arXiv subjects

Shenghong Ju

Publications and source records attributed to Shenghong Ju.

At least 19 recordsLinked to original sources

arXiv2026

Discovering Physically Interpretable Mathematical Expression for Predicting CO2 Adsorption in Metal-Organic Frameworks via Machine Learning-Symbolic Regression

This work presents a machine learning-symbolic regression (ML-SR) strategy to develop a physically interpretable formula for predicting low pressure CO2 adsorption capacity in hypothetical metal-organic frameworks (hMOFs). Four ML models were trained on a small dataset of 1,000 samples, and five key descriptors-largest cavity diameter, pore limiting diameter, void fraction, gravimetric surface area, and number of hydrogen atoms-were identified through SHAP and feature importance analyses. Symbolic regression was then employed to derive a concise adsorption formula, Q=aA, where a represents an adsorption baseline (mmol/g) and A is a dimensionless adsorption number incorporating four structural descriptors. We interpret A as the ratio between an adsorption binding force and a diffusion driving force, revealing how pore topology and surface chemistry jointly influence adsorption. Validation against a comprehensive dataset of 137,652 hMOFs demonstrates that this formula achieves over 70% prediction accuracy for 62,448 structures, confirming strong applicability within defined structural and operational ranges. Unlike conventional black box ML models, the proposed physics-guided expression enables efficient prediction and provides clearer insight into adsorption mechanisms.

cond-mat.mtrl-sci↗

arXiv2025

CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT

Contrast-enhanced computed tomography (CECT) is the primary imaging technique that provides valuable spatial-temporal information about lesions, enabling the accurate diagnosis and subclassification of pancreatic tumors. However, the high heterogeneity and variability of pancreatic tumors still pose substantial challenges for precise subtyping diagnosis. Previous methods fail to effectively explore the contextual information across multiple CECT phases commonly used in radiologists' diagnostic workflows, thereby limiting their performance. In this paper, we introduce, for the first time, an automatic way to combine the multi-phase CECT data to discriminate between pancreatic tumor subtypes, among which the key is using Mamba with promising learnability and simplicity to encourage both temporal and spatial modeling from multi-phase CECT. Specifically, we propose a dual hierarchical contrast-enhanced-aware Mamba module incorporating two novel spatial and temporal sampling sequences to explore intra and inter-phase contrast variations of lesions. A similarity-guided refinement module is also imposed into the temporal scanning modeling to emphasize the learning on local tumor regions with more obvious temporal variations. Moreover, we design the space complementary integrator and multi-granularity fusion module to encode and aggregate the semantics across different scales, achieving more efficient learning for subtyping pancreatic tumors. The experimental results on an in-house dataset of 270 clinical cases achieve an accuracy of 97.4% and an AUC of 98.6% in distinguishing between pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (PNETs), demonstrating its potential as a more accurate and efficient tool.

cs.CV↗

arXiv2025

Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation

Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study, we establish a standardized dataset and evaluation framework for medical imaging QC, systematically assessing large language models (LLMs) in image quality assessment and report standardization. Specifically, we first constructed and anonymized a dataset of 161 chest X-ray (CXR) radiographs and 219 CT reports for evaluation. Then, multiple LLMs, including Gemini 2.0-Flash, GPT-4o, and DeepSeek-R1, were evaluated based on recall, precision, and F1 score to detect technical errors and inconsistencies. Experimental results show that Gemini 2.0-Flash achieved a Macro F1 score of 90 in CXR tasks, demonstrating strong generalization but limited fine-grained performance. DeepSeek-R1 excelled in CT report auditing with a 62.23\% recall rate, outperforming other models. However, its distilled variants performed poorly, while InternLM2.5-7B-chat exhibited the highest additional discovery rate, indicating broader but less precise error detection. These findings highlight the potential of LLMs in medical imaging QC, with DeepSeek-R1 and Gemini 2.0-Flash demonstrating superior performance.

cs.CL↗

arXiv2024

High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data

In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power factors. Aiming at the problem of weak generalization ability of small data with power factors at high temperatures, we employ symbolic regression for feature creation which enhances the robustness of the model while preserving the physical meaning of features. 33 candidate metal oxides are finally targeted for high-temperature thermoelectric applications from a pool of 48,694 compounds in the Materials Project database. The Boltzmann transport theory is utilized to perform electrical transport properties calculations at 1,000 K. The relaxation time is approximated by employing constant electron-phonon coupling based on the deformation potential theory. Considering band degeneracy, the electron group velocity is obtained using the momentum matrix element method, yielding 28 materials with power factors greater than 50 $μW cm^{-1} K^{-2} $. The high-throughput framework we proposed is instrumental in the selection of metal oxides for high-temperature thermoelectric applications. Furthermore, our data-driven analysis and transport calculation suggest that metal oxides rich in elements such as cerium (Ce), tin (Sn), and lead (Pb) tend to exhibit high power factors at high temperatures.

cond-mat.mtrl-sci↗

arXiv2024

Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity

Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer datasets, property calculators, feature engineering and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic.

cond-mat.soft↗

arXiv2024

AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic polymers is urgent because of their importance in the thermal management of many industrial applications such as microelectronic devices and integrated circuits. In this work, we have proposed a robust AI-assisted workflow for the inverse design of high TC polymers. By using 1144 polymers with known computational TCs, we construct a surrogate deep neural network model for TC prediction and extract a polymer-unit library with 32 sequences. Two state-of-the-art multi-objective optimization algorithms of unified non-dominated sorting genetic algorithm III (U-NSGA-III) and q-noisy expected hypervolume improvement (qNEHVI) are employed for sequence-ordered polymer design with both high TC and synthetic possibility. For triblock polymer design, the result indicates that qNHEVI is capable of exploring a diversity of optimal polymers at the Pareto front, but the uncertainty in Quasi-Monte Carlo sampling makes the trials costly. The performance of U-NSGA-III is affected by the initial random structures and usually falls into a locally optimal solution, but it takes fewer attempts with lower costs. 20 parallel U-NSGA-III runs are conducted to design the pentablock polymers with high TC, and half of the candidates among 1921 generated polymers achieve the targets (TC > 0.4 W/(mK) and SA < 3.0). Ultimately, we check the TC of 50 promising polymers through molecular dynamics simulations and reveal the intrinsic connections between microstructures and TCs. Our developed AI-assisted inverse design approach for polymers is flexible and universal, and can be extended to the design of polymers with other target properties.

cond-mat.soft↗

arXiv2024

Tunable thermal conductivity of sustainable geopolymers by Si/Al ratio and moisture content: insights from atomistic simulations

In this work, the effects of Si/Al ratio and moisture content on thermal transport in sustainable geopolymers has been comprehensively investigated by using the molecular dynamics simulation. The thermal conductivity of geopolymer systems increases with the increase of Si/Al ratio, and the phonon vibration frequency region which plays a major role in the main increase of its thermal conductivity is 8-25 THz, while the rest of the frequency interval contribute less. With the increase of moisture content, the thermal conductivity of geopolymer systems decreases at first, then increases and finally tends to be stable, which is contrary to the changing trend of porosity of the system. This is mainly because the existence of pores will lead to phonon scattering during thermal transport, which in turn affects the thermal conductivity of the system. When the moisture content is 5%, the thermal conductivity reaches a minimum value of about 1.103 W/(mK), which is 40.2% lower than the thermal conductivity of the system without water molecule. This work will help to enhance the physical level understanding of the relationship between the geopolymer structures and thermal transport properties.

cond-mat.soft↗

arXiv2023

When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation (MPLiTS), in terms of prompts, data resolution, phases. Experimental results demonstrate that there might be a large gap between SAM and expected performance. Fortunately, the qualitative results show that SAM is a powerful annotation tool for the community of interactive medical image segmentation.

eess.IV↗

arXiv2023

Data-driven design of multilayer hyperbolic metamaterials for near-field thermal radiative modulator with high modulation contrast

The thermal modulator based on the near-field radiative heat transfer has wide applications in thermoelectric diodes, thermoelectric transistors, and thermal storage. However, the design of optimal near-field thermal radiation structure is a complex and challenging problem due to the tremendous number of degrees of freedom. In this work, we have proposed a data-driven machine learning workflow to efficiently design multilayer hyperbolic metamaterials composed of $α$-MoO$_{\rm 3}$ for near-field thermal radiative modulator with high modulation contrast. By combining the multilayer perceptron and Bayesian optimization, the rotation angle, layer thickness and gap distance of the multilayer metamaterials are optimized to achieve a maximum thermal modulation contrast ratio of 6.29. This represents a 97% improvement compared to previous single layer structure. The large thermal modulation contrast is mainly attributed to the alignment and misalignment of hyperbolic plasmon polaritons and hyperbolic surface phonon polaritons of each layer controlled by the rotation. The results provide a promising way for accelerating the designing and manipulating of near-field radiative heat transfer by anisotropic hyperbolic materials through the data-driven style.

physics.optics↗

arXiv2023

Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically. However, the performances of existing multi-phase liver tumor segmentation (MPLiTS)-based methods suffer from redundancy and weak interpretability, % of the fused result, resulting in the implicit unreliability of clinical applications. In this paper, we propose a novel trustworthy multi-phase liver tumor segmentation (TMPLiTS), which is a unified framework jointly conducting segmentation and uncertainty estimation. The trustworthy results could assist the clinicians to make a reliable diagnosis. Specifically, Dempster-Shafer Evidence Theory (DST) is introduced to parameterize the segmentation and uncertainty as evidence following Dirichlet distribution. The reliability of segmentation results among multi-phase CECT images is quantified explicitly. Meanwhile, a multi-expert mixture scheme (MEMS) is proposed to fuse the multi-phase evidences, which can guarantee the effect of fusion procedure based on theoretical analysis. Experimental results demonstrate the superiority of TMPLiTS compared with the state-of-the-art methods. Meanwhile, the robustness of TMPLiTS is verified, where the reliable performance can be guaranteed against the perturbations.

eess.IV↗

arXiv2023

Large modulation of thermal transport in 2D semimetal triphosphides by doping-induced electron-phonon coupling

Recent studies demonstrate that novel 2D triphosphides semiconductors possess high carrier mobility and promising thermoelectric performance, while the carrier transport behaviors in 2D semimetal triphosphides have never been elucidated before. Herein, using the first-principles calculations and Boltzmann transport theory, we reveal that the electron-phonon coupling can be significant and thus greatly inhibits the electron and phonon transport in electron-doped BP3 and CP3. The intrinsic heat transport capacity of flexural acoustic phonon modes in the wrinkle structure is largely suppressed arising from the strong out-of-plane phonon scatterings, leading to the low phonon thermal conductivity of 1.36 and 5.33 W/(mK) for BP3 and CP3 at room temperature, and at high doping level, the enhanced scattering from electron diminishes the phonon thermal conductivity by 71% and 54% for BP3 and CP3, respectively. Instead, electron thermal conductivity shows nonmonotonic variations with the increase of doping concentration, stemming from the competition between electron-phonon scattering rates and electron group velocity. It is worth noting that the heavy-doping effect induced strong scattering from phonon largely suppresses the electron transport and reduces electron thermal conductivity to the magnitude of phonon thermal conductivity. This work sheds light on the electron and phonon transport properties in semimetal triphosphides monolayer and provides an efficient avenue for the modulation of carrier transport by doping-induced electron-phonon coupling effect.

cond-mat.mtrl-sci↗

arXiv2023

Microscopic mechanism of tunable thermal conductivity in carbon nanotube-geopolymer nanocomposites

Geopolymer has been considered as a green and low-carbon material with great potential application due to its simple synthesis process, environmental protection, excellent mechanical properties, good chemical resistance and durability. In this work, the molecular dynamics simulation is employed to investigate the effect of the size, content and distribution of carbon nanotubes on the thermal conductivity of geopolymer nanocomposites, and the microscopic mechanism is analyzed by the phonon density of states, phonon participation ratio and spectral thermal conductivity, etc. The results show that there is a significant size effect in geopolymer nanocomposites system due to the carbon nanotubes. In addition, when the content of carbon nanotubes is 16.5%, the thermal conductivity in carbon nanotubes vertical axial direction (4.85 W/(mk)) increases 125.6% compared with the system without carbon nanotubes (2.15 W/(mk)). However, the thermal conductivity in carbon nanotubes vertical axial direction (1.25 W/(mk)) decreases 41.9%, which is mainly due to the interfacial thermal resistance and phonon scattering at the interfaces. The above results provide theoretical guidance for the tunable thermal conductivity in carbon nanotube-geopolymer nanocomposites.

cond-mat.soft↗

arXiv2023

Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors

The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional thermal conductivity polymers remains a trial and error process due to the multi-degrees of freedom during the synthesis and characterization process. In this work, we have proposed a high-throughput screening framework for polymer chains with high thermal conductivity via interpretable machine learning and physical-feature engineering. The polymer thermal conductivity datasets for training were first collected by molecular dynamics simulation. Inspired by the drug-like small molecule representation and molecular force field, 320 polymer monomer descriptors were calculated and the 20 optimized descriptors with physical meaning were extracted by hierarchical down-selection. All the machine learning models achieve a prediction accuracy R2 greater than 0.80, which is superior to that of represented by traditional graph descriptors. Further, the cross-sectional area and dihedral stiffness descriptors were identified for positive/negative contribution to thermal conductivity, and 107 promising polymer structures with thermal conductivity greater than 20.00 W/mK were obtained. Mathematical formulas for predicting the polymer thermal conductivity were also constructed by using symbolic regression. The high thermal conductivity polymer structures are mostly π-conjugated, whose overlapping p-orbitals enable easily to maintain strong chain stiffness and large group velocities. The proposed data-driven framework should facilitate the theoretical and experimental design of polymers with desirable properties.

cond-mat.mtrl-sci↗

arXiv2022

Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Structural manipulation at the nanoscale breaks the intrinsic correlations among different energy carrier transport properties, achieving high thermoelectric performance. However, the coupled multifunctional (phonon and electron) transport in the design of nanomaterials makes the optimization of thermoelectric properties challenging. Machine learning brings convenience to the design of nanostructures with large degree of freedom. Herein, we conducted comprehensive thermoelectric optimization of isotopic armchair graphene nanoribbons (AGNRs) with antidots and interfaces by combining Green's function approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by manipulating antidots was obtained at the interfaces of the aperiodic isotope superlattices, which is 5.69 times larger than that of the pristine structure. The proposed optimal structure via machine learning provides physical insights that the carbon-13 atoms tend to form a continuous interface barrier perpendicular to the carrier transport direction to suppress the propagation of phonons through isotope AGNRs. The antidot effect is more effective than isotope substitution in improving the thermoelectric properties of AGNRs. The proposed approach coupling energy carrier transport property analysis with machine learning algorithms offers highly efficient guidance on enhancing the thermoelectric properties of low-dimensional nanomaterials, as well as to explore and gain non-intuitive physical insights.

cond-mat.mes-hall↗

arXiv2022

Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization

Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective. In this letter, we have developed a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal emitters at different target wavelengths. With only several hundreds of training data sets, new structures with optimal properties can be quickly figured out in a compressed 2-dimensional latent space. This enables the optimal design by calculating far less than 0.001\% of the total candidate structures, which greatly decreases the design period and cost. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.

cs.LG↗

arXiv2022

High thermoelectric performance in metastable phase of silicon: a first-principles study

In this work, both thermal and electrical transport properties of diamond$-$cubic Si (Si$-$I) and metastable R8 phase of Si (Si$-$XII) are comparatively studied by using first$-$principles calculations combined with Boltzmann transport theory. The metastable Si$-$XII shows one magnitude lower lattice thermal conductivity than stable Si$-$I from 300 to 500~K, attributed from the stronger phonon scattering in three$-$phonon scattering processes of Si$-$XII. For the electronic transport properties, although Si$-$XII with smaller band gap (0.22 eV) shows lower Seebeck coefficient, the electrical conductivities of anisotropic $n$$-$type Si$-$XII show considerable values along $x$ axis due to the small effective masses of electron along this direction. The peaks of thermoelectric figure of merit ($ZT$) in $n$$-$type Si$-$XII are higher than that of $p$$-$type ones along the same direction. Owing to the lower lattice thermal conductivity and optimistic electrical conductivity, Si$-$XII exhibits larger optimal $ZT$ compared with Si$-$I in both $p$$-$ and $n$$-$type doping. For $n$$-$type Si$-$XII, the optimal $ZT$ values at 300, 400, and 500 K can reach 0.24, 0.43, and 0.63 along $x$ axis at carrier concentration of $2.6\times10^{19}$, $4.1\times10^{19}$, and $4.8\times10^{19}$~cm$^{-3}$, respectively. The reported results elucidate that the metastable Si could be integrated to the thermoelectric power generator.

cond-mat.mtrl-sci↗

arXiv2021

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Machine learning analysis of tunnel magnetoresistance of magnetic tunnel junctions with disordered MgAl2O4

Through Bayesian optimization and the least absolute shrinkage and selection operator (LASSO) technique combined with first-principles calculations, we investigated the tunnel magnetoresistance (TMR) effect of Fe/disordered-MgAl2O4(MAO)/Fe(001) magnetic tunnel junctions (MTJs) to determine structures of disordered-MAO that give large TMR ratios. The optimal structure with the largest TMR ratio was obtained by Bayesian optimization with 1728 structural candidates, where the convergence was reached within 300 structure calculations. Characterization of the obtained structures suggested that the in-plane distance between two Al atoms plays an important role in determining the TMR ratio. Since the Al-Al distance of disordered MAO significantly affects the imaginary part of complex band structures, the majority-spin conductance of the Δ1 state in Fe/disordered-MAO/Fe MTJs increases with increasing in-plane Al-Al distance, leading to larger TMR ratios. Furthermore, we found that the TMR ratio tended to be large when the ratio of the number of Al, Mg, and vacancies in the [001] plane was 2:1:1, indicating that the control of Al atomic positions is essential to enhancing the TMR ratio in MTJs with disordered MAO. The present work reveals the effectiveness and advantage of material informatics combined with first-principles transport calculations in designing high-performance spintronic devices based on MTJs.

cond-mat.mtrl-sci↗