jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Achintya Haldar | Ali Hamze | Nikhil Sivadas | Yongwoo Shin
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling We introduce GEARS H, a state-of-the-art machine-learning Hamiltonian framework for large-scale electronic structure simulations. Using GEARS H, we present a statistical analysis of the hole concentration induced in defective $\mathrm{WSe}_2$ interfaced with Ni-doped amorphous $\mathrm{HfO}_2$ as a function of the Ni doping rate, system density, and Se vacancy rate in 72 systems ranging from 3326 to 4160 atoms-a quantity and scale of interface electronic structure calculation beyond the reach of conventional density functional theory codes and other machine-learning-based methods. We further demonstrate the versatility of our architecture by training models for a molecular system, 2D materials with and without defects, solid solution crystals, and bulk amorphous systems with covalent and ionic bonds. The mean absolute error of the inferred Hamiltonian matrix elements from the validation set is below 2.4 meV for all of these models. GEARS H outperforms other proposed machine-learning Hamiltonian frameworks, and our results indicate that machine-learning Hamiltonian methods, starting with GEARS H, are now production-ready techniques for DFT-accuracy device-scale simulation. Hamiltonian (control theory) ; Amorphous solid ; Vacancy defect ; Ionic bonding ; Doping ; Hamiltonian system ; Hamiltonian matrix Machine Learning in Materials Science ; 2D Materials and Applications ; Quantum many-body systems
Authors: Takahiro Kono | Souta Miyamoto | Taichi Masuda | Katsuaki Tanabe
Source: Computational Materials Science (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Improving DFT-predicted band gaps by symbolic regression for semiconductor materials discovery Leverage (statistics) ; Band gap ; Semiconductor ; Symbolic regression ; Workflow ; Wide-bandgap semiconductor ; Function (biology) ; Ternary operation ; Density functional theory Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Heusler alloys: electronic and magnetic properties
Authors: I Infanta Mary Priya | Siddharth Anand | Aravindan R. Bishwakarma | Uma M | S. Prabhu | Moola Mohan Reddy
Source: Processes (article, 2025-08-14)
Field: Materials Science — Polymers and Plastics

Design and Evaluation of LLDPE/Epoxy Composite Tiles with YOLOv8-Based Defect Detection for Flooring Applications With the increasing demand for sustainable and cost-effective alternatives in the construction industry, polymer composites have emerged as a promising solution. This study focuses on the development of innovative composite tiles using Linear Low-Density Polyethylene (LLDPE) powder blended with epoxy resin and a hardener as a green substitute for conventional ceramic and cement tiles. LLDPE is recognized for its flexibility, durability, and chemical resistance, making it an effective filler within the epoxy matrix. To optimize its material properties, composite samples were fabricated using three different LLDPE-to-epoxy ratios: 30:70, 40:60, and 50:50. Flexural strength testing revealed that while the 50:50 blend achieved the highest maximum value (29.887 MPa), it also exhibited significant variability, reducing its reliability for practical applications. In contrast, the 40:60 ratio demonstrated more consistent and repeatable flexural strength, ranging from 16 to 20 MPa, which is ideal for flooring applications where mechanical performance under repeated loading is critical. Scanning Electron Microscopy (SEM) images confirmed uniform filler dispersion in the 40:60 mix, further supporting its mechanical consistency. The 30:70 composition showed irregular and erratic behaviour, with values ranging from 11.596 to 25.765 MPa, indicating poor dispersion and increased brittleness. To complement the development of the materials, deep learning techniques were employed for real-time defect detection in the manufactured tiles. Utilizing the YOLOv8 (You Only Look Once version 8) algorithm, this study implemented an automated, vision-based surface monitoring system capable of identifying surface deterioration and defects. A dataset comprising over 100 annotated images was prepared, featuring various surface defects such as cracks, craters, glaze detachment, and tile lacunae, alongside defect-free samples. The integration of machine learning not only enhances quality control in the production process but also offers a scalable solution for defect detection in large-scale manufacturing environments. This research demonstrates a dual approach to material innovation and intelligent defect detection to improve the performance and quality assurance of composite tiles, contributing to sustainable construction practices. Epoxy ; Linear low-density polyethylene ; Composite number ; Composite material ; Materials science ; Polymer Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; High voltage insulation and dielectric phenomena
Authors: Cheng Su | Tongqing Lu | Shuiqing Li | Rui Zhou | Huan Liu | Qing Wang | Yi Zhang | Laifei Cheng | Xingang Luan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Preparation, Structure and Properties of C/Sicnw-Zrc Composites Composite material ; Materials science Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: A.H. Syaeful Anwar | Ropiudin Ropiudin | Kavadya Syska | Siswantoro Siswantoro
Source: Journal of Agricultural and Biosystem Engineering Research (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Nanotechnology holds immense potential in enhancing efficiency and sustainability across the environmental, energy, and agricultural sectors. Plant-based nanoparticle synthesis (phytosynthesis) has emerged as an eco-friendly method that replaces toxic chemicals with natural metabolites, producing nanoparticles that are stable, uniform, and cost-effective. The application of nanotechnology in phytoremediation leverages the ability of hyperaccumulator plants to absorb pollutants and transform them into high-value materials. Meanwhile, in bioenergy, nanoparticles act as catalysts to accelerate lignocellulose conversion into bioethanol and enhance biogas production. In agriculture, nano-fertilizers and nano-pesticides based on metal oxides, such as ZnO and CuO, improve nutrient efficiency and plant protection against pathogens, supported by carbon nanotubes (CNTs) that enhance soil structure and water absorption. Nevertheless, challenges such as nanoparticle toxicity to the environment, bioaccumulation in the food chain, and disruption of soil microbes require serious attention. Additionally, data-driven approaches like artificial intelligence (AI) and machine learning (ML) offer solutions to optimize nanoparticle design, improve stability, and minimize toxic impacts. Integrating nanotechnology with these approaches is believed to accelerate innovations in various sectors while supporting global ecosystem sustainability. Political science Nanoparticles: synthesis and applications ; Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications
Authors: Zige Liu | Siqin Cao | Bojun Liu | Xuhui Huang
Source: ChemRxiv (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Revealing Competing Kinetic Pathways in Amphiphilic Pt(II) Complex Self-Assembly via Deep Learning with Graph Neural Networks Supramolecular assembly is a versatile bottom-up strategy for creating advanced functional materials. Metallic platinum-platinum (Pt···Pt) interactions provide a distinctive driving force for supramolecular assembly due to their strong, directional, and long-range nature. Despite their importance, the microscopic dynamics underlying the self-assembly of Pt(II) complexes remain challenging to probe experimentally. Molecular dynamics (MD) simulations can capture these processes at atomic resolution, but extracting kinetic pathways is complicated by the indistinguishability and permutation of identical monomers within self-assembled structures. In this study, we employ GraphVAMPnet, a deep learning framework based on graph neural networks (GNN), on extensive MD simulations of amphiphilic PtB complexes during the early stage of self-assembly. GraphVAMPnet inherently accounts for permutational, rotational, and translational invariance, making it well suited for analyzing self-assembly dynamics. Our analysis reveals three slow collective variables (CVs) that govern PtB self-assembly. The slowest mode (CV1) separates two distinct kinetic growth routes: an incremental growth mechanism, in which single monomers join existing aggregates with predominantly anti-parallel packing (CV3), and a hopping growth mechanism, in which clusters of smaller size merge via heterogeneous collisions, yielding a mix of anti-parallel and parallel packing arrangements (CV2). Further energetic analysis indicates that incremental growth is favored, potentially leading to the well-ordered nanosheet morphologies observed experimentally. Our findings provide molecular-level insight into PtB self-assembly pathways and showcase the capability of GraphVAMPnet in dissecting the complex dynamics of supramolecular assembly. Amphiphile ; Deep learning ; Self-assembly ; Graph ; Artificial neural network ; Deep neural networks ; Chemistry ; Kinetic energy ; Artificial intelligence ; Computer science ; Nanotechnology ; Materials science ; Theoretical computer science ; Organic chemistry ; Physics ; Copolymer Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Surface Chemistry and Catalysis
Authors: Maxim Polomoshnov | Nitheesh M. Nair | Gerardo Hernandez‐Sosa | Markus Reischl
Source: Research Square (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Bidirectional process prediction in the laser-induced-graphene production using blackbox deep learning Process (computing) ; Automation ; Deep learning ; Artificial neural network ; Production (economics) ; Product (mathematics) ; Resource (disambiguation) ; Range (aeronautics) Machine Learning in Materials Science ; Laser Material Processing Techniques ; Graphene research and applications
Authors: Nurkanti Mia | MA Hashem | Han‐Sul Yang | Jin‐Kyu Seo
Source: Food Control (article, 2025-12-15)
Field: Chemistry — Analytical Chemistry

AI-driven rapid non-destructive authentication of fresh and frozen meat from multiple species using NIR spectroscopy with reference to physicochemical and bioactive markers Principal component analysis ; Chemometrics ; Smoothing ; Spectroscopy ; Artificial neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Hiroto Okuyama (7511687) | Yuuki Sugawara (6293213) | Takeo Yamaguchi (1556080)
