jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jakob Blahusch (11356301) | Konstantin S. Jakob (21652173) | Johannes T. Margraf (1707877) | Karsten Reuter (1342152) | Bettina V. Lotsch (1467520)
Source: Figshare (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Predict beforeYou Precipitate: Learning TemplatingEffects in Hybrid Antimony and Bismuth Halides Hybrid organic–inorganic (HOI) antimony and bismuth halides exhibit diverse structural features and have been studied intensely for their promising electronic and optical properties. There are well-explored structure–property relations for these materials. However, a thorough understanding of the synthesis routes and templating effects is lacking, turning their targeted synthesis into an open challenge. In this study, we assemble a literature data set of established HOI material candidates and train an explainable machine learning classification model to explore the templating effects in more detail. With a classification accuracy upward of 70%, our model is effective in predicting HOI structure types based on the reactants and points out several structural and electrostatic design features for the organic cation that influence the inorganic substructure most strongly. We further demonstrate the validity of our classifier on 9 newly synthesized members of this materials class and propose incremental learning routes to expand the model in future research. Bismuth ; Antimony ; Halide ; Substructure ; Class (philosophy) ; Classifier (UML) Machine Learning in Materials Science ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Patrick Giese | Mathias Stokkebye Nissen | Stig Helveg | Jakob Schiøtz
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Visualizing phonon edge states on molybdenum disulphide We employ Molecular Dynamics (MD) simulations to study atom vibrational amplitudes in carbon-supported Molybdenum Disulphide (MoS2) nanoparticles. Enhanced and correlated atom vibrational amplitudes are observed as the nanoparticle edges are approached from the bulk, consistent with recent experimental High-Resolution Transmission Electron Microscopy (HR-TEM) observations by Chen et al (Nature Communications 12, 5007 (2021). Analysis of phonon modes in finite systems explains the experimental observation by low-energy phonon modes confined at the nanoparticle edge, underscoring the need of full MD modeling for accurate HR-TEM image interpretation. Noticeably, we introduce a workflow for training Equivariant Neural Network-based machine learning potentials using limited Density Functional Theory (DFT) calculations. This approach effectively captures both covalent and van der Waals interactions, enabling accurate extrapolations of DFT calculations to larger systems with built-in error estimation. Phonon ; van der Waals force ; Density functional theory ; Molybdenum ; Molecular dynamics ; Atom (system on chip) ; Amplitude ; Molecular vibration Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; 2D Materials and Applications
Authors: Pieter Verding | Danny E. P. Vanpoucke | Yunus Tansu Aksoy | Tobias Corthouts | Maria Rosaria Vetrano | Wim Deferme
Source: Advanced Materials Technologies (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning ABSTRACT This study examines droplet formation in ultrasonic spray coating (USSC) as a function of ink formulation (solvent, polymer, nanoparticles). First, acetone with polyvinylidene fluoride (PVDF) at concentrations from 0 to 4.5 wt.% is used to examine the effect of polymer additions. Additionally, acetone‐based SiO 2 nanofluids (0–10 g/L), are explored. Finally, the combination of both polymer (PVDF) and nanoparticles (SiO 2 ) in acetone is studied. Droplet sizes are measured using Phase Doppler Anemometry under varying atomization power and flow rates. Machine Learning (ML) algorithms are employed to develop droplet size models from key spray parameters, including atomization power, flow rate, polymer concentration, and nanoparticle concentration. The model shows significantly higher accuracy than existing empirical models. The model is further validated on IPA‐based inks with polyethylenimine (PEIE) or ZnO nanoparticles, and on acetone–cellulose acetate formulations, confirming its robustness across diverse ink systems. In addition to revealing the influence of coating parameters on the droplet formation and distribution, obtained both via experimental validation and ML, this study demonstrates that machine learning (ML) can be effectively applied to small experimental datasets, offering a robust framework for optimizing droplet formation and understanding key spray parameters in USSC for complex, unexplored inks enabling novel coating applications. Inkwell ; Coating ; Nanoparticle ; Polymer ; Robustness (evolution) ; Polyvinylidene fluoride ; Polyethylenimine Ultrasound and Cavitation Phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Ultrasound and Hyperthermia Applications
