jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lejia Zeng | Xintong Zhang | Yuchan Pei | Lifeng Zhao | Lan Hua | Jincai Yang | Niu Huang
Source: Open MIND (preprint, 2026-01-14)
Field: Materials Science — Materials Chemistry

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy. However, constructing robust and representative training datasets that capture subtle, system-specific interaction motifs remains challenging. We introduce PANIP (PAirwise Non-covalent Interaction Potential), an ensemble MLIP model built upon the NequIP framework and trained on non-covalent interactions (NCIs) between protein-derived fragments. PANIP is trained using an automated multi-fidelity active learning (MFAL) workflow, in which a representative training subset, termed PDB-FRAGID (PDB Fragment Interaction Dataset), was distilled from an otherwise prohibitively large pool of fragment dimers extracted from the Protein Data Bank (PDB). PANIP retains $ω$B97X-D3BJ/def2-TZVPP-level accuracy and achieves mean absolute errors below 0.2 kcal/mol on out-of-distribution systems, demonstrating excellent transferability across diverse NCI motifs. Compared to the widely used ANI-2x potential, PANIP delivers substantially lower errors, particularly for charged and strongly interacting dimers. Coupled with a fragmentation-based energy decomposition scheme, PANIP estimates protein-ligand binding energies at near force-field computational cost yet QM-level accuracy, enabling its use as a fragment-based scoring function that rivals specialized docking scoring functions. Transferability ; Protein–protein interaction ; Interaction energy ; Interatomic potential ; Function (biology) ; Training set ; Interaction model ; Docking (animal) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Y.F. He | M.Z. Li
Source: Journal of Non-Crystalline Solids (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Uncovering what machine learning learns from structure-dynamics correlations in Cu50Zr50 supercooled liquids Collinearity ; Supercooling ; Molecular dynamics ; Sensitivity (control systems) ; Experimental data ; Dynamics (music) ; Key (lock) ; Complex dynamics Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides
Authors: Khanh-Duy Cao-Phan | Hoang-Khang Dang | Huyen-Tran Tran-Quynh | Minh-Phuc Lam-Doan | Huyen-Trang Luu | Hong-Quan Bui
Source: Food Analytical Methods (article, 2026-04-02)
Field: Chemistry — Analytical Chemistry

Fast and Accurate Meat Freshness Classification Using Depthwise Separable Convolution and SPPF Convolution (computer science) ; Separable space ; Pattern recognition (psychology) ; Sensitivity (control systems) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Jaekyung Sung | B.S. Reddy | M. Premasudha | Ho-Jun Son | K.K. Cho | N.S. Reddy
Source: Journal of Energy Storage (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Empowering energy storage: Predicting specific capacitance for carbon-based supercapacitors with artificial neural networks Supercapacitors are a popular choice for energy storage in hybrid electric vehicles and portable electronic devices due to their quick charging/discharging rates, long lifespans, and low maintenance. This study examined the effect of various physicochemical characteristics of carbon-based materials on the capacitive performance of electric double-layer capacitors using an artificial neural network (ANN) model. The data was retrieved from published experimental datasets (over 300 articles) to build and evaluate ANN models. A total of 66 architectures were developed to estimate the effect of input variables on specific capacitance (F/g). Among all, the 13–9–9-1 architecture with 0.5/0.8 momentum term/learning rate and 15,000 iterations showed minimum error with high accuracy. The developed ANN model predicts the influence of input variables on specific capacitance through sensitivity analysis and qualitative assessment. The results are implemented in a modular, open-source application, enabling estimation of capacitance using only the properties of carbon-based electrodes as inputs. This study also presents a new comprehensive dataset of carbon electrodes for supercapacitors extracted from published research articles and highlights how electrochemical technology can benefit from ANN. • ANN models were developed to predict specific capacitance of carbon-based supercapacitors. • The best architecture was 13–9–9-1 with 0.5 MT, 0.8 LR, and 15,000 iterations. • A GUI was created based on the synaptic weights of the ANN model. • Quantitatively estimated the input variable's impact on specific capacitance. Supercapacitor ; Capacitance ; Artificial neural network ; Capacitive sensing ; Capacitor ; Sensitivity (control systems) Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Electric and Hybrid Vehicle Technologies
Authors: Gajanan M. Hingangavkar
Source: Next research. (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Fluorescent metal–organic and covalent organic frameworks: Materials development and applications Fluorescence ; Modular design ; Solar energy conversion ; Energy transformation ; Aggregation-induced emission ; Biomolecule ; Förster resonance energy transfer ; Linker Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Fritz Backofen | Ulrike Hähnel | Frank Hahn | Kristin Hockauf
Source: Metals (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Various Supervised Machine Learning Models for the Prediction of the Outcome of the Welded Bead Bending Test The Welded Bead Bending Test (WBBT) assesses steel structures intended for construction in Germany in accordance with ZTV-ING Part 4 or DBS 918 002-02, as specified in Stahl-Eisen-Prüfblatt (SEP) 1390. Test outcomes are classified as passed (p) if the minimum bending angle α≥60∘ is achieved without fracture, not passed (n.p.) if fracture occurs beforehand, and invalid if no crack propagates into the base material. This study evaluates eight supervised machine learning models for classification regarding their suitability for predicting WBBT results: Decision Tree Classifier (DT), Random Forest Classifier (RF), Histogram-based Gradient Boosting Classifier (HGBC), k-Nearest-Neighbour (KNN), Bagging Classifiers based on DT (BCDT) and RF (BCRF), Generalized Learning Vector Quantizer (GLVQ), and Generalized Matrix Learning Vector Quantizer (GMLVQ). An industrial dataset of approximately 3600 samples was compiled in collaboration with Chemnitzer Werkstoff und Oberflächentechnik GmbH (CEWUS). Evaluation metrics included Balanced Accuracy, Recall, Specificity, computation time, and prediction stability. BCDT and BCRF achieved the highest Balanced Accuracy (70.6% and 70.3%, respectively), with BCRF excelling in Specificity (82.5%), thereby reliably detecting the n.p. class. GLVQ and GMLVQ demonstrated superior stability (maximum variability between training and testing dataset 0.14% and 3.17%, respectively), while BCRF and GMLVQ required the longest training times (BCRF: 10 s–20 s; GMLVQ: up to 80 s). KNN proved least suitable for WBBT outcome prediction. Random forest ; Decision tree ; Classifier (UML) ; Boosting (machine learning) ; Gradient boosting ; Welding ; Support vector machine ; Computation Machine Learning in Materials Science ; Fatigue and fracture mechanics ; Rock Mechanics and Modeling
Authors: Lejia Zeng | Xintong Zhang | Yuchan Pei | Lifeng Zhao | Lan Hua | Jincai Yang | Niu Huang
Source: ArXiv.org (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy. However, constructing robust and representative training datasets that capture subtle, system-specific interaction motifs remains challenging. We introduce PANIP (PAirwise Non-covalent Interaction Potential), an ensemble MLIP model built upon the NequIP framework and trained on non-covalent interactions (NCIs) between protein-derived fragments. PANIP is trained using an automated multi-fidelity active learning (MFAL) workflow, in which a representative training subset, termed PDB-FRAGID (PDB Fragment Interaction Dataset), was distilled from an otherwise prohibitively large pool of fragment dimers extracted from the Protein Data Bank (PDB). PANIP retains $ω$B97X-D3BJ/def2-TZVPP-level accuracy and achieves mean absolute errors below 0.2 kcal/mol on out-of-distribution systems, demonstrating excellent transferability across diverse NCI motifs. Compared to the widely used ANI-2x potential, PANIP delivers substantially lower errors, particularly for charged and strongly interacting dimers. Coupled with a fragmentation-based energy decomposition scheme, PANIP estimates protein-ligand binding energies at near force-field computational cost yet QM-level accuracy, enabling its use as a fragment-based scoring function that rivals specialized docking scoring functions. Transferability ; Protein–protein interaction ; Interaction energy ; Interatomic potential ; Function (biology) ; Training set ; Interaction model ; Docking (animal) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Patxi Fernandez Zelaia | Noah Holtham | Brad Sampson | Sebastien Dryepondt | Michael  M. Kirka | Vincent Paquit | Christopher Ledford
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Active learning strategies for training deep learning microscopy segmentation models Segmentation ; Market segmentation ; Deep learning ; Active learning (machine learning) ; Image segmentation ; Active appearance model ; Pattern recognition (psychology) ; Image (mathematics) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Mineral Processing and Grinding
Authors: Armand J. Lannerd | Nathan J. Szymanski | Christopher J. Bartel
Source: Physical Review Materials (article, 2026-01-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermodynamics of proton insertion across the perovskite-brownmillerite transition in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi mathvariant="normal">L</mml:mi> <mml:msub> <mml:mi mathvariant="normal">a</mml:mi> <mml:mrow> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:msub> <mml:mi mathvariant="normal">S</mml:mi> <mml:msub> <mml:mi mathvariant="normal">r</mml:mi> <mml:mrow> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:msub> <mml:mi>Co</mml:mi> <mml:msub> <mml:mi mathvariant="normal">O</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>−</mml:mo> <mml:mi>δ</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> La$_{1-x}$Sr$_{x}$CoO3-$δ$ is a promising off-stoichiometric metal oxide that undergoes a topotactic perovskite ($δ$ = 0) to brownmillerite ($δ$ = 0.5) transition under electrochemical and thermochemical stimuli, with concomitant variations in its electrical, magnetic, thermal, and optical properties. Recent studies on thin-film cycling in electrochemical devices show incomplete reversibility of this transition, with significant acid-etching serving as a degradation mechanism. While earlier investigations examined the protonation of brownmillerite SrCoO2.5, the thermodynamics of protonation across the perovskite-to-brownmillerite transition remain poorly understood. In this work, we combine density functional theory calculations with predictions from universal machine-learning interatomic potentials to elucidate the energetics and implications of protonation across the transition for La0.5Sr0.5CoO3-$δ$. These calculations reveal negative hydrogen insertion energies and strong competition with oxygen vacancy formation across the transition for a wide range of conditions. The extent of protonation is primarily limited by the availability of Co 3d states to accommodate reduction by inserted hydrogen. Although hydrogen insertion is often thermodynamically favorable within a defect picture, a convex hull analysis of the resulting HyLa0.5Sr0.5CoO3-$δ$ phases reveals them to be unstable against decomposition into hydroxides among other products. This instability increases with hydrogen content and provides a thermodynamic basis for the acid-etching observed during electrochemical cycling. This work advances the fundamental understanding of protonation in La0.5Sr0.5CoO3-$δ$ and contextualizes experimental observations of related materials in the presence of moisture or H2. Brownmillerite ; Protonation ; Perovskite (structure) ; Hydrogen ; Transition metal ; Oxide ; Density functional theory ; Work (physics) Magnetic and transport properties of perovskites and related materials ; High-pressure geophysics and materials ; Solid-state spectroscopy and crystallography
Authors: Obed Issakah | A.M. Kayaba | E. K. A. Asare
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Tuning of the Tensile Property of HDPE/CaCO₃/PKS Composite using Machine Learning Overfitting ; Ultimate tensile strength ; Composite number ; Deformation (meteorology) ; Representation (politics) ; Tensile testing ; Modulus ; Kernel (algebra) Natural Fiber Reinforced Composites ; Calcium Carbonate Crystallization and Inhibition ; Polymer Nanocomposites and Properties
Authors: M. D. Umbharatwala | Manmohan Dass Goel | Marcus Maeder | Steffen Marburg
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-model Investigation of Nano-Composite Armor: Experimental Ballistics, Numerical Modelling, and Phenomenological Predictive Analytics Projectile ; Ballistic limit ; Finite element method ; Residual ; Particle swarm optimization ; Dissipation ; Composite number ; Armour ; Range (aeronautics) ; Empirical modelling High-Velocity Impact and Material Behavior ; Boron and Carbon Nanomaterials Research ; Mechanical Behavior of Composites
Authors: Kazi Abrar Shafin | Md.Alamin Hossain Pappu | Ahnaf Tahmid Abir | Abdul Kuddus | Shinichiro Mouri | Jaker Hossain
Source: Journal of Physics D Applied Physics (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Modelling and Absorption Optimization in Stacked-Absorber Heterojunction Solar Cells Abstract Achieving superior light harvesting in stacked-absorber heterojunction solar cells requires precise electronic and optical alignment between adjacent absorber layers. This necessity stems from the complex, non-linear interdependence among key material parameters including bandgap (Eg), electron affinity (Ea), and effective density of states (NC, NV) that collectively dictate carrier generation, transport, and absorptive efficiency. In this work, we integrate machine-learning (ML) models with conventional SCAPS-1D simulations to identify optimal absorber-layer properties and guide the rational design of stacked architecture. Five ML algorithms are trained for thousands of data combinations followed by parameter optimization for high-Eg top absorbers (L1) and low-Eg bottom absorbers (L2). Explainable AI (SHAP; SHapley Additive exPlanations) analysis reveals critical design rules, highlighting the roles of density of states, free-carrier concentration, and favorable band offsets, specifically, lower NC and higher Na for L2, and the opposite for L1, with minimal VBM/CBM discontinuity (~ ±0.02 eV). Using these ML-derived criteria, we propose high-absorption stacks incorporating ZnSnN2 (ZTN), CdTe and Cu3PSe4 (CPSe) as L1 and MoTe2 as L2, with CdS and WSe2 as window and hole-transport layers. The optimized device structures achieve power-conversion efficiencies, PCEs of 39.77%, 39.49%, and 39.65% with VOC ≈ 1.15 V, JSC ≈ 39 mA cm-2, and FF ≈ 86% under ideal interface conditions. We further discuss the physical origins of the enhanced photocurrent and voltage, along with possible key challenges and practical considerations for experimental realization. Thus, this study demonstrates a data-driven hybrid design framework that advances the development of next-generation high-efficiency stacked-absorber solar cells by identifying optimized material properties and device configurations that surpasses single-junction performance limits. Heterojunction ; Photocurrent ; Absorption (acoustics) ; Key (lock) ; Band gap ; Cadmium telluride photovoltaics ; Ideal (ethics) ; Thin film solar cell Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Chemical and Physical Properties of Materials
Authors: Shuting Gong | Mengdi Wang | Zhipeng Li | Xinle Jiang | Wentian Yang | WenJun Qian | Zhaotang Ding | Yuqi Wang | Kai Fan
Source: Microchemical Journal (article, 2026-03-13)
Field: Chemistry — Analytical Chemistry

Using hyperspectral imaging combined with the diagnosis and recommendation integrated system (DRIS) to diagnose mineral deficiency in tea leaves Hyperspectral imaging ; Principal component analysis ; Chlorosis ; Nutrient deficiency ; Mineral ; Reflectivity Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Xin Kang | Shunhui Liu | Qiaoqiao Shao | Peng Qin | Xiaotong Zhang | Liang Fang | Sheng Liu | Xue-Tao Gan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Specific Identification of Butanol Isomers via MOF-Functionalized on-chip Photonic Waveguide Sensor Arrays Sensitivity (control systems) ; Detection limit ; Selectivity ; Steric effects ; Sensor array ; Waveguide ; Resonator ; Spectral line ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Dare Victor Abere | Sammy A. Ojo | Ojoma Helen Adejo | Grace Modupe Oyatogun
Source: Discover Materials (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Mechanistic advances in reinforcement interface and processing synergies and translation barriers in titanium matrix composites Titanium matrix composites (TMCs) offer low density, high specific strength and good thermal stability, yet their deployment in certification-critical aerospace, automotive, energy and biomedical components remain constrained by incomplete understanding of strength–toughness–durability trade-offs. This work provides a mechanistic, meta-analytical review of reinforced TMCs, spanning ceramic, intermetallic, metallic, nanocarbon and laminate or hybrid architectures, with emphasis on how reinforcement chemistry, interface design and hierarchical microstructure jointly control static and time-dependent performance. A quantitative property landscape is compiled for major reinforcement families, enabling side-by-side comparison of tensile strength, stiffness, ductility, fracture toughness, fatigue strength, creep resistance and environmental stability, and revealing how ceramic-rich systems occupy a strong-but-brittle corner of design space, whereas laminate, hybrid and nanocarbon-reinforced systems offer more balanced toughness and durability windows. Building on this analysis, the review introduces a tri-coupled framework that treats interface engineering, hierarchical reinforcement architecture and multiscale validation as interdependent design levers for next-generation TMCs. Within this framework, advanced characterization, digital-twin concepts and machine-learning-assisted models are integrated with mechanical testing to generate more predictive process–structure–property maps and to identify microstructural signatures of long-term damage tolerance. The review further outlines key translation barriers—including microstructural reproducibility in advanced processing routes, the lack of standardized multi-environment fatigue and creep protocols, data limitations for mechanistic and data-driven modeling, and sustainability constraints—and proposes an agenda in which targeted durability testing, hetero-deformation-based strengthening models and data-rich digital workflows are systematically combined to accelerate qualification of titanium-based composite systems for reliable, certifiable service in demanding structural applications. Durability ; Interface (matter) ; Creep ; Toughness ; Reinforcement ; Resilience (materials science) ; Workflow ; Delamination (geology) Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Minh Triet Chau | Hyeokjae Kwon | S. H. Yun | Kevin Ferreira | Thi Ha Kyaw | Jack Stephen Baker
Source: Open MIND (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Quantum Neural density functionals in density functional theory Quantum neural functionals (QNFs) are introduced as a hybrid quantum-classical framework to advance density functional theory (DFT). QNFs employ quantum neural networks to capture nonlocal many-body correlations in exchange-correlation functionals. Symmetry-aware inductive biases, including rotational invariance under discrete O(3) subgroups and exact functional constraints, may enhance trainability and generalization while mitigating barren plateaus. Benchmark studies on diatomic molecules demonstrate that QNFs accurately reproduce binding energies and effectively describe strongly correlated regimes, such as H2 dissociation, outperforming both classical neural functionals and naive quantum models. These results highlight QNFs as a scalable and physically grounded approach for next-generation density functional approximations Generalization ; Quantum ; Density functional theory ; Artificial neural network ; Rotational invariance ; Hybrid functional ; Orbital-free density functional theory ; Benchmark (surveying) Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies
Authors: Weimin Zhuang | Bu Yang | Chengguo Qiang
Source: Journal of Materials Science (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Prediction of static tensile properties and reverse design of microstructure for dual-phase steel based on deep learning Microstructure ; Ultimate tensile strength ; Tensile testing ; Finite element method ; Solid mechanics ; Voronoi diagram Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Model Reduction and Neural Networks
Authors: Minh Triet Chau | Hyeokjae Kwon | S. H. Yun | Kevin Ferreira | Thi Ha Kyaw | Jack Stephen Baker
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Quantum Neural density functionals in density functional theory Quantum neural functionals (QNFs) are introduced as a hybrid quantum-classical framework to advance density functional theory (DFT). QNFs employ quantum neural networks to capture nonlocal many-body correlations in exchange-correlation functionals. Symmetry-aware inductive biases, including rotational invariance under discrete O(3) subgroups and exact functional constraints, may enhance trainability and generalization while mitigating barren plateaus. Benchmark studies on diatomic molecules demonstrate that QNFs accurately reproduce binding energies and effectively describe strongly correlated regimes, such as H2 dissociation, outperforming both classical neural functionals and naive quantum models. These results highlight QNFs as a scalable and physically grounded approach for next-generation density functional approximations Generalization ; Quantum ; Density functional theory ; Artificial neural network ; Rotational invariance ; Hybrid functional ; Orbital-free density functional theory ; Benchmark (surveying) Machine Learning in Materials Science ; Quantum many-body systems ; Spectroscopy and Quantum Chemical Studies