jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gerardo M. Casañola‐Martín | Grace Tiffany | Omolola Eniodunmo | Tori Oas | Mohammed A. Jabed | Wenfang Sun | Dmitri S. Kilin | Svetlana Kilina | Bakhtiyor Rasulev
Source: ChemRxiv (preprint, 2025-04-30)
Field: Materials Science — Materials Chemistry

Accelerating the Design of Cu(I) Complexes with Near-Infrared Absorption by Interpretable Machine Learning Approaches Metal coordination complexes have emerged as promising molecules because of its interesting optical properties and among them the Cu(I) complexes are one of the most fascinating ones, because it’s combination of optical properties and sustainability at the same time. In this work we show for the first time a combination of quantum chemical and cheminformatics approaches to study the wavelength and oscillator strength as photoluminescence properties in a data set of Cu(I) complexes. As a result, machine learning-based Quantitative Structure-Property Relationship (ML/QSPR) model based on machine learning that can reliably predict the absorption wavelength and oscillator strength of Cu(I) complexes was successfully developed, laying the groundwork for high-throughput virtual screening of Cu(I) complexes optical properties. To explain the differences in absorption wavelengths and oscillator strength across structurally diverse individual Cu(I) complexes, the relation between the models and molecular descriptors was analyzed with patterns revealed positive contributing to the properties like polarizability effects, aliphatic chains connected to heteroatoms, and other with negative influence corresponding to symmetry factors, and molecular size and branching. The squared correlation coefficient values for training and validation were 0.82 and 0.85, respectively. In addition, the applicability domain demonstrated the reliability of the prediction of the obtained models. This combined quantum-chemical and cheminformatics strategy has great potential for rational design of novel Cu(I) complexes with desired optical properties. Furthermore, the described procedures might be applied to a wide variety of copper transition-metal complexes. Absorption (acoustics) ; Infrared ; Artificial intelligence ; Chemistry ; Machine learning ; Computer science ; Materials science ; Optics ; Physics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jun Hu | Yanchen Jian | Dawei Sun | Hongkui Zhou | Weidong Lou | Jin Zhang | Chengquan Zhou | Wenxuan Chen
Source: Journal of Food Composition and Analysis (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of multiple dried squid qualities by hyperspectral imaging combined with 1D-KAN-CNN Hyperspectral imaging ; Squid ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Computer science ; Biology ; Geology ; Fishery Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Qiaolin Gou (12211411) | Qun Su (771328) | Jike Wang (10766056) | Hui Zhang (7197) | Huiyong Sun (599853) | Xujun Zhang (1262985) | Linlong Jiang (21566242) | Meijing Fang (21551145) | Yu Kang (40770) | Huanxiang Liu (418534) | Tingjun Hou (61462) | Chang-Yu Hsieh (4738329)
Source: Figshare (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

ChargeNet: E(3)Equivariant Graph Attention Networkfor Atomic Charge Prediction Atomic charge is a fundamental quantum chemical property essential for advancing drug design and discovery. Although quantum mechanics (QM) methods offer the highest level of accuracy, their computational demands scale quadratically with the number of atoms, limiting their practicality for large-scale applications. In light of this, empirical and semiempirical methods have been introduced to improve computational efficiency, albeit often at the expense of accuracy. The advent of artificial intelligence has witnessed a growing application of machine learning (ML) techniques to accelerate atomic charge predictions. However, existing ML models often suffer from low accuracy and limited generalization capabilities. To address these challenges, we introduce an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision. This model introduces a sophisticated global graph attention mechanism, enabling it to capture charge contributions across multiple scales. By utilizing a combination of structural symmetry-preserving transformations and multiscale attention, our approach not only preserves the inherent symmetries of molecular structures but also substantially improves the model’s accuracy, generalization, and robustness in complex scenarios. Our empirical analyses demonstrate that, compared to leading baseline models, the proposed model improves charge prediction accuracy by over 40% on average across various charge-calculation schemes. Remarkably, the model achieves superior performance on the external RESP (restrained electrostatic potential) test data sets, with a 54.6% improvement over the baseline. Additionally, we evaluated our charge model under the setting of virtual screening, where it outperforms both the OPLS3 charges and baseline deep learning models across all evaluation metrics, highlighting its extensive potential for scientific discovery. Scalability ; Artificial neural network ; Robustness (evolution) ; Graph ; Generalization ; Quantum ; Charge (physics) ; Atomic charge ; Formalism (music) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Rothenhäusler, Florian | Albuquerque, Rodrigo Queiroz | Sticher, Marcel | Kuenneth, Christopher | Ruckdaeschel, Holger
