jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John Shiny J | Haranya S | A Sameeha
Source:  (article, 2025-10-15)
Field: Chemistry — Analytical Chemistry

Identification of Afatoxin Adulterants in Pistachios using Deep Learning Approaches Food adulteration threatens public health and compromises the integrity of the food supply chain, especially where resources are scarce or population is dense. Despite analytical methods for detecting adulterants being very accurate, they tend to involve sophisticated chemical processes that require considerable time and manpower. The current research presents a non-invasive and streamlined means of pistachio adulteration detection through the fusion of deep learning and hyperspectral grayscale imaging. To mitigate redundancy and extract the most important patterns out of hyperspectral data, the approach uses Principal Component Analysis (PCA) to extract low-dimensional spectral features. These features, along with the spatial and spectral properties of hyperspectral image cubes, are subsequently used as input to a hybrid deep neural network consisting of 3D Convolutional Neural Networks (3D-CNN) and Residual Networks (ResNet). The 3D-CNN layer exploits the spatial-spectral nature of the image cubes, while the ResNet layer captures deeper abstract representations from the spectral sequences. The integrated 3D CNN-ResNet model performs well in the classification task with high accuracy to differentiate between adulterated and genuine pistachios. The model achieved excellent performance at the identification of aflatoxin contamination, providing a scalable, real-time, and non-destructive technology suitable for food safety assurance, regulatory surveillance, and industrial manufacturing. The findings emphasize that the proposed system is a highly promising innovation for contemporary food quality control, with significant potential to detect even faint adulteration patterns reliably. Hyperspectral imaging ; Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network ; Redundancy (engineering) ; Residual ; Identification (biology) Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Edible Oils Quality and Analysis
Authors: Daniel Hedman
Source: arXiv (Cornell University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Vapor-solid-solid growth of single-walled carbon nanotubes Single-walled carbon nanotubes (SWCNTs) are promising for nanoscale electronics and photonics, but practical deployment requires chirality control. Most catalytic chemical vapor deposition (CCVD) growth of SWCNTs proceeds on liquid metal nanoparticles via a vapor-liquid-solid (VLS) mechanism and yields broad chirality distributions, whereas improved selectivity has been reported for high-melting-point crystalline catalysts, suggesting vapor-solid-solid (VSS) growth. However, the atomistic mechanism and kinetic of VSS SWCNT growth remain unclear. Here it is shown, using machine-learning interatomic potential-driven molecular dynamics on rhenium nanoparticles, that VSS growth is diffusion-limited and governed by facet-dependent surface carbon transport coupled to carbon-driven facet reconfiguration without catalyst melting. Surface diffusion is up to $\sim 50\times$ slower than carbon diffusion in liquid iron, imposing a strict upper bound on sustainable carbon supply and producing a narrow growth window: insufficient transport drives carbon accumulation and multiple nucleation, whereas higher temperatures favor graphitic encapsulation. In contrast to defect-free VLS growth, defects persist, indicating slow defect healing, and equilibrium simulations reveal suppressed edge configurational entropy with stabilization of zigzag-rich, Klein-decorated edges. Together, these results establish facet evolution and surface diffusion as joint regulators of diffusion-limited VSS growth and motivate stringent control of temperature and carbon supply. Carbon nanotube ; Surface diffusion ; Carbon fibers ; Chemical vapor deposition ; Catalysis ; Molecular dynamics ; Diffusion ; Chirality (physics) ; Nanoparticle Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Yannic Rath | Massimo Bortone | George H. Booth
Source: ArXiv.org (preprint, 2025-08-29)
Field: Materials Science — Materials Chemistry

Bayesian perspectives for quantum states and application to ab initio quantum chemistry The quantum many-electron problem is not just at the heart of condensed matter phenomena, but also essential for first-principles simulation of chemical phenomena. Strong correlation in chemical systems are prevalent and present a formidable challenge in the simulation of these systems, while predictive phenomena in this domain often also requires a demanding level of accuracy to inform chemical behavior. Efficient representations of the many-electron states of chemical systems are therefore also being inspired by machine learning principles to provide an alternative to established approaches. In this chapter, we review recent progress in this endeavor for quantum chemical problems represented in second quantization, and the particular challenges present in this field. In particular, we focus on the application of Gaussian Process States emerging from efficient representations of the many-body wavefunction with rigorous Bayesian modeling frameworks, allowing for the unification of multiple paradigms under a common umbrella. We show how such models (and other representations derived from machine learning) can be used as novel tools to compute ab initio chemical properties, while in turn also informing the design of machine learning models to extract correlation patterns in classical data. Ab initio ; Quantum ; Unification ; Gaussian process ; Wave function ; Bayesian probability ; Quantum chemical Machine Learning in Materials Science ; Quantum many-body systems ; Gaussian Processes and Bayesian Inference
Authors: Y. Zeng | Wenhao Xie | Wei Cao | Tan Peng | Yue Hou | Ziyu Wang | Jing Shi
Source: ArXiv.org (preprint, 2025-04-11)
Field: Materials Science — Materials Chemistry

Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In this work, we present a deep learning model capable of accurately predicting thermoelectric properties of doped materials directly from their chemical formulas, achieving state-of-the-art performance. To enhance interpretability, we further incorporate sensitivity analysis techniques to elucidate how physical descriptors affect the thermoelectric figure of merit (zT). Moreover, we establish a coupled framework that integrates a surrogate model with a multi-objective genetic algorithm to efficiently explore the vast compositional space for high-performance candidates. Experimental validation confirms the discovery of a novel thermoelectric material with superior $zT$ values in the medium-temperature regime. Thermoelectric effect ; Thermoelectric materials ; Nonlinear system ; Figure of merit ; Doping ; Sensitivity (control systems) ; Thermoelectric generator ; Material properties Advanced Thermoelectric Materials and Devices
Authors: Meihui Liu (7531946) | Wei Wang (17594) | Peifeng Su (2110915) | Jiajun Ren (1826458) | Qian Peng (74762)
Source: Figshare (article, 2025-08-19)
Field: Chemistry — Organic Chemistry

A MolecularDescriptor for Characterizing the Strengthof Through-Space Conjugation for Luminescent Property in Organic Materials Noncovalent through-space conjugation (TSC), a unique property of π-charge delocalization, has emerged as a powerful strategy for modulating the optoelectronic properties of organic materials. However, the quantitative relationship among the molecular structure, the strength of TSC, and optoelectronic properties has not yet been established. Here, using typical conjugated aromatic compounds as model systems, it is revealed that TSC arises from the interaction of π-orbitals, and a molecular descriptor, composed of geometric parameters, is proposed to characterize the strength of TSC. This descriptor exhibits a strong linear correlation with the orbital splitting energy of symmetric and antisymmetric π-orbitals, achieving a linear fit coefficient (<i>R</i><sup>2</sup>) as high as 0.935. More importantly, <i>A</i> shows a strong positive correlation with the spectral red-shift of organic materials, suggesting that TSC can effectively tune the luminescent properties of organic materials. The descriptor is helpful for the rational design and machine-learning-based high-throughput screening of advanced organic luminescent materials. Luminescence ; Antisymmetric relation ; Conjugated system ; Property (philosophy) ; Spectral properties ; Linear relationship ; Correlation coefficient ; Oscillator strength Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Zheng Ma | Hai Lin | Changfei Huang | Chengcheng Tian | Yayun Zhang
Source: ACS Sustainable Chemistry & Engineering (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Low Temperature Pyrolysis of Plastics Based on Small Sample Machine Learning: Product Prediction, Optimization Conditions, and Model Interpretability Analysis This study proposes a novel data-driven strategy that integrates machine learning (ML) with low-temperature catalytic pyrolysis to optimize the selection of plastic feedstocks and catalyst designs. Using a custom-built experimental data set based on polyethylene (PE) model compounds and zeolite ZSM-5 catalysts, we trained and evaluated three ML models─TabPFN, CatBoost, and XGBoost─on a small-sample data set of 105 orthogonally designed experiments. TabPFN outperformed conventional gradient boosting models in both classification and regression tasks, achieving an R2 of 0.982 and RMSE of 4.37 on test data, with strong generalization capacity. SHAP analysis and Pearson correlation jointly revealed that temperature and the Si/Al ratio were the dominant factors influencing C2–C6 olefins selectivity, with the latter exhibiting an inverted U-shaped influence on olefin selectivity. The trained TabPFN model successfully identified optimal reaction conditions, which were experimentally validated with a high C2–C6 olefin yield of 82.5% and minimal coke formation. This work demonstrates the potential of ML-based small-sample frameworks for rapidly optimizing catalytic systems, and the analysis of SHAP interpretation provides possible insights into the reaction pathway. Interpretability ; Olefin fiber ; Yield (engineering) ; Pyrolysis ; Boosting (machine learning) ; Test set ; Generalization ; Polyethylene ; Feature selection ; Regression analysis Machine Learning in Materials Science ; Thermochemical Biomass Conversion Processes ; Catalysis and Oxidation Reactions
Authors: Souvik Mitra (8043785) | Clara Zens (11534210) | Stephan Kupfer (1609666) | Andreas Heuer (295921) | Diddo Diddens (1852390)
Source: Figshare (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Machine LearningModels for Predicting ElectronicCoupling in TEMPO/TEMPO<sup>+</sup> Systems Organic radical batteries (ORBs) based on the TEMPO (2,2,6,6-tetramethylpiperidin-1-yl oxyl) radical have drawn significant attention, owing to their unique redox properties. A key factor influencing ORB’s redox properties, i.e., the kinetics of the electron transfer between the TEMPO–TEMPO<sup>+</sup> pairs, is the communication between the underlying redox-active states as given by the electronic coupling. However, due to the complex structure, predicting accurate electronic couplings for these pairs is computationally expensive and challenging. In this study, we introduce a machine learning (ML) workflow to predict the electronic coupling for TEMPO–TEMPO<sup>+</sup> pairs simply by their specific geometric orientations. For the ML models, a data set was generated through time-dependent density functional theory calculations coupled with the Generalized Mulliken Hush method to assess energies, (transition-)dipole moment, and couplings for specific TEMPO–TEMPO<sup>+</sup> configurations obtained from classical molecular dynamics simulations that mimic a realistic electrolyte environment. Our results demonstrate that, among the three ML modelslinear regression, kernel ridge regression (KRR), and random forestthe KRR model, with its kernel-based approach, most effectively handles the correlated orientation-based descriptors. Moreover, our SHapley Additive exPlanations (SHAP)-based feature importance analysis indicates that multiple orientation factors jointly influence electronic coupling, rather than any single distance or angle dominating, with each parameter’s impact strongly contingent on the values of the others which is in agreement with previous studies computational by the consortium. Coupling (piping) ; Representation (politics) ; Workflow ; Kernel (algebra) ; Electronic structure ; Feature (linguistics) ; Set (abstract data type) ; Density functional theory ; Outlier Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Advanced battery technologies research
Authors: Marlen Neubert | Patrick Reiser | Frauke Gräter | Pascal Friederich
Source: PubMed (preprint, 2025-08-01)
Field: Materials Science — Materials Chemistry

