jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maciej Haranczyk | Christina Schenk | Jose Hobson | De-Yi Wang
Source: OSF Preprints (OSF Preprints) (preprint, 2025-06-06)
Field: Materials Science — Polymers and Plastics

Data-Driven Design and Green Preparation of Bio-Based Flame Retardant Polyamide Composites This work introduces, for the first time, an innovative bio-based flame retardant (FR) system for biocomposites, integrating experimental insights and machine learning (ML) to optimize both composition and performance. By employing a computationally guided, cost-efficient experimentation strategy, we systematically combine design of experiments for space exploration, ML-driven property prediction, and optimization methods to rapidly identify high-performance formulations. Crucially, this approach demonstrates how data-driven techniques can be seamlessly incorporated into conventional experimental material design, ensuring proper sampling of the design space and leveraging the collected data to generate new predictions and optimize the properties of these sustainable materials. As a result, mechanical strength is significantly enhanced and fire safety improved, minimizing reliance on resource-intensive trial-and-error processes. The optimal formulation achieved an 18.4% increase in tensile strength (TS) and a 53.1% reduction in the peak heat release rate (pHRR) compared to the neat polymer. Bayesian optimization further validated individual optimal solutions, delivering up to a 22.3% improvement in TS and a 73.7% reduction in pHRR. Overall, this research establishes a digitally integrated workflow that accelerates the development of sustainable, high-performance biocomposites and bio-based flame retardants, providing eco-friendly alternatives to conventional fire-safe polymeric materials. Fire retardant ; Ultimate tensile strength ; Bayesian optimization ; Reduction (mathematics) ; Design of experiments ; Workflow ; Polyamide ; Work (physics) ; Space (punctuation) Flame retardant materials and properties ; Fire dynamics and safety research ; Natural Fiber Reinforced Composites
Authors: Jing Qian (170232) | Jingkuan Lyu (21792009) | Zhucheng Yang (14550861) | Jianping Xie (550373)
Source: Figshare (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Topology-DrivenSearch for Molecular Links in Nanoclusters:Structure Prediction for Au<sub>16</sub>(SR)<sub>14</sub> Molecular topology provides a fundamental understanding of the molecular structures and dictates the chemical properties. Here, knot theory, a branch of mathematics, is leveraged to systematically search for molecular links with machine learning interatomic potential and give a structure prediction for Au<sub>16</sub>(SR)<sub>14</sub> (SR denotes a thiolate ligand). This knot theory-based search method is also applicable to a variety of low-valency nanoclusters, including the widely studied Au<sub>15</sub>(SR)<sub>13</sub>. The thoroughly searched energy landscape of the small sized nanoclusters also suggests potential isoelectronic conversions during the typical nanocluster reduction growth. This study not only provides a possible scaffold for future Au nanocluster structure prediction but also promotes the understanding of the implications of twists and turns for a nanocluster in a topological space. Nanoclusters ; Knot (papermaking) ; Topology (electrical circuits) ; Variety (cybernetics) ; Molecular dynamics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Jinni Yang (21228621) | Runtong Pan (18496325) | Jikai Sun (12474355) | Jianzhong Wu (465178)
Source: Figshare (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

High-Dimensional Operator Learning for Molecular DensityFunctional Theory Classical density functional theory (cDFT) provides a systematic framework to predict the structure and thermodynamic properties of chemical systems through molecular density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established a convolutional operator learning method that effectively separates the high-dimensional molecular density profile into lower-dimensional components, thereby exponentially reducing the vast input space. The operator learning network demonstrates exceptional learning capabilities, accurately mapping the relationship between the molecular density profile and its one-body direct correlation function for an atomistic polarizable model of carbon dioxide. The machine-learning procedure can be generalized to more complex molecular systems, offering high-precision operator-cDFT calculations at a low computational cost. Density functional theory ; Operator (biology) ; Function (biology) ; Polarizable continuum model ; Polarizability ; Molecular dynamics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Chunguang Shen | Wangzhong Mu | Chenchong Wang | Jun Hui Yu | Wei Xu | Peter Hedström
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Integrated machine learning approach for predicting low-temperature embrittlement in duplex stainless steels via ferrite hardening Embrittlement ; Ferrite (magnet) ; Toughness ; Hardening (computing) ; Random forest ; Duplex (building) Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Shrinjay Sharma (18476637) | Ping Yang (56755) | Yachan Liu (22318781) | Kevin Rossi (3239994) | Peng Bai (220785) | Marcello S. Rigutto (2035702) | Erik Zuidema (2313943) | Umang Agarwal (4965352) | Richard Baur (18476640) | Sofia Calero (1271889) | David Dubbeldam (1271880) | Thijs J.H. Vlugt (650018)
Source: Figshare (article, 2025-09-26)
Field: Chemistry — Inorganic Chemistry