Source: EPub Bayreuth (University of Bayreuth) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Application of convolutional neural networks and ensemble methods in the fiber volume content analysis of natural fiber composites The incorporation of natural fibers into fiber-reinforced polymer composites (FRPC) has the potential to bolster their sustainability. A critical attribute of FRPC is the fiber volume content (FVC), a parameter that profoundly influences their thermo-mechanical characteristics. However, the determination of FVC in natural fiber composites (NFC) through manual analysis of light microscopy images is a labor-intensive process. In this work, it is demonstrated that the pixels from light microscopy images of NFC can be utilized to predict FVC using machine learning (ML) models. In this proof-of-concept investigation, it is shown that convolutional neural network-based models predict FVC with an accuracy required in polymer engineering applications, with a mean average error of 2.72 and an R2 coefficient of 0.85. Finally, it is shown that much simpler ML models, non-specialized in image recognition, besides being much easier and more efficient to optimize and train, can also deliver good accuracies required for FVC characterization, which not only contributes to the sustainability, but also facilitates the access of such models by researchers in regions with little computational resources. This study marks a substantial advancement in the area of automated characterization of NFC, and democratization of knowledge, offering a promising avenue for the enhancement of sustainable materials. Convolutional neural network ; Fiber ; Pixel ; Volume (thermodynamics) ; Artificial neural network ; Microscopy ; Natural fiber ; Support vector machine Natural Fiber Reinforced Composites ; Composite Material Mechanics ; Fiber-reinforced polymer composites
Authors: 見波, 将 | 丸山, 泰明 | 阿部, 能将 | 仲山, 智裕 | 嶋田, 隆広
Source: Kyoto University Research Information Repository (Kyoto University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

First-principles based data-driven strain engineering for ferroelectrics via active machine learning: A nonlinear piezoelectric constitutive equation Strain engineering is a crucial approach in the engineering field to optimize various physical properties of materials by applying mechanical strain loading. However, it is extremely challenging to find out the best conditions of strain with unprecedented physical properties in the vast strain space consisting of six components. Here, we developed a technical framework that enables efficient exploration of physical properties in the vast strain space based on machine learning (i.e., artificial neural networks), active learning, and high-throughput first-principles calculation. We demonstrated the active learning technique to successfully and efficiently construct an accurate machine learning model for ferroelectric PbTiO₃ with minimal first-principles datasets (only 3.7% of the vast strain-space). Our machine learning model can accurately predict the nonlinear mechanical deformation and electromechanical response in the three components of normal strain loading. We also carried out strain optimization of piezoelectric response using the machine learning model and found that a large piezoelectric response is five times larger than without strain loading. We showed that the physical property explorer framework constructed in this study makes it possible to optimize strain for various material properties in a vast strain space by calculating only a small number of data points. These results suggest paving the way for constructing nonlinear piezoelectric constitutive equations for novel piezoelectric devices via strain engineering. Piezoelectricity ; Nonlinear system ; Constitutive equation ; Strain (injury) ; Field (mathematics) ; Deformation (meteorology) ; Property (philosophy) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Sensor and Energy Harvesting Materials
Authors: Timothée Redarce | G. Lambard | Masahiko Demura | Hisao Matsunaga
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Data-Driven Prediction of Fatigue Crack Growth Rate in Low-Alloy Steels in High-Pressure Hydrogen Gas Using Processing Parameters and Microstructure Images Microstructure ; Materials science ; Alloy ; Paris' law ; Hydrogen ; Metallurgy ; Gas pressure ; Composite material ; Crack closure ; Fracture mechanics ; Engineering ; Petroleum engineering ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels
Authors: Shabir Ahmad et al.
Source: Letters in High Energy Physics (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Advancing Computational Modeling in Quantum Materials: A DFT-Based Approach to Electronic Structure and Material Properties Computational modeling is now a cornerstone of quantum materials research, where the density functional theory (DFT) provides a key tool at the heart of predicting electronic structure and material properties. We evaluate DFT methods, with a focus on exchange-correlation functional; computational efficiency enhancements; and the incorporation of machine learning (ML). The study leverages first-principles calculations for the analysis of band structures, DOS, and functional-dependent electronic properties variations for quantum materials (2D materials, (Graphene, MoS₂, TMDs), superconductors, topological insulators. Additionally, time-dependent DFT (TDDFT) and orbital-free DFT (OF-DFT) increase accuracy for large-scale simulations, which are limited by computational resources in complex materials. In fact, these new ML-assisted DFT techniques greatly improve the computational speed and predictive accuracy, thereby minimizing the conflicting options between computational cost and precision. The study further presents critical perspectives on defect engineering in the context of semiconductors, asserting its importance in tuning electronic properties for emerging fields such as Nano electronics and quantum computing applications. Standard DFT functional come with accuracy limitations, but by combining them with AI-based surrogate models and many-body physics such as DFT+DMFT, GW, and Quantum Monte Carlo methods, there are powerful options on both fronts. Such innovations are unlocked by more efficient, scalable, and precise quantum material simulations, facilitating advances in next-generation devices based on optoelectronics, spintronic, and superconductivity. This work highlights the potential of AI-assisted computational modeling to revolutionize quantum materials science and ultimately enable advances in energy-efficient electronics and quantum technologies. Electronic structure ; Computer science ; Quantum ; Statistical physics ; Materials science ; Computational science ; Physics ; Quantum mechanics Advanced Physical and Chemical Molecular Interactions
Authors: Satoru Takeshita | Shinya Mine | Takumi Ono
Source: Angewandte Chemie (article, 2025-04-01)
Field: Chemistry — Spectroscopy

Data‐Driven Review on Aerogels and Polymeric Aerogels Abstract A thoroughly data‐driven review on the chemistry, properties, and applications of aerogels is provided. Aerogel is a category of solid materials having homogeneous, highly porous nanostructures. Unique physicochemical properties of aerogels have attracted much attention in wide fields due to their potentials for energy/environmental issues in the 21st century, while an exponentially increasing number of papers are causing an academic fever and unreliable data issues. Statistical analysis based on an exhaustive literature survey from almost all the papers with “aerogel” in their titles reveals a flood of freeze‐dried aerogels mainly consisting of organic polymers and organic–inorganic composites. Process‐property data from polymer aerogel papers are analyzed by a machine‐learning approach aiming to establish data infrastructure toward future aerogel studies. Data‐evidenced critical discussion specifies a tendency in unreliable thermal conductivity data of these papers. Aerogel ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Engineering Aerogels and thermal insulation ; Phase Equilibria and Thermodynamics
Authors: Y. A. Tang
Source: Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

An automated machine learning framework for high-fidelity prediction of phase stability and thermal conductivity in metallic nuclear fuels Thermal conductivity ; Gradient boosting ; Artificial neural network ; Boosting (machine learning) ; Stability (learning theory) ; Regression ; Support vector machine ; Feature (linguistics) ; Linear regression Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Fermín Sáez-Pardo | J.J. Giner-Sanz | V. Pérez‐Herranz
Source: Systems and Soft Computing (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Deep Learning algorithm, based on convolutional neural networks, for equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy • A new Deep Learning algorithm was developed for EEC recommendation for EIS spectra • Convolutional Neural Networks were optimized for the first time for EEC recommendation • The algorithm achieved better recommendation accuracies than previous AI algorithms • The algorithm was applied to an experimental EIS of a commercial Li-V2O5 battery • Though the results are far from perfect, CNNs seem promising for EEC recommendation Electrochemical Impedance Spectroscopy (EIS) is a technique widely used in the field of electrochemistry, due to its ability to probe the dynamics of electrochemical systems. Commonly, EIS spectra are analysed using Equivalent Electrical Circuits (EECs). The EEC selection is not trivial. For this reason, Digby D. Macdonald proposed the Electrochemical Genome Project, which would consist of a large database of EIS spectra and an Artificial Intelligence able to recommend EECs given an experimental EIS spectrum. In this work, we developed a Deep Learning algorithm, based on Convolutional Neural Networks (CNNs), for EEC recommendation for EIS spectra. To achieve this, we optimized the CNN in 3 sequential stages: first, the convolutional architecture was optimized; then, the Initial Learn Rate was selected; and finally, the dense network architecture was optimized. At the end of this process, we obtained a CNN model with a maximum test accuracy of 61.11%. The obtained results show that CNNs are good candidates for EEC recommendation tools for EIS spectra. Convolutional neural network ; Deep learning ; Dielectric spectroscopy ; Equivalent circuit ; Pattern recognition (psychology) ; Electrical impedance Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Explainable Artificial Intelligence (XAI)
Authors: Ali Banjafar | Guido Falk von Rudorff
Source: The Journal of Chemical Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Intrinsic dimensionality of molecular properties Chemical space, which encompasses all stable compounds, is unfathomably large, and its dimension scales linearly with the number of atoms considered. The success of machine learning methods suggests that many physical quantities exhibit substantial redundancy in that space, lowering their effective dimensionality. A low dimensionality is favorable for machine learning applications, as it reduces the required number of data points. It is unknown, however, how far the dimensionality of physical properties can be reduced, how this depends on the exact physical property considered, and how accepting a model error can help further reduce the dimensionality. We show that accepting a modest, nearly negligible error leads to a drastic reduction in independent degrees of freedom. This applies to several properties, such as the total energy, frontier orbital energies, and static polarizability for a wide range of neutral molecules with up to 20 atoms. We provide a method to quantify an upper bound for the intrinsic dimensionality given a desired accuracy threshold by the inclusion of all continuous variables in the molecular Hamiltonian, including the nuclear charges. We find the intrinsic dimensionality to be remarkably stable across molecules, i.e., it is a property of the underlying physical quantity and the number of atoms rather than a property of an individual molecular configuration and, therefore, highly transferable between molecules. The results suggest that the feature space of state-of-the-art molecular representations can be compressed further, leaving room for more data efficient and transferable models. Curse of dimensionality ; Dimensionality reduction ; Property (philosophy) ; Redundancy (engineering) ; Dimension (graph theory) ; Intrinsic dimension ; Feature (linguistics) ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Computational Drug Discovery Methods
Authors: Tirtha Vinchurkar | Kareem Abdelmaqsoud | John R. Kitchin
Source: ArXiv.org (preprint, 2025-04-17)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles Machine-learned potentials (MLPs) have revolutionized materials discovery by providing accurate and efficient predictions of molecular and material properties. Graph Neural Networks (GNNs) have emerged as a state-of-the-art approach due to their ability to capture complex atomic interactions. However, GNNs often produce unreliable predictions when encountering out-of-domain data and it is difficult to identify when that happens. To address this challenge, we explore Uncertainty Quantification (UQ) techniques, focusing on Direct Propagation of Shallow Ensembles (DPOSE) as a computationally efficient alternative to deep ensembles. By integrating DPOSE into the SchNet model, we assess its ability to provide reliable uncertainty estimates across diverse Density Functional Theory datasets, including QM9, OC20, and Gold Molecular Dynamics. Our findings often demonstrate that DPOSE successfully distinguishes between in-domain and out-of-domain samples, exhibiting higher uncertainty for unobserved molecule and material classes. This work highlights the potential of lightweight UQ methods in improving the robustness of GNN-based materials modeling and lays the foundation for future integration with active learning strategies. Uncertainty quantification ; Robustness (evolution) ; Propagation of uncertainty ; Artificial neural network ; Deep neural networks ; Graph ; Deep learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ying Zhou | Sacha Fop | Abbie C. Mclaughlin | James A. Dawson
Source: Research Square (preprint, 2025-04-21)
Field: Materials Science — Materials Chemistry

Elucidating Oxide-Ion and Proton Transport in Ionic Conductors using Machine Learning Potentials Proton ; Ionic bonding ; Oxide ; Electrical conductor ; Ion ; Chemical physics ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Physics ; Nuclear physics ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Yuhao Ang | Helmi Zulhaidi Mohd Shafri | Mohammed Mustafa Al-Habshi
Source: Agriculture and Natural Resources (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with orange data mining Importance of the work:With the advancement of hyperspectral remote sensing technology, the potential of categorising frogeye leaf spot (FLS) of soybean has been examined.No previous study has investigated Orange mining tool as visual programming approach in analysing hyperspectral reflectance data, especially in crop disease detection. Objectives:The main objective of the study is to classify the severity level of FLS disease in soybean using hyperspectral reflectance data and machine learning algorithms. Materials and Methods:We used hyperspectral reflectance data from healthy and FLS of soybeans.The first step was to smooth out the data by applying a filtering technique called Savitzky-Golay to remove the spectrum noise.The ReliefF feature selection technique was used to determine the most influential wavelengths for the classification of FLS disease severity in soybean.Next, machine learning (ML) methods (i.e.decision tree, gradient boosting, random forest, stacking, and neural network) were used to classify FLS of soybean.This analysis' performance was evaluated using overall accuracy, F1, precision and the receiver operating characteristic curve metric.All of these steps were conducted using Orange data mining software.Results: Based on the findings, neural network scored the highest overall accuracy of 98.6% after conducting filtering technique.Furthermore, reliefF-Gradient boosting and random forest algorithms achieved promising overall accuracy of 97.4% and 96.9%, respectively after implementing filtering and feature selection techniques.Main finding: Due to the integration of workflow and the specially designed spectroscopic widget in Orange Data Mining Software, it is capable of processing hyperspectral reflectance data in order to determine the severity level of disease affecting crops. Hyperspectral imaging ; Orange (colour) ; Artificial intelligence ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses