jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M.D. Kiran | M.C. Madhu | M. Srinivasamurthy | Rashmi P. Shetty | Nithin Kumar | Abdul Amir H. Kadhum | Aseel Smerat | Rasoul Karimi
Source: Hybrid Advances (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Prediction of Fracture Toughness Parameters of Epoxy-Carbon fabric-CNT composites using Taguchi’s and Machine Learning approach This study investigates the prediction and optimization of fracture toughness parameters of carbon fabric/epoxy composites enhanced with carbon nanotubes (CNTs). The composites were fabricated by hand layup method, and the effects of filler addition, crack length/specimen width ratio (a/W), and temperature were examined. Taguchi’s method was used for experimental optimization, while Random Forest was applied as the predictive modelling technique. Results revealed that fracture toughness improved with CNT addition, reaching optimum performance at 0.5 wt.% CNT, a/W ratio of 0.55, and room temperature (25 °C). The Taguchi analysis confirmed filler addition as the most influential factor, followed by temperature and a/W ratio. ANOVA indicated filler addition had the maximum influence, followed by temperature and a/W ratio. Comparative performance showed that Random Forest outperformed Taguchi’s method, with a lower mean squared error (MSE = 1.156 vs. 3.009), highlighting the superior predictive capability of machine learning. The results contribute to develop the composite materials with better fracture toughness for industrial and structural applications. Fracture toughness ; Taguchi methods ; Toughness ; Composite number ; Random forest ; Fracture (geology) Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Pramita Sharma | Ruhani Ahluwalia | Ibrahim E. Bozkurt | Ugur C. Topkiran | Alyssa Dickens | Alina Valimukhametova | Donatus A. Agbaglo | John Brannon | Benjamin G. Janesko | Yuri M. Strzhemechny | Anton V. Naumov
Source: ACS Applied Nano Materials (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Hormone/Neurotransmitter Detection with NIR-Emissive Graphene Quantum Dots Enabled by AI-Assisted Spectral Recognition Early identification and survey of biological analytes, including neurotransmitters and hormones, are essential for early disease diagnosis, tailored treatment, and immediate physiological evaluation. Dysregulations in dopamine, cortisol, oxytocin, and serotonin are associated with a wide array of illnesses, encompassing neurodegenerative, psychiatric, cardiovascular, and metabolic conditions. In this study, we present an AI-assisted sensing platform utilizing reduced graphene quantum dots (RGQDs) for the multiplex detection of analytes (dopamine, serotonin, cortisol, and oxytocin) by spectral fingerprint identification generated due to analyte-binding-facilitated fluorescence quenching. RGQDs exhibit photostable near-infrared emission, allowing for signal detection through the layers of biological tissue and remarkable in vivo biocompatibility, altogether enabling their utility in implanted or wearable diagnostic systems. In contrast to conventional sensors that depend on molecular specificity, this study employs cross-reactive RGQDs, whose fluorescence is influenced by noncovalent interactions with aforementioned analytes, evaluated via the conformer–rotamer sampling tool (CREST). This theoretical model of interactions of analyte with the RGQD surface computes optimized dimer geometries, binding energies, and π–π stacking configurations, revealing analyte-specific noncovalent binding patterns that enable selective sensing. Artificial intelligence models are developed and trained to recognize spectral variations unique to each analyte. Through unsupervised dimensionality reduction and nonlinear classification (UMAP combined with a random forest model), the system distinguishes analytes using three key spectral features: continuum-removed emission area, first-derivative amplitudes, and wavelet detail energy at level 4, achieving an overall discrimination accuracy of 91.2% and a macro F1 score of 91.5%. This approach facilitates multiplexed nonspecific sensing of four critical hormones/neurotransmitters without the need for designing complex analyte-specific surface groups. This work beneficially merges material innovation with machine learning to provide a cost-efficient, noninvasive, and scalable diagnostic platform, aimed to enhance healthcare accessibility in both clinical and underserved environments. Analyte ; Stacking ; Quantum dot ; Graphene ; Multiplex ; Fluorescence ; Identification (biology) ; Spectral signature Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Mohamed Arame | Issam Meftah Kadmiri | Oussama Elbaraghi | François Bourzeix | Yahya Zennayi | Ilyass Maafa | Abdelghani Chehbouni
Source: Research Square (preprint, 2026-03-27)
Field: Chemistry — Analytical Chemistry

Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning Hyperspectral imaging ; Ascochyta ; Blight ; Precision agriculture ; Plant disease ; Pipeline (software) ; Image processing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Zheng Fang | Chen Yang | Haitao Yu | Haoming Luo | Haitao He | Jiaqing Xie | Zhuo Yang | Jun Xia
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

NMRGym: A Comprehensive Benchmark for Nuclear Magnetic Resonance Based Molecular Structure Elucidation Nuclear Magnetic Resonance (NMR) spectroscopy is the cornerstone of small-molecule structure elucidation. While deep learning has demonstrated significant potential in automating structure elucidation and spectral simulation, current progress is severely impeded by the reliance on synthetic datasets, which introduces significant domain shifts when applied to real-world experimental spectra. Furthermore, the lack of standardized evaluation protocols and rigorous data splitting strategies frequently leads to unfair comparisons and data leakage. To address these challenges, we introduce \textbf{NMRGym}, the largest and most comprehensive standardized dataset and benchmark derived from high-quality experimental NMR data to date. Comprising \textbf{269,999} unique molecules paired with high-fidelity $^1$H and $^{13}$C spectra, NMRGym bridges the critical gap between synthetic approximations and real-world diversity. We implement a strict quality control pipeline and unify data formats to ensure fair comparison. To strictly prevent data leakage, we enforce a scaffold-based split. Additionally, we provide fine-grained peak-atom level annotations to support future usage. Leveraging this resource, we establish a comprehensive evaluation suite covering diverse downstream tasks, including structure elucidation, functional group prediction from NMR, toxicity prediction from NMR, and spectral simulation, benchmarking representative state-of-the-art methodologies. Finally, we release an open-source leadboard with an automated leaderboard to foster community collaboration and standardize future research. The dataset, benchmark and leaderboard are publicly available at \textcolor{blue}{https://AIMS-Lab-HKUSTGZ.github.io/NMRGym/}. Benchmark (surveying) ; Benchmarking ; Pipeline (software) ; Suite ; Nuclear data ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Hanlin Zhang | Ruixuan Zheng | Jing Lin | Jiawu Weng | Lexiang Zhang | Fangfu Ye
Source: Aggregate (article, 2026-03-01)
Field: Chemistry — Spectroscopy

Aerogel Scaffolds: Breathing Life Into Tissue Repair and Companion Diagnostics ABSTRACT Engineering biomaterials that actively interface with and instruct their biological milieu have given rise to a new generation of platforms for tissue repair and companion diagnostics. Among them, aerogel scaffolds, with their ultra‐porous architecture, ultralow density, tunable mechanics, and versatile chemistries, have emerged as transformative candidates capable of emulating and interpreting extracellular environments. This review highlights up‐to‐date advances shaping the landscape of aerogel‐based scaffolds in tissue repair and diagnostic applications. We first summarize emerging fabrication and assembly strategies, including sol–gel processing, freeze‐drying, electrospinning, and 3D printing, which unlock hierarchical morphologies and bioinspired features. The recent implementations of intelligent aerogels for tissue repair and neuroregeneration are then highlighted, together with related applications in bioactive functionalization, immune modulation, wound healing, sustained drug delivery, and moist repair dressings. Meanwhile, we outline aerogel‐based disease diagnosis regarding genotypic physiological cues, focusing on faithfully detecting nucleic acids, tumor biopsy, virus antigen testing of infectious disease, and state‐of‐the‐art demos with innovative signal transduction mechanisms. Data‐driven strategies powered by machine learning are also reviewed, alongside integration into smart wearables for self‐adapting, responsive platforms. Finally, persisting challenges and present perspective of aerogel scaffolds in medicine research and practice are also discussed. Tissue repair ; Wearable computer ; Scaffold ; Aerogel ; Biomedicine ; Tissue engineering ; Decellularization ; Regenerative medicine Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Tissue Engineering and Regenerative Medicine
Authors: Mingxiang Wang | Zhe Li | Min Liu | Ran Tao
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2026-01-01)
Field: Chemistry — Spectroscopy

A ML-Driven Study on Feature Descriptors and Mechanisms for Adsorption of Air Discharge Decomposition Components Insulation materials in air switchgear may degrade due to partial discharge (PD). Characteristic gases generated by PD (such as CO, NO, and NO₂) can be detected by gas sensors. However, existing gas-sensitive material screening relies on trial-and-error methods or costly density functional theory (DFT) calculations. Machine learning (ML)-driven DFT calculations have emerged as a new paradigm in gas-sensitive material research. This approach significantly accelerates the rational design of high-performance gas-sensitive materials by efficiently predicting adsorption behavior and revealing underlying microscopic mechanisms. This study screened 14 key features from 200 sets of adsorption data across 50 literature sources (2018–2024), then compared and optimized six ML models (XGBoost, GBR, etc.) to evaluate the predictive performance of D and E<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad</sub>. Subsequently, SHAP and PDPs analyses were conducted to quantify feature contributions and identify core factors. Research findings indicate that XGBoost achieved prediction R² values of 91.55% for adsorption distance and 89.79% for adsorption energy, significantly outperforming other models (GBR ranked second with R² values of 89.79% and 83.56%, respectively). In terms of generalization ability, XGBoost outperforms GBR in extreme value usage and feature balancing utilization, which is overly dependent on substrate material features. Charge transfer (Qₜ) and dopant atom density (ρ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub>) are the core factors affecting the adsorption behavior, and the SHAP analysis shows that Qₜ has the highest contribution to the model output. Substrate and dopant characteristics contribute equally in the XGBoost model (∼45% each), while the GBR model is overly dependent on substrate characteristics (>60%). Adsorption ; Feature (linguistics) ; Generalization ; Dopant ; Substrate (aquarium) ; Switchgear ; Decomposition ; Density functional theory Aerogels and thermal insulation ; High voltage insulation and dielectric phenomena ; Supercapacitor Materials and Fabrication
Authors: Yannick Hollenweger | Dennis M. Kochmann | Burigede Liu
Source: Journal of the Mechanics and Physics of Solids (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Temperature-aware recurrent neural operator for temperature-Dependent anisotropic plasticity in HCP materials Neural network surrogate models for constitutive laws in computational mechanics have been in use for some time. In plasticity, these models often rely on gated recurrent units (GRUs) or long short-term memory (LSTM) cells, which excel at capturing path-dependent phenomena. However, they suffer from long training times and time-resolution-dependent predictions that extrapolate poorly. Moreover, most existing surrogates for macro-or mesoscopic plasticity handle only relatively simple material behavior. To overcome these limitations, we introduce the Temperature-Aware Recurrent Neural Operator (TRNO), a time-resolution-independent neural architecture. We apply the TRNO to model the temperature-dependent plastic response of polycrystalline magnesium, which shows strong plastic anisotropy and thermal sensitivity. The TRNO achieves high predictive accuracy and generalizes effectively across diverse loading cases, temperatures, and time resolutions. It also outperforms conventional GRU and LSTM models in training efficiency and predictive performance. Finally, we demonstrate multiscale simulations with the TRNO, yielding a speedup of at least three orders of magnitude over traditional constitutive models. Plasticity ; Anisotropy ; Operator (biology) ; Recurrent neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Dhandapani Kuzhandaivel | Weiqiang Chen | Zixiang Weng | Longhui Zheng | Jianlei Wang | Naveen Thirunavukkarasu | Lixin Wu
Source: Chemical Engineering Journal (article, 2026-02-27)
Field: Materials Science — Polymers and Plastics

Adaptive direct ink writing of dual-curable, silica-filled rubber latex composites via a hybrid physics-machine learning framework Compression set ; Natural rubber ; Rheology ; Ultimate tensile strength ; Extrusion ; Vulcanization ; Tear resistance ; Styrene-butadiene ; Compounding Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes ; Polymer composites and self-healing
Authors: Zhiyu He | Zun Liang | Sheng Qian | Yang Yang
Source: Crystal Growth & Design (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Activation-Prefactor Landscapes of Crystal Growth at Planar BCC and FCC Crystal-Melt Interfaces We extend the local-structure-dependent (LSD) framework to planar crystal-melt interfaces (CMIs) of BCC Fe(100) and FCC Ni(100) to quantify how the local liquid structure governs solidification kinetics. Using machine-learning descriptors of atomic softness (S) and steady-state MD simulations, we decompose the kinetic factor into a superposition of Arrhenius channels, k(T,S) = k0(S) exp[−ΔEa(S)/(kBT)], and map the (ΔEa, k0) landscapes across S. Here, ΔEa(S) denotes the softness-resolved activation-energy barrier associated with thermally activated atomic attachment and rearrangement events at the crystal-melt interface within a given local-structure channel. The LSD model, parametrized only at low undercooling, accurately predicts growth rates over broader temperatures. Distinct structure-kinetics fingerprints emerge: in Fe(100), both ΔEa(S) and k0(S) decrease with increasing S (low-barrier + low-prefactor channels), whereas in Ni(100) they increase (high-barrier + high-prefactor channels), with rare low-S channels exhibiting apparent negative activation energies. These contrasting channel families rationalize the different temperature responses of BCC and FCC growth and reveal that kinetic anisotropy originates from the interplay between interface-induced ordering and local packing constraints. The orientation-resolved, softness-decomposed analysis provides a physically transparent route to “liquid-structure engineering”, and the results establish a transferable, data-driven basis for embedding softness statistics into mesoscale models to connect atomic mechanisms with microstructure control. Planar ; Superposition principle ; Kinetic energy ; Arrhenius equation ; Anisotropy ; Crystal growth ; Mesoscale meteorology ; Microstructure ; Activation barrier Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Pungponhavoan Tep | Marc Bernacki
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation Thermal ; Isothermal process ; Grain growth ; Grain size ; Heat equation ; Measure (data warehouse) ; Microstructure ; Deep learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and Mechanical Properties of Steels
Authors: Zhangpeng Wei | Wenli Du | Xin Peng | Feng Qian
Source: ENGINEERING Chemical Engineering (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

AdapGNN: enhancing the explainability of GNN models in molecular properties prediction Noise (video) ; Pattern recognition (psychology) ; Feature (linguistics) ; Stability (learning theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Djamel Ghernaout Ghernaout | Messaouda Boumaaza | Ahmed Belaadi | Noureddine Elboughdiri | Mhamed Benaissa | Ibrahim M. H. Alshaikh
Source: Journal of Natural Fibers (article, 2026-02-23)
Field: Materials Science — Polymers and Plastics

Determining Water Absorption in Hybrid Biocomposites Made from <i>Syagrus romanzoffiana</i> Palm Waste Biomass and Fibers: Optimizing with ANN and RSM Methods The water absorption (WA) behavior of hybrid biocomposites reinforced with Syagrus romanzoffiana palm fibers (SrPFs) and Syagrus romanzoffiana palm waste (biochar, SrPW) shows a nonlinear relationship with immersion time and biochar content. This study explores the use of response surface methodology (RSM) and artificial neural networks (ANN) for multi-objective optimization and predictive modeling of this behavior. A hybrid model combining ANN with a genetic algorithm (GA) and RSM was developed to predict WA over immersion periods from 24 to 720 hours and SrPW contents from 0.5% to 2%. The model was further enhanced using a Multi-Criteria Decision-Making (MCDM) approach with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The ANN-GA model predicted WA with reasonable accuracy (2.72% at 672 hours), closely matching experimental results, as confirmed by validation tests. At 615 hours, RSM optimization resulted in a slightly lower WA (2.68%). These findings demonstrate the reliability of the proposed modeling framework in reducing experimental work and guiding material design. The biocomposite exhibits potential for eco-friendly applications, particularly in the automotive sector. Response surface methodology ; Biocomposite ; Palm ; Absorption of water ; Multi-objective optimization ; Biomass (ecology) ; Artificial neural network ; Genetic algorithm Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials ; Bamboo properties and applications
Authors: José Diogo da Rocha Viana | Moacir Jean Rodrigues | Arthur Cláudio Rodrigues de Souza | Raimundo Marcelino da Silva Neto | Paulo Riceli Vasconcelos Ribeiro | José Carlos Cunha Petrus | Ana Paula Dionísio
Source: Foods (article, 2026-02-09)
Field: Chemistry — Inorganic Chemistry

Green Coconut Biorefinery: RSM and ANN–GA Optimization of Coconut Water Microfiltration with IntegratedTechno-Economic Analysis The coconut water market continues to expand, but industrial supply is constrained by the high perishability of fresh coconut water and the need for stabilization routes that preserve quality. This study optimized crossflow microfiltration of coconut water using a silicon carbide (SiC) ceramic membrane, high permeability, chemical/thermal robustness, and cleanability, and assessed the techno-economic feasibility of a green coconut biorefinery producing microfiltered coconut water and coconut pulp. Pressure and temperature were modeled and optimized using a face-centered design (FCD) and artificial neural networks coupled with a genetic algorithm (ANN-GA), considering permeate flux and fouling index (<i>p</i> < 0.05). Both approaches converged to the same operating point, and experimental validation at 75 kPa and 30 °C achieved 605.32 ± 15.34 L h<sup>-1</sup> m<sup>-2</sup> and 82.79 ± 1.35% at VRR = 1. Sample-level fit statistics favored ANN (higher <i>R</i><sup>2</sup> and lower sample-level errors), whereas condition-wise grouped cross-validation (leave-one-condition-out) indicated higher predictivity and lower <i>RMSE<sub>CV</sub></i> for the quadratic FCD/RSM models across experimental conditions, highlighting response-dependent generalization within the investigated domain. Fouling analysis indicated concentration polarization as the main resistance contribution and a flux-decline behavior best described by the intermediate blocking mechanism. A SuperPro Designer<sup>®</sup> simulation over a 20-year project life indicated economic feasibility under baseline assumptions (Internal rate of return-IRR = 23.80%, Net present value-NPV = US$733,761, payback = 2.96 years), with profitability remaining attractive under ±10% selling-price variation. Overall, the process optimization and modeling outcomes align with the economic case, reinforcing the potential of this biorefinery concept for industrial deployment. Fouling ; Microfiltration ; Biorefinery ; Response surface methodology ; Payback period ; Water use ; Net present value Coconut Research and Applications ; Membrane Separation Technologies ; Solar-Powered Water Purification Methods