Authors: Shiho Sakuma (22303888) | Shunsaku Yasumura (8519667) | Kenichiro Saita (2361829) | Tetsuya Taketsugu (1271238) | Masaru Ogura (2135035)
Source: Figshare (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Predicting CatalyticActivity for CH<sub>4</sub> Combustionon Pd-Exchanged Zeolite Catalysts Using Automated Reaction Route Mapping While computational predictions of catalytic activity are highly desired, conventional methods have difficulty capturing the complexity of reactions on solid catalyst surfaces. To address this issue, we employed a novel approach combining neural network potentials (NNPs) with automated reaction route mapping to explore reaction mechanisms of CH<sub>4</sub> combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR). The predicted reaction map of CH<sub>4</sub> combustion over Pd<sup>2+</sup> site revealed partially oxidized species such as CH<sub>2</sub>O, HCOOH, and bicarbonate as the potential intermediates toward CO<sub>2</sub> + 2H<sub>2</sub>O. Activation energies (<i>E</i><sub>a</sub>) of the rate-determining step (RDS) were evaluated, revealing the order of <i>E</i><sub>a</sub> as Pd-MOR < Pd-beta < Pd-CHA. A kinetic analysis using rate constant matrix contraction (RCMC) method estimated the catalytic activities of these catalysts. No reaction intermediates with significant lifetimes were observed on Pd-beta and Pd-MOR, whereas stable bicarbonate intermediates were present on Pd-CHA, decreasing the formation rate of CO<sub>2</sub> + 2H<sub>2</sub>O. Kinetic analysis further predicted the pseudo CO<sub>2</sub> formation rate with activity order Pd-MOR > Pd-beta > Pd-CHA, aligning with experimental results. These findings demonstrate the potential of the automated reaction route mapping with NNP for predicting the catalytic activity of solid catalysts, enabling their efficient prescreening and rational design. Catalysis ; Combustion ; Reaction rate constant ; Zeolite ; Reaction rate ; Bicarbonate ; Order of reaction ; Kinetic energy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Ahmad F. Klaib | Rawan Alshami | A. F. Al-Shunnaq | Ghina’a Abu Deiab
Source: IEEE Access (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

A Computer-Based Model for Predicting the Outcomes of Organic Reactions With a Fragment-Based Method Reaction prediction continues to be challenging in the field of organic chemistry and is a prerequisite for synthetic drug design and biosynthesis. This study presents a novel model for predicting the outcomes of a reduction reaction based on machine learning, fragmentation, and reassembling computational methods. The study investigates applying this model to predicting the reactions of small and large drug-like organic compounds. The methodology utilizes the fragmentation method to selectively decompose the compounds into fragments containing the reacting component in a reduction reaction and successfully recognize the reacting functional group. The reacting fragments are then replaced with their corresponding product components and reassembled with the rest of the fragments to generate the predicted reaction outcome. The results show remarkable accuracy, achieving a 100% and 87.93% prediction rate for the small and large drug-like compounds, respectively. The model presents an important contribution to the field of computer-based prediction by offering an efficient and reliable method to predict reaction outcomes. Moreover, the model presents a promising opportunity for higher predictive accuracy as it simplifies reaction prediction by specifically focusing on the reacting functional groups. This model can be expanded to other reactions and a large set of compounds. Field (mathematics) ; Reaction conditions ; Reduction (mathematics) ; Reaction rate ; Fragmentation (computing) ; Set (abstract data type) ; Organic reaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Zhi Zhang | Yongzong Lu | Pengfei Liu
Source: Agriculture (article, 2025-09-27)
Field: Chemistry — Analytical Chemistry

Training Strategy Optimization of a Tea Canopy Dataset for Variety Identification During the Harvest Period Accurate identification of tea plant varieties during the harvest period is a critical prerequisite for developing intelligent multi-variety tea harvesting systems. Different tea varieties exhibit distinct chemical compositions and require specialized processing methods, making varietal purity a key factor in ensuring product quality. However, achieving reliable classification under real-world field conditions is challenging due to variable illumination, complex backgrounds, and subtle phenotypic differences among varieties. To address these challenges, this study constructed a diverse canopy image dataset and systematically evaluated 14 convolutional neural network models through transfer learning. The best-performing model was chosen as a baseline, and a comprehensive optimization of the training strategy was conducted. Experimental analysis demonstrated that the combination of Adamax optimizer, input size of 608 × 608, training and validation sets split ratio of 80:20, learning rate of 0.0001, batch size of 8, and 20 epochs produced the most stable and accurate results. The final optimized model achieved an accuracy of 99.32%, representing a 2.20% improvement over the baseline. This study demonstrates the feasibility of highly accurate tea variety identification from canopy imagery but also provides a transferable deep learning framework and optimized training pipeline for intelligent tea harvesting applications. Canopy ; Convolutional neural network ; Identification (biology) ; Artificial neural network ; Pipeline (software) ; Field (mathematics) ; Variety (cybernetics) ; Training (meteorology) Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Cunlin LONG | Minjuan ZHANG | Jian REN | Haiqing LIU | Yuerong CHEN | Zhijie ZHAO | Hongtao ZHANG | Di LEI
Source: PubMed (article, 2025-11-30)
Field: Materials Science — Biomaterials

[Progress in Application of Recombinant Collagen in Medical Devices]. Recombinant collagen has emerged as a core material for medical device innovation due to its programmable molecular structure, excellent biocompatibility, and scalable production capabilities. This article reviews the application value of recombinant collagen in regenerative medicine, tissue engineering, drug delivery, and wound repair. By combining molecular design strategies with innovative preparation processes, the intrinsic relationship between material properties and clinical efficacy is elucidated. The review explores bottlenecks in clinical translation and highlights emerging trends, such as AI-driven design for predicting material performance <i>via</i> machine learning and 3D bioprinting technology for promoting the regeneration of complex organs. These advancements demonstrate the multi-dimensional application potential of recombinant collagen and drive its efficient translation from laboratory research to clinical applications. Recombinant DNA ; Regenerative medicine ; Regeneration (biology) ; Tissue engineering ; Translation (biology) ; Medical device ; Scalability Collagen: Extraction and Characterization ; 3D Printing in Biomedical Research ; Hydrogels: synthesis, properties, applications
Authors: Akash
K. Ball (21490430) | Gianmarco G. Terrones (14205202) | Shuwen Yue (2884871) | Heather J. Kulik (1373331)
Source: Figshare (article, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

Data-DrivenDiscovery of Water-Stable Metal–OrganicFrameworks with High Water Uptake Capacity Metal–organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identify water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with 12 diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to both be water-stable and have high water uptake. Stability (learning theory) ; Monte Carlo method ; Water model ; Chemical stability ; Water chemistry ; Notice ; Water treatment Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly ; Supramolecular Chemistry and Complexes
Authors: Daria Mysiak | Justyna Werner
Source: Sustainability (article, 2025-12-31)
Field: Chemistry — Analytical Chemistry

An Ecologically Sustainable Approach to Solid-Phase Microextraction Techniques Using Deep Eutectic Solvents Deep eutectic solvents (DESs) have attracted significant attention as eco-friendly and sustainable alternatives to conventional, often toxic, organic solvents. They are easy to synthesize, and their tunable physicochemical properties enable their application in microextraction techniques for a wide range of analytes. However, some DESs may exhibit thermal instability, and their high viscosity or solubility can influence the extraction efficiency. Despite these limitations, in recent years, DESs have been successfully used in multiple roles in solid-phase microextraction (SPME). They may be used to functionalize or modify sorbent materials, thereby forming composite sorbents with enhanced performance. Moreover, DESs can be combined with polymers to produce hybrid materials with improved extraction capabilities. Additionally, DESs can act as porogens within SPME sorbents, increasing sorption capacity and, consequently, extraction efficiency. They can also serve as green desorption solvents, replacing traditional volatile organic solvents during the recovery of analytes from sorbent materials. This review synthesizes current knowledge on the implementation of DESs in SPME techniques, critically evaluating their primary advantages and inherent limitations. The novelty of this review lies in the assessment of DES-based SPME through the metrics of greenness and sustainable chemistry. Furthermore, the review identifies research perspectives and priorities to advance DES-based SPME, including: the integration of predictive modeling (COSMO-RS, machine learning) to elucidate DES-analytes interactions; the adoption of 3D printing for the precision fabrication of DES-based sorbents; the standardization of DES-based SPME performance; and the exploration of natural DESs for in vivo SPME in biomedical applications. Sorbent ; Extraction (chemistry) ; Eutectic system ; Solid-phase microextraction ; Sorption ; Desorption Analytical chemistry methods development ; Ionic liquids properties and applications ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Edoardo Monti | Peter Yatsyshin | Konstantinos Gkagkas | A. Duncan
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Learning Density Functionals to Bridge Particle and Continuum Scales Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models. Interpretability ; Helmholtz free energy ; Consistency (knowledge bases) ; Statistical mechanics ; Density functional theory ; Artificial neural network ; Adaptability Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Zhe Su | Shuoran Song | Qi Wang | Jian-Hua Jiang
Source: ArXiv.org (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Topological surface phonons modulate thermal transport in semiconductor thin films While phonon topology in crystalline solids has been extensively studied, its influence on thermal transport-especially in nanostructures-remains elusive. Here, by combining first-principles-based machine learning potentials with the phonon Boltzmann transport equation and molecular dynamics simulations, we systematically investigate the role of topological surface phonons in the in-plane thermal transport of semiconductor thin films (Si, 4H -SiC, and c-BN). These topological surface phonons, originating from nontrivial acoustic phonon nodal lines, not only serve as key scattering channels for dominant acoustic phonons but also contribute substantially to the overall thermal conductivity. Remarkably, for these thin semiconductor films below 10 nm this contribution can be as large as over 30% of the in-plane thermal conductivity at 300 K, and the largest absolute contribution can reach 82 W/m-K, highlighting their significant role in nanoscale thermal transport in semiconductors. Furthermore, we demonstrate that both temperature and biaxial strain provide effective means to modulate this contribution. Our work establishes a direct link between topological surface phonons and nanoscale thermal transport, offering the first quantitative assessment of their role and paving the way for topology-enabled thermal management in semiconductors. Phonon ; Boltzmann equation ; Thermal conductivity ; Topology (electrical circuits) ; Scattering ; Semiconductor ; Nanoscopic scale Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Chiedu Ezeanyim Okechukwu | Charles Chikwendu Okpala | Somto Kenneth Onukwuli
Source: Tech Journal of Engineering Science (article, 2025-12-03)
Field: Materials Science — Polymers and Plastics

Design and Optimization of Sustainable Green Composites for High-Performance Applications This study presents a data-driven framework for the design and optimization of next-generation sustainable green composites that are aimed at high-performance industrial applications. A hybrid dataset that comprises 180 experimental records of natural fiber-reinforced biopolymer composites was analyzed using Machine Learning (ML) algorithms, including Random Forest Regression (R² = 0.962), Artificial Neural Network (R² = 0.948), and Support Vector Regression (R² = 0.921). Feature importance analysis identified fiber volume fraction (38.5%), filler type (24.7%), and matrix viscosity (18.9%) as the most influential variables that govern tensile strength and biodegradability. Multi-objective optimization with the application of NSGA-II achieved a tensile strength of 127 MPa and biodegradability of 73%, which represent a 19.6% increase in mechanical performance and a 42% improvement in environmental compatibility when compared to conventional composites. Life-cycle assessment revealed significant sustainability advantages: embodied energy reduced by 33.8% (from 68 MJ/kg to 45 MJ/kg), carbon footprint lowered by 52% (from 2.5 kg CO₂-eq/kg to 1.2 kg CO₂-eq/kg), and end-of-life recyclability enhanced from 42% to 78%. Furthermore, the optimized composite achieved a processing temperature reduction of 21.4% and a 20.5% lower material cost. These results confirm that the integration of ML-driven prediction and optimization with green composite fabrication can accelerate sustainable materials development, reduce resource waste by up to 60%, and provide a replicable model for digital twin-assisted design. The proposed framework demonstrates clear potential for adoption in automotive, aerospace, and packaging sectors, where lightweight, recyclability, and environmental performance are critical. Embodied energy ; Ultimate tensile strength ; Composite number ; Artificial neural network ; Sustainable design ; Compatibility (geochemistry) ; Sustainability ; Carbon footprint ; Energy consumption ; Support vector machine Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Composite Material Mechanics