Machine Learning-BasedPredictions of Henry Coefficientsfor Long-Chain Alkanes in One-Dimensional Zeolites: Application toHydroisomerization Shape-selective adsorption in zeolites plays a pivotal role in catalytic hydroisomerization of long-chain alkanes, a key process in producing sustainable aviation fuels from Fischer–Tropsch products. Accurately predicting adsorption behavior for the large number of alkane isomers in different zeolite frameworks is computationally intensive. To address this, we have developed a machine learning framework that rapidly and accurately predicts Henry coefficients of linear (C<sub>1</sub>–C<sub>30</sub>) and branched (C<sub>4</sub>–C<sub>20</sub>) alkanes in one-dimensional zeolites. Using descriptors based on chain length, branching patterns, and molecular graphs, we evaluate multiple ML models, including Random Forest, XGBoost, CatBoost, TabPFN, and D-MPNN in MTT-, MTW-, MRE-, and AFI-type zeolites. TabPFN and D-MPNN offer the highest predictive accuracy. Active learning further boosts model performance by efficiently selecting diverse and structurally informative isomers. We also uncover activity cliffs, where small changes in molecular structure lead to sharp variations in adsorption, and demonstrate that targeted oversampling of these cases improves model robustness. Finally, we combine the ML-predicted Henry coefficients with gas-phase thermodynamics to compute reaction equilibrium distributions for C<sub>16</sub> hydroisomerization. This integrated, data-driven approach enables efficient screening and design of shape-selective zeolite catalysts, thereby reducing the need for costly simulations. Zeolite ; Adsorption ; Alkane ; Process (computing) ; Branching (polymer chemistry) ; Key (lock) Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Han Leng (16855302) | Yulong Xu (3075696) | Ya-Fei Chen (21555861) | Jingqi Sun (14592288) | Jiaxin Li (441199) | Yufei Guan (11374118) | Wei Chen (23863) | Yanfeng Zhang (145335) | Xuwei Chen (1558234)
Source: Figshare (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Precisely Regulatingthe Three-Dimensional Morphologyof COF-Based Architecture via the Alloy-Embedding Strategy as EfficientPhotocatalysts The construction of covalent organic frameworks (COFs) with distinct three-dimensional (3D) structures is crucial to explore the structure–activity relationship and expand potential applications. Herein, we propose a facile strategy to fabricate a COF snowflake, nanofiber, and nanosphere by introducing Au–Ag, Hg–Ag, and Pd–Ag alloys into the structure of phenanthroline-functionalized COF (PA-COF) via ordered reduction of metal ions by a phenanthroline moiety. The final morphologies of 3D PA-COF architectures depend critically on the assembly mode of COF modules, driven by the lattice mismatch as well as the distinct metal autocatalytic activities of the three different alloys. A machine learning model trained on limited data was adopted to guide the construction of a Au–Ag embedded COF snowflake (PA-COF-SF). The obtained PA-COF-SF showed excellent degradation performance to sulfamethoxazole (SMX) with a high-rate constant of 0.0606 min<sup>–1</sup>, due to the significantly enhanced charge transfer and separation efficiency contributed by the embedded Au–Ag alloy and the unique 3D snowflake architecture. Attributed to the strong interactions between Au/Ag atoms and the phenanthroline moiety, PA-COF-SF exhibited favorable homogeneity and remarkable stability, maintaining an 88.5% removal rate of SMX after 20 cycles. This work revealed the construction mechanism of a COF with a unique 3D architecture and provides insights into the structure–activity relationship of the COF materials. Homogeneity (statistics) ; Alloy ; Autocatalysis ; Fabrication ; Metal ; Covalent bond ; Lattice (music) ; Metal-organic framework Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Carbon dioxide utilization in catalysis
Authors: Ramin Pourhanasa | Ali Monadipour
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Quantum-Inspired Bayesian Neural Networks for Early-Age Concrete Strength Prediction: A Classical Feature Map Approach Interpretability ; Feature (linguistics) ; Probabilistic logic ; Artificial neural network ; Kernel (algebra) ; Representation (politics) ; Prior probability ; Embedding ; Compressed sensing Machine Learning in Materials Science ; Concrete Properties and Behavior ; Gaussian Processes and Bayesian Inference
Authors: Hailong Dai | Samuel Friedlein | Irem Efe | Chengyin Wu | Yuxuan Shui | Yanzhou Ji | Xiaolei Guo
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Metals and Alloys

Characterization and Computer Vision of Localized Corrosion on SS304H for Dry Cask Storage Application The ambient air used to cool the stainless steel (SS) canisters for storing spent nuclear fuel contains salt aerosols. These salts can deposit on the canister walls and, upon reacting with ambient humidity, form an aggressive aqueous brine that facilitates localized corrosion and subsequent stress corrosion cracking. This study focuses on characterization and computer vision of pitting corrosion in a 4.47 M MgCl 2 brine with saturated MgCO 3 , which simulates the solution chemistry that develops on the spent nuclear fuel canister surface after deliquescence at a low relative humidity of 40%. Pits with various morphologies were created using three approaches, including atmospheric corrosion exposure in controlled constant relative humidity and temperature, potentiostatic polarization under varying potentials, durations, and solution chemistries, as well as field exposure to a coastal environment for up to half a year. Depending on the exposure condition, distinct pit morphology form on the surface of SS304H. Some of the pits exhibit a cross-hatch morphology similar to that observed on samples exposed to a controlled low relative humidity of 40% in an atmospheric condition. This morphology could have direct implications for the pit-to-crack transition because of the concentrated stress near the sharp features produced by pitting corrosion. The various pits with distinct morphologies created in this study were fed into several computer vision based machine learning and deep learning models to automatically identify the presence of pits and accurately segment their boundaries. The database developed in this study may serve as a foundation for the development of a data-driven model capable of predicting pit-to-crack transition. Corrosion ; Relative humidity ; Brine ; Aqueous solution ; Pitting corrosion ; Humidity Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Graphite, nuclear technology, radiation studies
Authors: Markus Wallerberger
Source: reposiTUm (TU Wien) (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Navigating the bias-variance tradeoff in materials science In this introductory talk, I aim to give a broad overview over machine learning (ML) and related techniques as they have been applied to problems in materials science. The fundamental tradeoff between uncertainty and bias inherent to learning shall serve as our guide. It explains why large-scale general purpose models, while valuable, have had limited impact in physics, and why on the other hand simple ML models tailored to specific problems have led to breakthroughs to molecular dynamics and quantum field theories. Time permitting, we will turn to physical interpretations and phase transitions in the tradeoff itself. Field (mathematics) ; Simple (philosophy) ; Phase (matter) ; Quantum ; Focus (optics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Chong Guo | Kun Guo | Ziliang Deng | Huacheng Zhang | Kang Li | Zhiyong Liu | Bing Xie | Pu Mao | Chee Kiang Ivan Tan | Kui Yao | Francis Eng Hock Tay
Source: Materials Today Energy (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated screening of all-inorganic perovskites for tandem solar cells Band gap ; Tandem ; Photovoltaic system ; Density functional theory ; Linear discriminant analysis ; Crystal (programming language) ; ABX test ; Electronic band structure ; Stability (learning theory) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Dan Lin | Anqi Liu | Lei Pan | Liang Yang | Changlong Jiang
Source: Chemical Engineering Journal (article, 2025-11-19)
Field: Chemistry — Inorganic Chemistry

Reversible flexible functional metal-organic frameworks sensor with artificial intelligence data analysis for real-time trace water monitor Reusability ; Visualization ; TRACE (psycholinguistics) ; Carboxymethyl cellulose ; Response time ; Cellulose ; Environmentally friendly Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Jing Qian | Jingkuan Lyu | Zhucheng Yang | Jianping Xie
Source: The Journal of Physical Chemistry Letters (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Topology-Driven Search for Molecular Links in Nanoclusters: Structure Prediction for Au<sub>16</sub>(SR)<sub>14</sub> Molecular topology provides a fundamental understanding of the molecular structures and dictates the chemical properties. Here, knot theory, a branch of mathematics, is leveraged to systematically search for molecular links with machine learning interatomic potential and give a structure prediction for Au<sub>16</sub>(SR)<sub>14</sub> (SR denotes a thiolate ligand). This knot theory-based search method is also applicable to a variety of low-valency nanoclusters, including the widely studied Au<sub>15</sub>(SR)<sub>13</sub>. The thoroughly searched energy landscape of the small sized nanoclusters also suggests potential isoelectronic conversions during the typical nanocluster reduction growth. This study not only provides a possible scaffold for future Au nanocluster structure prediction but also promotes the understanding of the implications of twists and turns for a nanocluster in a topological space. Nanoclusters ; Topology (electrical circuits) ; Computer science ; Materials science ; Nanotechnology ; Mathematics ; Combinatorics Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Topological and Geometric Data Analysis
Authors: Diego H. de Hoyos-Sifuentes | Perla J. Reséndiz-Hernández | Edgar O. Reséndiz‐Flores | L.A. Garcı́a-Cerda | Rocío M. Ochoa-Palacios
Source: Journal of Sol-Gel Science and Technology (article, 2025-03-13)
Field: Chemistry — Inorganic Chemistry

An intelligent prediction model for the synthesis of magnetic glass-ceramic and experimental validation Ceramic ; Materials science ; Glass-ceramic ; Computer science ; Composite material Pigment Synthesis and Properties ; Metallurgical Processes and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Ole‐Christian Galbo Engstrøm
Source: ArXiv.org (preprint, 2025-10-15)
Field: Chemistry — Analytical Chemistry

Near-Infrared Hyperspectral Imaging Applications in Food Analysis -- Improving Algorithms and Methodologies This thesis investigates the application of near-infrared hyperspectral imaging (NIR-HSI) for food quality analysis. The investigation is conducted through four studies operating with five research hypotheses. For several analyses, the studies compare models based on convolutional neural networks (CNNs) and partial least squares (PLS). Generally, joint spatio-spectral analysis with CNNs outperforms spatial analysis with CNNs and spectral analysis with PLS when modeling parameters where chemical and physical visual information are relevant. When modeling chemical parameters with a 2-dimensional (2D) CNN, augmenting the CNN with an initial layer dedicated to performing spectral convolution enhances its predictive performance by learning a spectral preprocessing similar to that applied by domain experts. Still, PLS-based spectral modeling performs equally well for analysis of the mean content of chemical parameters in samples and is the recommended approach. Modeling the spatial distribution of chemical parameters with NIR-HSI is limited by the ability to obtain spatially resolved reference values. Therefore, a study used bulk mean references for chemical map generation of fat content in pork bellies. A PLS-based approach gave non-smooth chemical maps and pixel-wise predictions outside the range of 0-100\%. Conversely, a 2D CNN augmented with a spectral convolution layer mitigated all issues arising with PLS. The final study attempted to model barley's germinative capacity by analyzing NIR spectra, RGB images, and NIR-HSI images. However, the results were inconclusive due to the dataset's low degree of germination. Additionally, this thesis has led to the development of two open-sourced Python packages. The first facilitates fast PLS-based modeling, while the second facilitates very fast cross-validation of PLS and other classical machine learning models with a new algorithm. Hyperspectral imaging ; Preprocessor ; Convolutional neural network ; Partial least squares regression ; RGB color model ; Pattern recognition (psychology) ; Convolution (computer science) ; Chemical imaging Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Enze Hou | Xiaoyang Wang | Han Wang
Source: ArXiv.org (preprint, 2025-11-17)
Field: Materials Science — Materials Chemistry

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical behavior comprises two key phenomena: plastic deformation and shock induced phase transitions. The underlying mechanisms of these processes are still poorly understood. Revealing these mechanisms remains challenging for experimental approaches. Non-equilibrium molecular dynamics (NEMD) simulations are an alternative theoretical tool for studying dynamic responses, as they capture atomic-scale mechanisms such as defect evolution and deformation pathways. However, due to the limited accuracy of empirical interatomic potentials, the reliability of previous NEMD studies is questioned. Using our newly developed machine learning potential for Pb-Sn alloys, we revisited the microstructure evolution in response to shock loading under various shock orientations. The results reveal that shock loading along the [001] orientation of Pb exhibits a fast, reversible, and massive phase transition and stacking fault evolution. The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation. Loading along the [011] orientation leads to slow, irreversible plastic deformation, and a localized FCC-BCC phase transition in the Pitsch orientation relationship. This study provides crucial theoretical insights into the dynamic mechanical response of Pb, offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions. Shock (circulatory) ; Crystal twinning ; Deformation (meteorology) ; Reliability (semiconductor) ; Phase (matter) ; Deformation mechanism ; Dynamic loading ; Plasticity Machine Learning in Materials Science ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Patricia Suriana | Joshua A. Rackers | Ewa M. Nowara | Pedro O. Pinheiro | John M. Nicoloudis | Vishnu Sresht
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning Machine learning models for 3D molecular property prediction typically rely on atom-based representations, which may overlook subtle physical information. Electron density maps -- the direct output of X-ray crystallography and cryo-electron microscopy -- offer a continuous, physically grounded alternative. We compare three voxel-based input types for 3D convolutional neural networks (CNNs): atom types, raw electron density, and density gradient magnitude, across two molecular tasks -- protein-ligand binding affinity prediction (PDBbind) and quantum property prediction (QM9). We focus on voxel-based CNNs because electron density is inherently volumetric, and voxel grids provide the most natural representation for both experimental and computed densities. On PDBbind, all representations perform similarly with full data, but in low-data regimes, density-based inputs outperform atom types, while a shape-based baseline performs comparably -- suggesting that spatial occupancy dominates this task. On QM9, where labels are derived from Density Functional Theory (DFT) but input densities from a lower-level method (XTB), density-based inputs still outperform atom-based ones at scale, reflecting the rich structural and electronic information encoded in density. Overall, these results highlight the task- and regime-dependent strengths of density-derived inputs, improving data efficiency in affinity prediction and accuracy in quantum property modeling. Property (philosophy) ; Representation (politics) ; Convolutional neural network ; Electron density ; Density functional theory ; Pattern recognition (psychology) ; Quantum ; Atom (system on chip) ; Focus (optics) Machine Learning in Materials Science ; Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications