jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luiz Gustavo Zandavalli | Marcos V. Barp | Sebastian Vecino-Mantilla | Edoardo Gallo | Massimiliano Lo Faro
Source: Journal of The Electrochemical Society (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Lattice Parameters in Perovskite Oxygen Electrodes for SOECs Since their discovery, perovskite oxides have been widely studied for applications such as energy storage, fuel cells, and solar cells. Their crystal structure, defined by lattice parameters and atomic positions, governs material stability, critical for solid oxide electrochemical cells. This work aims to develop machine learning models comparing three different tree-based approaches, Decision Tree, Random Forest and XGBoost, evaluating how their different complexity affects performance in predicting the lattice constant of cubic ABO 3 perovskite oxides with cations in two different oxidation states (A 3+ B 3+ and A 2+ B 4+ ). These models have been trained and tested with databases extracted from publications. The evaluation metrics for such models are Mean Absolute Error, Root Mean Squared Error and R 2 score, k-fold cross-validation was implemented for the testing of all models and mean ± standard deviation for all metrics was reported. Also, Multiple Linear Regression was included as a baseline model for performance comparison. The models used ionic radius, electronegativity, and valence of the A and B cations as features and statistical analysis of the target distributions informed model selection, highlighting differences in linear and nonlinear behavior across datasets. XGBoost consistently delivered the best predictive performance between tree models, demonstrating robustness and suitability for future applications. Valence (chemistry) ; Lattice constant ; Decision tree ; Lattice (music) ; Random forest ; Crystal structure ; Robustness (evolution) ; Standard deviation ; Linear regression Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; X-ray Diffraction in Crystallography
Authors: Yixuan Zhai | Yubin Zhang | Xuyang Gao | Wenkai Xu | Shuliang Ma | Xi Zhang | Yiqiong Yang
Source: Dyes and Pigments (article, 2026-03-19)
Field: Chemistry — Inorganic Chemistry

Machine-learning-assisted intelligent identification of antibiotics using a rare-earth-functionalized metal-organic frameworks fluorescent sensor Fluorescence ; Software portability ; Operability ; Analyte ; Identification (biology) ; Chemical sensor ; Reuse Metal-Organic Frameworks: Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Rodrigo Moraes Kunrath | Andreas W. Kempa-Liehr | Wei Gao | Zhengwei Li | M. Beatrix Jones
Source: CORROSION (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

A Damage Accumulation-Based Model for Atmospheric Corrosion Time of Wetness Using Time-series Analytics Atmospheric corrosion is strongly influenced by the persistence of surface moisture, commonly quantified through the time of wetness (TOW). Traditional TOW models, such as those defined by ISO 9223, rely on fixed humidity and temperature thresholds and often fail to capture the complexity of real-world environmental conditions. This study introduces a novel TOW modelling approach based on the concept of damage accumulation supported by machine learning. Using environmental data from ten monitoring stations across New Zealand, 61 features were developed to simulate conditions conducive to electrolyte formation. Feature importance analysis identified three key predictors: two condensation-related humidity factors and cumulative solar radiation. A simplified corrosion model based on these three predictors achieved strong predictive performance (R² = 0.909). Bayesian linear regression confirmed the statistical relevance and directional influence of the selected factors. Notably, the Condensation Factor and the Dilution Factor demonstrated opposing effects on corrosion rate. The Condensation Factor, linked to early-stage condensation and the formation of concentrated electrolytes in droplets, showed a positive correlation with corrosion. In contrast, The Dilution Factor, associated with intense wetting events, exhibited a negative correlation, possibly due to the dilution of surface contaminants. The created model outperforms current ISO’s TOW model to predict atmospheric corrosion rate. This framework highlights the role of moisture in atmospheric corrosion and provides a foundation for future integration with corrodent-specific models. The proposed methodology offers a scalable and interpretable tool for improving corrosion rate predictions in diverse environmental settings. Corrosion ; Relative humidity ; Dilution ; Condensation ; Humidity ; Evaporation Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Concrete Corrosion and Durability
Authors: Yongkang Zhen | Wencheng Yao | Chaofeng Zhao | Liping Song | Yuejie Ai
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

An effective strategy of designing thiazole-linked zwitterionic covalent organic frameworks with anti-biofouling capability for selective uranium extraction from seawater Uranium ; Selectivity ; Seawater ; Covalent bond ; Uranyl ; Selective adsorption ; Adsorption ; Extraction (chemistry) Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Qiao Chen | Dongyang Li | Bing Yang | Yunqing Tang | Lin Li
Source: Applied Thermal Engineering (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Neural networks accelerate discovery of ultralow thermal conductivity configurations in disordered graphene/h-BN multilayers Thermal conductivity ; Superlattice ; Boron nitride ; Thermoelectric materials ; Phonon ; Hyperparameter ; Heterojunction ; Artificial neural network Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Syed Omer Shah | Mohammed Maqsood Ahmed | Danish Mohiuddin Mohammed | Shahnawaz Alam | Mohd Vahaj ur Rahman
Source: arXiv (Cornell University) (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion Most machine learning models for molecular property prediction rely on a single molecular representation (either a sequence, a graph, or a 3D structure) and treat molecular geometry as static. We present MolFM-Lite, a multi-modal model that jointly encodes SELFIES sequences (1D), molecular graphs (2D), and conformer ensembles (3D) through cross-attention fusion, while conditioning predictions on experimental context via Feature-wise Linear Modulation (FiLM). Our main methodological contributions are: (1) a conformer ensemble attention mechanism that combines learnable attention with Boltzmann-weighted priors over multiple RDKit-generated conformers, capturing the thermodynamic distribution of molecular shapes; and (2) a cross-modal fusion layer where each modality can attend to others, enabling complementary information sharing. We evaluate on four MoleculeNet scaffold-split benchmarks using our model's own splits, and report all baselines re-evaluated under the same protocol. Comprehensive ablation studies across all four datasets confirm that each architectural component contributes independently, with tri-modal fusion providing 7-11% AUC improvement over single-modality baselines and conformer ensembles adding approximately 2% over single-conformer variants. Pre-training on ZINC250K (~250K molecules) using cross-modal contrastive and masked-atom objectives enables effective weight initialization at modest compute cost. We release all code, trained models, and data splits to support reproducibility. Conformational isomerism ; Fusion ; Prior probability ; Initialization ; Context (archaeology) ; Property (philosophy) ; Representation (politics) ; Component (thermodynamics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Vidura Kanakaratne | Alireza Behvar | Francis Y. Asare Baffour | Harsh Bajaj | Anwar Algamal | Ahu Celebi | Mohammad Elahinia
Source: Smart Materials and Structures (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Sinter-based additive manufacturing of NiTiHf shape memory alloys: a comprehensive review of binder jetting approaches, challenges, and prospects Abstract NiTiHf shape memory alloys (SMAs) have garnered increasing attention due to their elevated transformation temperatures (TTs), enhanced strength, and stable superelasticity, outperforming binary NiTi in demanding environments. The addition of hafnium enables TTs exceeding 500 °C, while also influencing microstructure through the formation of strengthening H-phase precipitates. However, NiTiHf processing remains challenging due to compositional sensitivity, oxidation, and the need for precise thermal control. Binder jetting additive manufacturing (BJAM), a non-fusion powder-based AM technique, presents a promising solution. BJAM offers low thermal gradients, high geometric flexibility, and excellent compositional fidelity, traits particularly advantageous for complex, metastable ternary SMAs like NiTiHf. This review systematically discusses NiTiHf SMAs, including compositional effects, phase transformation pathways, thermal hysteresis behavior, and mechanical performance. It further evaluates the principles, advantages, and limitations of BJAM and highlights its potential for producing near-net-shape NiTiHf components with reduced residual stress and customizable microstructures. Finally, the review identifies critical research gaps in BJAM-processed NiTiHf and outlines future directions involving process optimization, powder design, and integration of modeling tools such as CALPHAD and machine learning. By bridging alloy design with advanced manufacturing, this work aims to support the scalable production of next-generation high-performance SMAs. Shape-memory alloy ; Residual stress ; CALPHAD ; Thermal ; Transformation (genetics) ; Microstructure ; Ternary operation ; Process (computing) ; Process integration Shape Memory Alloy Transformations ; Cellular and Composite Structures ; Additive Manufacturing Materials and Processes
Authors: Shuyue Shan | Cong Yu | S Hücümenog ̆ lu | Zhongwei Zhang | Jie Chen
Source: ACS Applied Materials & Interfaces (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Ultrahigh Interfacial Thermal Conductance in Borophene Heterostructures Enabled by the Phonon Bridge The rapid integration of nano- and microdevices underscores the critical role of interfacial thermal conductance (ITC) for effective heat dissipation, which is vital for device performance and stability. As an emerging two-dimensional (2D) material, borophene is a promising candidate for thermal management due to its excellent thermal and mechanical properties. In this study, we employ molecular dynamics simulations with machine learning potential to investigate the ITC of 2D borophene heterostructures formed by different edge orientations. Our results show that these heterostructures exhibit exceptionally high ITC values up to 6.46 GW m<sup>-2</sup> K<sup>-1</sup> at 300 K, surpassing most typical interfaces. This ultrahigh ITC result is primarily attributed to the substantial overlap in the phonon spectrum between two borophene polymorphs, which forms an ideal phonon bridge across the interface, as evidenced by their full-range phonon transmission. Since the matching of the phonon spectrum in