Source: Figshare (article, 2025-09-05)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative Comparisonof Protein Adsorption on PolyethyleneGlycol and Zwitterionic Brushes: A Machine Learning Approach Endowing a material surface with antifouling property using hydrophilic polymer brushes has attracted considerable attention in the design of devices such as water treatment membranes and medical devices. To design an ideal antifouling surface, a comprehensive understanding of protein adsorption on the polymer brush is essential; however, research in this regard is limited because of the complexity of the fouling phenomenon. In this study, a machine learning (ML) approach was employed to evaluate the protein adsorption behavior on a nonionic polyethylene glycol (PEG) brush. The ML model was constructed including the polymer density and molecular weight characteristics of the brush, enabling the prediction of the desired PEG brush state in a specific aqueous environment. The obtained results were then compared with the predictions from the previously constructed data set for a zwitterionic brush, revealing the differences in adsorption properties between the ionic and nonionic brushes. This approach provides a platform for the selection of suitable polymer brushes for practical application in aqueous environments. Polyethylene glycol ; Polymer ; Adsorption ; Protein adsorption ; Biofouling ; Aqueous solution ; PEG ratio ; Polymer brush Polymer Surface Interaction Studies ; Membrane Separation Technologies ; Hydrogels: synthesis, properties, applications
Authors: Wenwen Li | Nontawat Charoenphakdee | Yong‐Bin Zhuang | Ryosuke Okuno | Yuta Tsuboi | So Takamoto | Junichi Ishida | Ju Li
Source: Research Square (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale Scalability ; Transferability ; Scale (ratio) ; Distillation ; Ab initio ; Density functional theory ; Extrapolation ; Bridge (graph theory) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: H. Gittins | Graeme M. Day
Source: ChemRxiv (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Can machine learning predict the space group preference of organic molecules? Chemical space ; Random forest ; Space (punctuation) ; Artificial neural network ; Group (periodic table) ; Limit (mathematics) ; Ambiguity ; Set (abstract data type) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Martin Priessner | Richard J. Lewis | Isak Lemurell | Magnus Johansson | Jonathan M. Goodman | Jon Paul Janet | Anna Tomberg
Source: Angewandte Chemie (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Advancing Structure Elucidation with a Flexible Multi‐Spectral AI Model Abstract Validating chemical synthesis success requires confirming the desired product using various analytical techniques. While spectroscopic data collection is increasingly automated, interpreting results remains a major bottleneck, often requiring expert input. With advances in laboratory automation and high‐throughput synthesis, this challenge is expected to intensify. We introduce the MultiModalSpectralTransformer (MMST), a machine learning method that predicts chemical structures directly from diverse spectral data (NMR, IR, and MS). Trained on 4 million simulated compounds, MMST achieves 72% and 80% as top‐1 and top‐3 accuracy, respectively. To address out‐of‐distribution challenges, we implemented an active learning improvement cycle that generates molecules in similar chemical spaces, enabling the model to adapt to chemical structures beyond its original training data. We demonstrate MMST's capabilities through comprehensive benchmarking across diverse molecular weight ranges and chemical spaces. Notably, despite training solely on simulated data, MMST demonstrates good performance with experimental spectra. This research represents a significant advancement in automated structure elucidation, offering a powerful and adaptable tool that bridges the gap between simulated and real‐world data. Benchmarking ; Automation ; Data collection ; Chemical process ; Product (mathematics) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Zixuan Wei (6685700) | Qigang Zhong (5018180) | Jinbo Pan (1671757) | Fang Han Lim (20819104) | Lifeng Chi (396288) | Shixuan Du (1511089)
Source: Figshare (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Two-Stage Machine LearningFramework for AccurateDiscrimination of Isomers and Very-Similar Molecules on Surfaces The accurate detection and discrimination of on-surface organic isomers and very similar molecules are crucial for monitoring chemical reaction processes and analyzing various reaction mechanisms and molecular properties. Despite its importance, nano- and surface science communities still lack an efficient, robust, precise, and automated detection approach for on-surface isomers and highly similar molecules. Here, we present ReSTOLO, a convolution neural network (CNN)-based framework for precise detection and identification of multiple types of sparsely distributed molecules on surfaces, particularly designed for scanning tunneling microscopy (STM) images containing numerous molecules with analogous features. To address challenges arising from molecular shape and size similarities, we implemented a two-stage framework comprising two CNN models: YOLO v5.m was used for molecular localization, and ResNet-101 for classification. The framework optimally harnesses the advantages of both models by applying a box normalization connection. We demonstrated the framework’s effectiveness by applying it to analyze a surface reaction process involving six molecules with nearly identical STM signatures. The training process employed an STM image database of single molecules augmented with