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides Hydrogen atom transfer (HAT) reactions are essential in many biological processes, such as radical migration in damaged proteins, but their mechanistic pathways remain incompletely understood. Simulating HAT processes is challenging due to the conflicting requirements of quantum chemical accuracy and biologically relevant time and length scales; thus, neither classical force fields nor DFT-based molecular dynamics simulations are applicable. Machine-learned potentials offer an alternative, with the ability to learn potential energy surfaces (PESs) that capture reactions and transitions with near-quantum accuracy. However, training such models to generalize across diverse HAT configurations-especially at radical positions in proteins-requires tailored data generation strategies and careful model selection. In this work, we systematically generate HAT reaction configurations in peptides to build large datasets using semiempirical methods as well as DFT. We benchmark three atomistic machine-learned potential architectures, SchNet, Allegro, and MACE, on their ability to learn HAT potential energy surfaces and indirectly predict reaction barriers through direct energy predictions. MACE consistently outperforms the other models in energy, force, and reaction barrier prediction accuracy, achieving a mean absolute error of 1.13 kcal mol<sup>-1</sup> on DFT barrier predictions. Short molecular dynamics simulations indicate that the learned potential is numerically stable at finite temperature and can sustain reactive HAT sampling under moderate biasing, serving as a feasibility check for downstream simulation workflows. We show that the trained MACE potential generalizes well beyond our training data by performing out-of-distribution evaluations and analysis of HAT barriers in collagen I snapshots. This level of accuracy can enable integration of machine-learning-based barrier predictions into large-scale simulation workflows to compute reaction rates from predicted barriers, advancing the mechanistic understanding of HAT and radical migration in peptides. We analyze scaling laws, model transferability, and cost-performance trade-offs, and outline strategies for improvement through the combination of machine-learned potentials with transition state search algorithms and active learning. The presented approach is generalizable to other biomolecular systems, offering a method toward quantum-accurate barrier predictions of chemical reactivity in complex biological environments. Hydrogen atom ; Scaling ; Potential energy ; Molecular dynamics ; Benchmark (surveying) ; Hydrogen ; Potential energy surface ; Transition state ; Atom (system on chip) ; Quantum Machine Learning in Materials Science ; Protein Structure and Dynamics ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Dandan Shen | Yao Sun
Source: International Dental Journal (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Nanozyme Hydrogel Promotes Bone Regeneration Via Cilia Regulation Bone tissue damage and defects are common medical issues in both dentistry and general medicine. Globally, approximately 2.2 million bone graft surgeries are performed annually, highlighting the urgent clinical need for efficient and safe bone repair and regeneration materials. During bone repair, cells perceive the extracellular matrix microenvironment through cilia, and the absence of cilia significantly impairs this process. However, whether regulating cilia to enhance cellular sensing can promote bone regeneration remains unclear. This study aims to construct a multifunctional antioxidant hydrogel platform to modulate cilia occurrence. We constructed a multifunctional biomimetic extracellular matrix (ECM) platform using hydrogel materials. Leveraging mineralization-promoting short peptides screened by our team via AI deep learning, we employed chemical modification to graft hydrophobic heads, thereby altering their hydrophilic properties and enhancing compatibility with water-insoluble drugs. This enabled the development of a pH-responsive supramolecular hydrogel. To further improve mechanical strength, chondroitin sulfate grafted with double bonds was utilized as a crosslinker alongside small-molecule monomers. Under UV initiation, nanozymes facilitated gelation, forming a dual-network structure integrating supramolecular and polymeric frameworks. This study developed a cilia-regulating hydrogel system via nanozyme-photoinitiated fabrication. The hydrogel exhibits excellent antioxidant properties, enhances cilia occurrence, and promotes osteogenesis both in vitro and in vivo. This study elucidates the hydrogel’s unique gelation mechanism and explores its biological utility, demonstrating cilia-mediated enhancement of bone regeneration. Cilium ; Extracellular matrix ; Regeneration (biology) ; Chondroitin sulfate ; Self-healing hydrogels ; Bone formation ; Bone tissue ; Bone healing Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Supramolecular Self-Assembly in Materials
Authors: Mehrab Mahdian | Tamás Stummer | Norman Sepsik | Ferenc Ender | Diána Balogh‐Weiser | Tamás Párdy
Source:  (preprint, 2025-12-01)
Field: Materials Science — Biomaterials