the borophene heterostructures is inherently strong, we find that the variations of ITC values are significantly influenced by factors such as interfacial bonding and structural regularity induced by the edge orientations. Our analysis of steady-state atomic heat flux highlights the importance of bonding alignment, while calculations of Young's modulus confirm the influence of interfacial bonding strength. This work offers key insights into the thermal transport mechanisms across borophene heterostructures and thus paves the way for their applications in advanced thermal management technology. Borophene ; Phonon ; Heterojunction ; Thermal conductivity ; Thermal ; Semiconductor ; Molecular dynamics ; Work (physics) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Syed Omer Shah | Mohammed Maqsood Ahmed | Danish Mohiuddin Mohammed | Shahnawaz Alam | Mohd Vahaj ur Rahman
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion Most machine learning models for molecular property prediction rely on a single molecular representation (either a sequence, a graph, or a 3D structure) and treat molecular geometry as static. We present MolFM-Lite, a multi-modal model that jointly encodes SELFIES sequences (1D), molecular graphs (2D), and conformer ensembles (3D) through cross-attention fusion, while conditioning predictions on experimental context via Feature-wise Linear Modulation (FiLM). Our main methodological contributions are: (1) a conformer ensemble attention mechanism that combines learnable attention with Boltzmann-weighted priors over multiple RDKit-generated conformers, capturing the thermodynamic distribution of molecular shapes; and (2) a cross-modal fusion layer where each modality can attend to others, enabling complementary information sharing. We evaluate on four MoleculeNet scaffold-split benchmarks using our model's own splits, and report all baselines re-evaluated under the same protocol. Comprehensive ablation studies across all four datasets confirm that each architectural component contributes independently, with tri-modal fusion providing 7-11% AUC improvement over single-modality baselines and conformer ensembles adding approximately 2% over single-conformer variants. Pre-training on ZINC250K (~250K molecules) using cross-modal contrastive and masked-atom objectives enables effective weight initialization at modest compute cost. We release all code, trained models, and data splits to support reproducibility. Conformational isomerism ; Fusion ; Prior probability ; Initialization ; Context (archaeology) ; Property (philosophy) ; Representation (politics) ; Component (thermodynamics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yuanbiao Hu | Zhe Hu | Ce Li | Junfeng Zhang | Xirun Tong | Junjie Han | Wen Zhang
Source: Analytical Chemistry (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

LA-TReQNet: Improving Multielement Quantification Model for Laser Ablation Inductively Coupled Plasma Mass Spectrometry Based on Deep Learning Network Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a widely used quantitative technique. However, conventional calibration approaches that depend on internal standards, external reference materials, and linear regression are subject to practical limitations. To address these challenges, we present LA-TReQNet, an end-to-end deep learning framework, that enables fully automated quantitative calibration in LA-ICP-MS. A CNN-LSTM architecture was trained on a data set of 221,364 labeled mass spectra from 5676 samples. We demonstrate that preprocessing of mass spectrometry data substantially impacts model performance and propose an optimized strategy incorporating power transformer-based standardization and data set grouping. By training on extensive multielement data sets, the deep learning model captures complex empirical relationships, thus establishing for the first time a standard-free calibration approach. The optimized LA-TReQNet model accurately quantified 39 elements in three RMs (BCR-2G, BHVO-2G, and BIR-1G) from independent laboratories, confirming its robustness to data source variations. When applied to a broader set of RMs, LA-TReQNet achieved deviations of 0.2% ± 5.8% (SD, <i>n</i> = 198) for major elements and -0.9% ± 9.2% (SD, <i>n</i> = 898) for trace elements relative to certified reference values. The results confirm that deep learning-based elemental quantification achieves accuracy on par with conventional approaches while eliminating the reliance on internal or external standards, thus significantly expanding the applicability of LA-ICP-MS to complex and diverse samples. Moreover, LA-TReQNet eliminates data quality fluctuations from researcher experience differences in traditional handling, improving both processing efficiency and result stability. Calibration ; Preprocessor ; Robustness (evolution) ; Deep learning ; Mass spectrometry ; Laser ablation ; Data pre-processing ; Data set ; Inductively coupled plasma mass spectrometry ; Analytical Chemistry (journal) Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Mass Spectrometry Techniques and Applications
Authors: Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations through Ewald summation of electrostatic contributions. In contrast, machine learning potentials (MLPs) typically rely on local atomic environment descriptors, and long-range interactions are often neglected. Such approximations may introduce systematic energetic errors and lead to inaccuracies in predicted material properties. To systematically investigate the impact of long-range interactions in ferroelectric BaTiO3 within the framework of MLPs, we developed a long-range MACELES model and compared its performance with the previously reported BaTiO3 MACE model across four key properties (phonon dispersion, phase transition behavior, mechanical response, and ferroelectric properties including dielectric constants). We find that qualitative behaviors, including phase transitions, stress-induced polarization switching, and polarization-electric field hysteresis, are consistently reproduced by both models. In contrast, quantitative properties such as transition temperatures, elastic constants, and dielectric constants exhibit systematic improvements in MACELES model, highlighting the importance of incorporating long-range electrostatics for accurately describing the structural and dielectric responses of BaTiO3. These results suggest that while long-range interactions play a role in improving quantitative accuracy, their omission does not significantly alter the qualitative ferroelectric behavior of BaTiO3. Ferroelectricity ; Dielectric ; Phase transition ; Electrostatics ; Polarization (electrochemistry) ; Electric field ; Dielectric response ; Phase (matter) ; Field (mathematics) Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Gianmarco Goycochea Casas | Zool Hilmi Ismail | Helio Garcia Leite
Source: Forests (article, 2026-01-14)
Field: Chemistry — Organic Chemistry

Computer Vision, Machine Learning, and Deep Learning for Wood and Timber Products: A Scopus-Based Bibliometric and Systematic Mapping Review (1983–2026, Early Access) This systematic mapping review and bibliometric analysis examines Scopus-indexed research on computer vision, image processing, and deep learning applied to wood and timber materials and products. A rule-based Scopus search (TITLE-ABS-KEY, 9 December 2025), combining wood and timber terms with imaging and computer vision terminology, followed by duplicate removal and structured exclusions, retained 1019 papers (1983–2026, early access) covering surface inspection, internal imaging, species identification, processing operations (log-yard/sawmill/panels), automation, dimensional metrology, and image-based property/structure characterization. The papers were classified into nine application categories and three methodological classes using improved rule-based classification with weighted scoring and exclusion rules. Paper output continues to accelerate, with 63.7% of papers published since 2016; Wood Surface Quality Control dominates (48.3%), followed by 3D and Internal Wood Imaging (13.6%), Wood Microstructure and Characterization (10.1%), and Wood Species and Origin Identification (10.6%). Methodologically, classical computer vision prevails (73.6%). Deep learning accounts for 26.4% of the corpus overall and 48.8% of papers from 2023–2026 (early access), while classical computer vision remains prevalent (70.1%) across most categories; the dataset totals 11,961 citations (mean: 11.74 per paper). Validation on 97 papers showed 80.41% accuracy for methodological classification and 70.1% for application categories. We quantitatively map method evolution across the nine categories, introducing a tailored taxonomy and tracking the shift from classical vision to deep learning at the category level. The remaining gaps include dimensional measurement automation, warp detection, sawing optimization, and benchmark datasets, with future directions emphasizing Vision Transformers, multi-modal sensing, edge computing, and explainable AI for certification. Deep learning ; Benchmark (surveying) ; Identification (biology) ; Taxonomy (biology) ; Machine vision ; Image processing Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Yoshiyasu Takefuji
Source: Food Chemistry (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Ensuring reliable feature importance in food chemistry AI Feature (linguistics) ; Audit ; SAFER ; Nonparametric statistics ; Logistic regression ; Stability (learning theory) ; Parametric statistics ; Snack food Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nutrition, Genetics, and Disease
Authors: Tao Feng | Xiaoliang Zhang | Dawei Tang | Shigeo Maruyama | Ya Feng
Source: Nano Letters (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Bose–Einstein Phonon Statistics Explain Drastic Thermal Conductivity Reduction in SWCNT Bundles The drastic reduction in thermal conductivity observed in bundled single-walled carbon nanotubes (SWCNTs) has eluded accurate theoretical prediction. We show that Bose-Einstein phonon statistics are essential to reproduce this effect. Using a machine-learning-driven quantum-statistical framework, combining a neuroevolution potential with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) calculations, we quantitatively predict an 81% reduction for a 5 μm seven-SWCNT bundle, matching experiments. The reduction originates from two distinct mechanisms enabled by quantum statistics: (1) the breaking of rotational symmetry, which directly quenches high-conductivity, symmetry-sensitive phonon modes like the twist mode; and (2) the introduction of abundant intertube phonon modes, which expands the three-phonon scattering phase space and elevates scattering rates universally. These insights, inaccessible to classical simulations, resolve a long-standing experimental-theoretical discrepancy and provide a predictive framework for designing SWCNT-based thermal materials. Zigzag ; Thermal conductivity ; Phonon ; Boltzmann equation ; Scattering ; Symmetry breaking ; Carbon nanotube ; Phonon scattering ; Quantum Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science