron spectroscopy characterizations, which are significantly higher than that of LaB6.

cond-mat.mtrl-sci↗
arXiv2020

High-Entropy Rare Earth Tetraborides

Six high-entropy rare earth tetraborides of the tetragonal UB4-prototyped structure have been successfully synthesized for the first time. The specimens are prepared from elemental precursors via high-energy ball mill and in-situ reactive spark plasma sintering. The sintered specimens are >98% in relative densities without detectable oxide impurities (albeit the presence of minor hexaborides in some compositions). No detectable secondary phase is observed in the composition (Y$_{0.2}$Nd$_{0.2}$Sm$_{0.2}$Gd$_{0.2}$Tb$_{0.2}$)B$_{4}$, which is proven homogeneous at both microscale and nanoscale. The Vickers microhardness are determined to be ~13-15 GPa at a standard indentation load of 9.8 N. A scientifically interesting observation is represented by the anisotropic lattice distortion from the rule-of-mixture averages. This work expands the family of high-entropy ceramics via fabricating a new class of high-entropy borides with a unique tetragonal quasi-layered crystal structure.

cond-mat.mtrl-sci↗
arXiv2020

High-Entropy Monoborides: Towards Superhard Materials

Single-phase high-entropy monoborides (HEMBs) of the CrB prototype structure have been synthesized for the first time. Reactive spark plasma sintering of ball milled mixtures of elemental precursor powders produced bulk (V0.2Cr0.2Nb0.2Mo0.2Ta0.2)B, (V0.2Cr0.2Nb0.2Mo0.2W0.2)B, and (V0.2Cr0.2Nb0.2Ta0.2W0.2)B HEMB specimens of ~98.3-99.5% relative densities. Vickers hardness was measured to be ~22-26 GPa at an indentation load of 9.8 N and ~32-37 GPa at 0.98 N. In particular, the load-dependent hardness of (V0.2Cr0.2Nb0.2Ta0.2W0.2)B is higher than those of ternary (Ta0.5W0.5)B (already considered as superhard) and hardest reported high-entropy metal diborides, and on a par with the classical superhard boride WB4.

cond-mat.mtrl-sci↗
arXiv2020

Dual-Phase High-Entropy Ultra-High Temperature Ceramics

A series of dual-phase high-entropy ultrahigh temperature ceramics (DPHE-UHTCs) are fabricated starting from N binary borides and (5-N) binary carbides powders. >~99% relative densities have been achieved with virtually no native oxides. These DPHE-UHTCs consist of a hexagonal high-entropy boride (HEB) phase and a cubic high-entropy carbide (HEC) phase. A thermodynamic relation that governs the compositions of the HEB and HEC phases in equilibrium is discovered and a thermodynamic model is proposed. These DPHE-UHTCs exhibit tunable grain size, Vickers microhardness, Young' and shear moduli, and thermal conductivity. The DPHE-UHTCs have higher hardness than the weighted linear average of the two single-phase HEB and HEC, which are already harder than the rule-of-mixture averages of individual binary borides and carbides. This study extends the state of the art by introducing dual-phase high-entropy ceramics (DPHECs), which provide a new platform to tailor various properties via changing the phase fraction and microstructure.

cond-mat.mtrl-sci↗
arXiv2020

Dissolving and Stabilizing Soft WB2 and MoB2 Phases into High-Entropy Borides via Boron-Metals Reactive Sintering to Attain Higher Hardness

Five single-phase WB2- and MoB2-containing high-entropy borides (HEBs) have been made via reactive spark plasma sintering of elemental boron and metals. A large reactive driving force enables the full dissolution of 10-20 mol. % WB2 to form dense, single-phase HEBs, including (Ti0.2Zr0.2Hf0.2Mo0.2W0.2)B2, (Ti0.2Ta0.2Cr0.2Mo0.2W0.2)B2, (Zr0.2Hf0.2Nb0.2Ta0.2W0.2)B2, and (Zr0.225Hf0.225Ta0.225Mo0.225W0.1)B2; the successful fabrication of such single-phase WB2-containing HEBs has not been reported before. In the processing science, this result serves perhaps the best example demonstrating that the phase formation in high-entropy ceramics can strongly depend on the kinetic route. A scientifically interesting finding is that HEBs containing softer WB2 and/or MoB2 components are significantly harder than (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 (with harder binary boride components). This exemplifies that high-entropy ceramics can achieve unexpected properties.

cond-mat.mtrl-sci↗
arXiv2019

Computer Vision Approach to Study Deformation of Materials

Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract deformation information of materials has been explored, which greatly expands the use of computer vision approaches to studying mechanics of materials and potentially opens new dialogues between the two communities. The computer vision algorithm is first developed and tested on computationally deformed images, before evaluating experimentally collected images on speckle painted samples before and after deformation. Moreover, a virtual experiment has also shown the feasibility of mapping surface strain of a sample based on its natural pattern with significantly improved accuracy compared to DIC result, which provides new opportunities in experimentation and computer algorithms to study deformation mechanics of materials. Validation experiments include evaluating the performance of strain mapping using the computer vision approach in the uniaxial tensile test and three-point bending test, compared with extensometer reading and digital image correlation respectively.

physics.comp-ph↗