Cogni-e-SpinDB 1.0: Open Dataset of Electrospinning Parameter Configurations and Resultant Nanofiber Morphologies Electrospinning is a versatile technique for producing nanofibers by elongating and depositing a polymer precursor solution in an electrostatic field. The formation process and resulting nanofiber quality are governed by process, environmental, and solution parameters, which need extensive fine-tuning. Such optimization is largely driven by experimental data, typically produced independently by individual laboratories. To date, few openly available datasets of adequate size exist to support modeling or to reference parameter sets that yield stable nanofiber formation via electrospinning. Here, we present an openly accessible dataset containing 809 experimental records, each comprising electrospinning process parameters and corresponding nanofiber morphologies-the first dataset of this scale. To assess the dataset's reliability and utility, we verified data integrity against source publications and trained a machine learning model as a proof of concept. The dataset is intended to support regression analyses and machine learning approaches for predicting stable electrospinning parameter configurations. We also developed a companion web platform to enable live dataset contributions and interactive exploration, fostering continuous community-driven updates. Electrospinning ; Nanofiber ; Process (computing) ; Reliability (semiconductor) ; Yield (engineering) ; Quality (philosophy) ; Polymer Electrospun Nanofibers in Biomedical Applications ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Théo Cavignac | Jonathan Schmidt | Pierre-Paul De Breuck | Antoine Loew | Tiago F. T. Cerqueira | Hai‐Chen Wang | Anton Bochkarev | Yury Lysogorskiy | A. Romero | Ralf Drautz | Silvana Botti | Miguel A. L. Marques
Source: ArXiv.org (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

AI-Driven Expansion and Application of the Alexandria Database We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses. Workflow ; Convex hull ; Scaling ; Benchmark (surveying) ; Stability (learning theory) ; Graph ; Generative grammar Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Computational Drug Discovery Methods