physical and experimental tools constructed using standardized image boxes. This two-stage approach achieved approximately ∼20% improvements in performance metrics, including precision, recall, and accuracy, compared to conventional frameworks. The framework exhibits robust capabilities in automatically and efficiently pinpointing and discriminating between molecular species with similar configurations in complex surface reactions. This automated molecular discriminator represents a significant advance in facilitating STM tip-manipulated chemical reactions on surfaces. Discriminator ; Convolutional neural network ; Process (computing) ; Scanning tunneling microscope ; Molecule ; Artificial neural network ; Identification (biology) ; Pattern recognition (psychology) ; Surface (topology) Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Renquan Lv (21259155) | Weiwei Han (286593) | Zixu Zeng (21259158) | Yi He (408132) | Lecheng Lei (1645309) | Ping Li (31981) | Xingwang Zhang (587405)
Source: Figshare (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Decoding Structure–OpticalProperty Relationshipsin TiO<sub>2</sub> Nanocomposites through Hybrid Features Integration Optical titanium dioxide nanocomposites have a wide range of applications, but ill-defined structure–property relationships pose a challenge to the systematic discovery of functional materials. We designed a TGEML multimodal nanocomposite processing framework to address this challenge. The framework consists of a polymer multimodal featurizer named TGEML-polymer and a nanoparticle-specialized featurizer named TGEML-nano. TGEML-polymer integrates a Transformer-based graph neural network specialized in representing polymer information to capture the chemical semantic sequence and graph information of polymers. TGEML-nano includes processing strategies for nanoparticle dispersion scattering and particle size distribution to provide a comprehensive picture of the nanosystem. We correlated polymer feature vectors with nanosystem parameters to generate hybrid features. This enabled the successful construction of a quantitative “structure–refractive index” mapping. Moreover, the structure–optical property relationships were decoded through a multilevel feature interpretability analysis. In conclusion, TGEML can provide powerful and useful hybrid features for artificial intelligence to accelerate the development and design process of nanocomposites. Interpretability ; Feature (linguistics) ; Graph ; Nanocomposite ; Property (philosophy) ; Hybrid system ; Artificial neural network ; Process (computing) ; Decoding methods Machine Learning in Materials Science ; Polymer Nanocomposites and Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xinna Yu (16870491) | Tianyuan Liu (1347378) | Lili Kong (19204507) | Tianshuo Lan (21197680) | Qifang Sun (15064237) | Fanhua Qu (21197683) | Meichun Liu (9423940) | Jie Chen (5892) | Meizhen Huang (7841399)
Source: Figshare (article, 2025-04-29)
Field: Chemistry — Analytical Chemistry

SpecRecFormer: Deep Learning-Driven Adaptive ComponentIdentification of PAH Mixtures Based on Single-Component Raman Spectra The identification of components in mixed spectra is a fundamental challenge in spectral analysis, complicated by factors such as spectral peak overlap due to structural similarities, shifts in characteristic peaks from molecular interactions, and interferences caused by matrix effects. While deep learning offers robust feature extraction capabilities and notable advantages in addressing these challenges, it still faces significant obstacles, including the limited availability of labeled spectral data for effective training and the difficulty of applying fixed-threshold predictive models to spectra containing uncertain components. This paper established a deep learning model, SpecRecFormer, for the rapid identification of individual components in mixed polycyclic aromatic hydrocarbons (PAHs) based on their Raman spectra. The model integrates a dual-channel convolutional neural network (CNN) for local feature extraction with a Transformer module for global representation. It is trained on a reference database composed of single-component spectra, with simulated mixed spectra generated through data augmentation to expand and diversify the training set. This architecture enables the model to evaluate the similarity between unknown mixed spectra and known single-component references. To further enhance recognition accuracy, an adaptive threshold strategy is introduced, dynamically adjusting decision thresholds based on spectral characteristics to retain only components exceeding the threshold as candidate predictions. Experimental results demonstrate that with training data derived from only four single-component reference spectra, the model generalizes effectively to three real-world PAH data sets, achieving accuracies of 93.75%, 89.21%, and 93.63%, respectively, significantly outperforming conventional neural network models. These findings present an innovative and highly effective approach to mixed spectral analysis, with substantial potential for advancing applications in environmental science and chemical analysis. Pattern recognition (psychology) ; Convolutional neural network ; Artificial neural network ; Feature extraction ; Spectral line ; Deep learning ; Raman spectroscopy ; Spectral signature ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification