jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulou Ouyang | Aolei Ma | Guotao Yuan | Pinzhen Jia | zhenkun Tang | Zhongwei Zhang | Jie Chen
Source: Journal of Applied Physics (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Effect of superlattice period length on thermoelectric performance: A case study of monolayer CrS2, CrSe2, and CrS2/CrSe2 lateral superlattice Two-dimensional (2D) lateral superlattice is a promising route to tailor thermoelectric properties, yet the impact of superlattice period length on the modulation of phonon and electronic transport remains insufficiently characterized. Using a monolayer CrS2/CrSe2 superlattice as a model system, we combine density-functional theory with a machine-learning potential to quantify the period-dependent thermoelectric properties. Due to the reduced phonon group velocities and shortened phonon lifetimes, our calculation results reveal that the room-temperature lattice thermal conductivity (κp) of short-period superlattice (SS) and long-period superlattice (LS) structures along the modulation direction is reduced by 71.3% and 82.9%, respectively, compared with that of the pristine CrS2. Moreover, the interface-induced charge redistribution in LS disrupts electronic transport continuity, suppressing electrical conductivity (σ). By contrast, SS retains more extended charge states while maintaining σ with reduced κp. As a result, the ZT value of SS (∼0.39) is higher than that of LS (∼0.21) at 600 K, despite LS having a lower κp. This work emphasizes the importance of co-optimizing phonon and electronic transport in superlattice structures by tuning the superlattice period, providing engineering strategies for designing 2D superlattice thermoelectric materials. Superlattice ; Thermoelectric effect ; Phonon ; Monolayer ; Thermoelectric materials ; Electrical resistivity and conductivity ; Thermal conductivity ; Lattice (music) Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Thermal properties of materials
Authors: Liming Tan | Zi Wang | Yongan Gui | Jiahao Tu | Lan Huang | Wei Li | Yong Liu | Feng Liu
Source: Materials Characterization (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Revolutionizing multi-scale phase identification in superalloys via advanced CNN-transformer integration Segmentation ; Convolutional neural network ; Adaptability ; Image segmentation ; Pattern recognition (psychology) ; Context (archaeology) ; Image processing ; Superalloy Machine Learning in Materials Science ; Advanced Neural Network Applications ; Additive Manufacturing Materials and Processes
Authors: Quoc Truong Nguyen
Source: Open MIND (preprint, 2026-02-07)
Field: Materials Science — Materials Chemistry

TruongSSCL: A Deterministic High-Rigor Statistical–Conservation Law Framework for Particle Discovery and Resonance Detection TruongSSCL is a deterministic mathematical–physical framework for particle discovery based on exact conservation laws, closed-form likelihood theory, and Fisher-information geometry. The project provides: • a fully rigorous theoretical derivation (axioms, theorems, proofs)• an exact statistical solver (no stochastic training or Monte Carlo)• a deterministic discovery pipeline for invariant-mass resonance search• reproducible numerical implementation Unlike machine-learning based approaches, the method relies solely on analytic likelihood maximization and conservation constraints, ensuring: – full reproducibility– no randomness– closed-form statistical guarantees– interpretable physical parameters The framework is applicable to: • high-energy particle physics (resonance discovery, Higgs-like searches)• statistical mechanics• mathematical physics• computational chemistry (element / structure inference) An example pipeline using public ATLAS/CMS open data is included for demonstration. All computations are deterministic and exactly reproducible. Pipeline (software) ; Computation ; Conservation law ; Monte Carlo method ; Solver ; Statistical model ; Maximization ; Expectation–maximization algorithm ; Calculus (dental) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Nuclear physics research studies
Authors: Yu-Jie Cen | Sandro Wieser | Georg K. H. Madsen | Jesus Angel Varela Carrete
Source: npj Computational Materials (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective Nanotubes, with their high aspect ratio and tunable thermal conductivities, are promising nanoscale heat-management components. However, their performance is often constrained by thermal resistance arising from structural defects or interfaces. Here, we examine how structural symmetry influences thermal transport through defect-laden sections. We introduce a framework that integrates representation theory with a mode-resolved Green's function approach, enabling symmetry-resolved analysis of phonon transmission in quasi-1D systems. To capture the intrinsic symmetries of such systems and avoid artifacts, we employ line-group theory, which introduces quantum numbers that partition phonon branches into symmetry-defined subsets for clearer mode classification. Force constants and phonons are obtained using an Allegro-based machine-learning potential with near-ab initio accuracy. Applying this to single- and multi-wall MoS<sub>2</sub>-WS<sub>2</sub> nanotubes, we link transmission probabilities of individual modes to structural symmetry. Counterintuitively, strong symmetry breaking can enhance heat transport by relaxing selection rules and opening additional transmission channels. Molecular dynamics confirms that this behavior persists even when anharmonicity is considered. The fact that higher disorder introduced through defects can enhance thermal transport, and not just suppress it, demonstrates the critical role of symmetry in deciphering the nuances of nanoscale thermal transport. Anharmonicity ; Phonon ; Quantum ; Thermal ; Function (biology) ; Partition function (quantum field theory) ; Symmetry (geometry) ; Symmetry breaking Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Y. Jun Xu | Xiaoming Shi | Zhaochen Xi | Shouzhe Dong | Changqing Guo | Rongzhen Gao | Letao Yang | Jing Wang | Dao Zhou | Houbing Huang
Source: Advanced Science (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Guided Design of Incommensurate Antiferroelectrics via Field‐Driven Phase Engineering Antiferroelectrics, defined by antiparallel polarization configurations, have emerged as promising dielectric materials for high-performance energy storage applications. The energy storage density and efficiency of antiferroelectrics are governed by the regulation of electric field-driven antiferroelectric-ferroelectric phase transitions (and their reversibility). Herein, we propose a machine learning-guided phase-field simulation framework to accelerate the design of energy storage ceramics. Taking PbZrO<sub>3</sub>-based incommensurate antiferroelectrics as an example, we establish quantitative mappings between energy storage density/efficiency and key features (point defect, antiphase boundary energy, grain size, and strain) using XGBoost. The model exhibits high accuracy for energy storage performance prediction, with R<sup>2</sup> values of 0.99. SHAP interpretable machine learning analysis further deciphers the correlations between multiple features and energy storage properties, while phase-field simulations clarify the mechanisms. Guided by this framework, we achieve a high energy storage density of 22.1 J/cm<sup>3</sup> with 96.1% efficiency. This work provides a theoretical foundation and a generalized approach for regulation of antiferroelectric energy storage properties and mechanisms of phase transition. Antiferroelectricity ; Antiparallel (mathematics) ; Energy storage ; Dielectric ; Phase boundary ; Computer data storage ; Polarization (electrochemistry) Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Shuaimin Zheng | Junting Lei | Peitao Xie | Duo Pan | Chuntai Liu | Changyu Shen
Source: iScience (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

AI empowerment: Establishing the synergy between artificial intelligence and electromagnetic shielding materials research With the increasing intelligence of electronic information systems, urgent demand exists for high-performance customized electromagnetic shielding materials. However, traditional research and development paradigms are constrained by critical bottlenecks, like multi-parameter coupling, high trial-and-error costs, and multi-scale design hurdles, failing to satisfy efficient material development needs. Artificial intelligence (AI), leveraging its core advantages of data-driven approaches and algorithmic optimization, offers a transformative paradigm to overcome these bottlenecks. Although AI-driven methodologies have demonstrated tremendous potential in the design of electromagnetic shielding materials, systematic barriers from inadequate interdisciplinary collaboration still limit its technological empowerment. In response to these challenges, we highlight the following solutions to advance AI-enabled electromagnetic shielding material design: (1) developing neural network models driven by data-physics integration, (2) developing domain-specific large language models for electromagnetic shielding materials, (3) establishing comprehensive databases for electromagnetic shielding materials, and (4) promoting domain-specific data sharing and the construction of standardized protocols. Electromagnetic shielding ; Transformative learning ; Applications of artificial intelligence ; Big data ; Artificial neural network ; Limit (mathematics) ; Electromagnetic compatibility Machine Learning in Materials Science ; Electromagnetic wave absorption materials ; Heusler alloys: electronic and magnetic properties
Authors: Zhekai Chen | Jue Lu | Xi Zhang | Jing Wang | Lin Chen
Source: NMR in Biomedicine (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Enhanced Glioma Genotype Prediction Using CEST MRI With Full Z‐Spectrum Input, Pixel‐Level Learning, and Majority Voting Accurate glioma genotype prediction, such as the isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, is critical for personalized treatment planning and prognosis. Chemical exchange saturation transfer (CEST) MRI enables noninvasive assessment of tumor metabolism and the microenvironment, offering promising potential for genotype prediction. However, existing CEST-based diagnostic methods are limited by information loss from simplified quantification and by instability arising from ROI-dependent analysis. To address these limitations, a deep learning framework was developed that integrates the full Z-spectrum input, pixel-level training, and a majority voting mechanism to improve prediction accuracy and robustness. In a cohort of 84 patients, a feedforward neural network (FNN) was trained to process the entire Z-spectrum and generate pixel-wise predictions, which were subsequently aggregated into patient-level outcomes through majority voting. T-distributed stochastic neighbor embedding (t-SNE) was applied to evaluate the effectiveness of the full Z-spectrum input. Model generalizability and robustness were assessed using fivefold cross-validation, and stability was quantified via the coefficient of variation (CoV). Prediction performance was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve (AUC). The proposed method demonstrated improved interclass separability with the full Z-spectrum input (t-SNE score: 64.50), outperforming APTw (55.60) and Lorentzian fitting (56.97). Stable prediction performance was achieved for both IDH (accuracy: 0.86 ± 0.04 and AUC: 0.91 ± 0.03) and MGMT (accuracy: 0.82 ± 0.02 and AUC: 0.91 ± 0.04) genotypes. The approach also showed strong robustness to ROI selection, with a CoV of 0.69%, compared to 7.44% for APTw-mean and 3.67% for Lorentzian fitting (LF-mean). These findings support the effectiveness of combining the full Z-spectrum input, pixel-level learning, and majority voting to improve the reliability of noninvasive glioma genotype prediction using CEST MRI. Robustness (evolution) ; Isocitrate dehydrogenase ; Glioma ; Artificial neural network ; Majority rule ; Pattern recognition (psychology) Lanthanide and Transition Metal Complexes ; MRI in cancer diagnosis ; Advanced MRI Techniques and Applications
Authors: Nesrine Ben Amor | Muhammad Tayyab Noman | Michal Petrů
Source: Applied Soft Computing (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Generative Adversarial Network-based machine learning framework for mapping and predicting nanostructures distribution in nanoparticulate composites Interpretability ; Robustness (evolution) ; Artificial neural network ; Mean squared error ; Uncertainty quantification ; Generative grammar ; Bayesian probability ; Nonlinear system Machine Learning in Materials Science ; Polymer Nanocomposites and Properties ; Hydrological Forecasting Using AI
Authors: John T. Allen | Daelyn A. Moore | Geoffrey D. Smith | Brandon Rotavera
Source: Journal of Molecular Spectroscopy (article, 2026-03-01)
Field: Chemistry — Analytical Chemistry

Oversampling and feature selection techniques in binary molecular classification of VUV absorption spectra Oversampling ; Binary number ; Spectral line ; Selection (genetic algorithm) ; Feature (linguistics) ; Absorption spectroscopy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hyrum J. Redd | John H. Kalivas
Source: Journal of Chemometrics (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

A Perspective on Using Immersive Analytics With Virtual Reality for One‐Class Classification Decisions ABSTRACT Multisensory tools are beginning to reformat research and education in chemistry and many other fields. For example, translating infrared spectra into sound (sonification) can unveil molecular facts that the eye might miss. Tactile approaches are used with 3‐D printed scientific data such as electrophoresis gels. These scientific advances are expanding the way we study data and are part of a broader area known as immersive analytics. While immersive analytics cover all the human senses for data immersion, this perspective focuses on data visualization in virtual reality (VR). By visualizing chemometric data information in VR, a human can use their extensively trained pattern recognition and problem‐solving skills to make final analysis decisions. As an example, presented is a feasibility study for one‐class classification with a focus on correcting samples machine learning (ML) identified as false positives (FPs) and false negatives (FNs) that typically reside in the gray zone (class fringe samples) to respective true negatives (TNs) and positives (TPs). Conversely, while not expected in a well‐designed VR universe, it is possible that TP and TN prediction samples in the gray zone could be classified as respective FNs and FPs by decisions in VR. Results are presented for three datasets showing the feasibility of using VR for classification decisions. These datasets are clam contamination and two cancer detection situations. Some brief comments on the potential of using VR to identify local structure within a class are also provided using a quantitative structure–activity relationship (QSAR) dataset. Virtual reality ; Visualization ; Perspective (graphical) ; False positive paradox ; Analytics ; Focus (optics) ; Augmented reality ; Data visualization Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies
Authors: Fangyuan Yu | Ping Sun | Y. J. Liu | Xiaodong Wang | Shuaihang Hou | Zuoxu Wu | Liming Xiao | Jian Wang | Zhihua Miao | Jinhua Mao | Qian Zhang | Feng Cao
Source: Advanced Energy Materials (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Three‐Dimensional Ag <sub>2</sub> Se Thermoelectric Network Advances Multimodal Intelligent Perception ABSTRACT Embodied artificial intelligence (Embodied AI) depends on the integration of multimodal sensing and physical interaction to achieve autonomous perception and adaptive control in complex environments. However, conventional multimodal sensing systems often rely on separate sensors for each physical stimulus, leading to integration challenges and signal interference. Here, we report a monolithic bimodal sensor based on a 3D Ag 2 Se thermoelectric network that enables simultaneous and decoupled detection of temperature and pressure within a single device. The sensor demonstrates a high temperature sensitivity of −122.7 µV K −1 with a fast response time of ∼0.14 s, and a high pressure sensitivity of −2.94% kPa −1 with a fast response time of about 0.08 s, indicating excellent signal isolation between the two sensing channels. When combined with deep learning, the sensor enables a real‐time intelligent sorting system that achieves 96% recognition accuracy across nine materials, outperforming single‐mode sensing approaches. This work provides an integrated multimodal sensing solution that advances the deployment of embodied AI in complex environments. Sensitivity (control systems) ; Nanosensor ; Pressure sensor ; SIGNAL (programming language) ; Software deployment ; Sorting ; System integration ; Wireless sensor network Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Liang Song | Yun Peng | Haiyan Zhao | Lin Zhao
Source: JOM (article, 2026-03-05)
Field: Materials Science — General Materials Science

Bainite Degradation in Simulated Fire-Resistant Steel Exposed at 600°C for 4 Hours by Coupling Phase-Field Crystal Method and CALPHAD CALPHAD ; Degradation (telecommunications) ; Bainite ; Coupling (piping) ; Crystal (programming language) Engineering and Material Science Research ; Fire effects on concrete materials ; High-Temperature Coating Behaviors
Authors: Sandip K. Jagadale | Madhuri Pagale | Pratham Bokefode | Shamal Dighole | Lokesh Ajmera | Isha Thakur | Mansi Sopan Rathod | Sopan  M. Rathod
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning–Driven Prediction of Band Gap and Electrical Conductivity in Synthesizable Nanomaterials Benchmarking ; Cluster analysis ; Decision tree ; Task (project management) ; Band gap ; Bridge (graph theory) ; Mean squared error Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: P. -R. Li | J. Zhong
Source: Journal of Dynamic Behavior of Materials (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven High-Accuracy Prediction of Concrete Penetration Depth Projectile ; Outlier ; Artificial neural network ; Shadowgraphy ; Solid mechanics ; Penetration (warfare) ; Explosive material ; Test data ; Boosting (machine learning) High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Rock Mechanics and Modeling
Authors: Yuzhou Hao | Yujie Liu | Xuejie Li | Turab Lookman | Xiangdong Ding | Jun Sun | Zhibin Gao
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

LLM-driven discovery for carbon allotropes with bond-network entropy The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between $sp, sp^2,$ and $sp^3$ hybridization states dictates material performance, we establish a closed-loop AI framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report ``yne-diamond C$_{12}$'' and ``yne-hex-diamond C$_{8}$'', which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed $sp$-$sp^3$ hybridization. Furthermore, we discovered a complex $sp$-$sp^2$-$sp^3$ hybridized C$_{12}$ phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond 96 GPa). Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with machine learning potentials for the accelerated inverse design of functional materials. Anisotropy ; Inverse ; Carbon fibers ; Thermal conductivity ; Entropy (arrow of time) ; Diamond ; Phase (matter) ; Phase space Machine Learning in Materials Science ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Mingchun Li | Naishi Feng | Qiang Wang | Dali Chen
Source: IEEE Transactions on Instrumentation and Measurement (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Shape2Mask: A Weakly Supervised Instance Segmentation Method for Aluminum Alloy Precipitations Based on Prior Shape Knowledge The size, distribution, and shape of precipitations in aluminum alloys critically influence their mechanical properties. Transmission electron microscopy (TEM) enables the analysis and measurement of these nanoscale precipitations. However, the inherent fuzziness and overlapping in TEM images necessitate annotation by domain experts. It not only increases the cost of pixel-level annotation but also poses significant challenges for segmentation algorithms. To this end, we present Shape2Mask, a novel instance segmentation framework that utilizes low-cost box supervisions to predict masks by parameterized shape modeling. First, it establishes a novel shape-based segmentation paradigm. Instead of pixel-wise prediction, it predicts shape parameters of precipitations, effectively handling fuzziness in TEM images. Second, we design a hierarchical module based on Graph Neural Network (GNN) and recent Kolmogorov-Arnold Networks (KAN). The former is employed to extract effective geometric shape representations, while the latter is used to fit nonlinear shape parameters. Furthermore, benefiting from the shape-based paradigm, we introduce a differentiable prior loss function, explicitly integrating domain knowledge into the deep model. Experiments demonstrate our method accurately segments three types of precipitations with an AP score of 29.5 under box supervision. Quantitative measurements show that Shape2Mask achieves errors as low as 1.2 nm and 9.8 nm² in precipitation length and area, respectively. It significantly outperforms existing weakly supervised methods for both segmentation and measurement metrics. These results prove the method's effectiveness not only in visual tasks but also in providing valuable quantitative measurements for potential materials science applications. Our source code and TEM image dataset are available at https://github.com/neulmc/Shape2Mask. Segmentation ; Pattern recognition (psychology) ; Image segmentation ; Alloy ; Feature extraction ; Scale-space segmentation Machine Learning in Materials Science ; Advanced Neural Network Applications ; Face recognition and analysis