jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xian Liu | Wei Tong | Biao Di | Ling Zhang | Juan K. Lin
Source: Sensors (article, 2025-11-14)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection and Grading of Plum Quality Based on Multimodal Data Assessing plum quality solely based on the external appearance of the peel may lead to inaccurate results. This paper proposes a multimodal data fusion technique based on deep learning, which evaluates plum quality by fusing color image data and spectral data of plums, thereby enabling plum grading. The method utilizes the Visual Geometry Group 16-layer network (VGG16) to extract plum image features, and a 1D Convolutional Neural Network (1D-CNN) to extract near-infrared spectral data of plums, subsequently classifying plum quality through the network's fully connected layers and output layer. Each modality independently extracts features: color images provide external color information, while visible and near-infrared spectroscopy (wavelength range 350-1700 nm) captures surface and internal chemical composition spectral properties. The multimodal preprocessing and feature extraction process creates an optimal comprehensive information representation for plum quality assessment within the feature space. By processing these integrated features through a fully connected neural network, the classification accuracy of plum quality reaches 100%, significantly outperforming single-modal methods (color imaging: 85.71%; spectroscopy: 83.33%). The performance comparison analysis between multimodal fusion and single-modal methods further confirms the robustness and applicability of the multimodal fusion approach, providing new technical means for non-destructive detection and grading of plum quality. This method can simultaneously detect internal and external quality indicators, compensating for the limitations of traditional single-dimensional detection. Preprocessor ; Robustness (evolution) ; Convolutional neural network ; Feature extraction ; Pattern recognition (psychology) ; Hyperspectral imaging ; Fusion ; Artificial neural network Spectroscopy and Chemometric Analyses ; Fire Detection and Safety Systems ; Insect Pheromone Research and Control
Authors: Yunchuan Cheng | Lei Wang | Zhihua Dong | Zengyong Zheng | Zhihong Xia | Shengwen Bai | Jiangfeng Song | Bin Jiang
Source: Journal of Materials Informatics (article, 2025-11-14)
Field: Materials Science — Biomaterials

RF-NSGA-II framework for inverse design of high-performance Mg-Gd-based magnesium alloys An inverse design framework, RF-NSGA-II, is developed using machine learning (ML) methods and a multi-objective co-optimization strategy. It enables intelligent design of chemical compositions and processing parameters for thermo-mechanical treatments based on desired mechanical properties of Mg alloys. Using a database of extruded Mg-Gd and Mg-Y-based alloys, RF-NSGA-II integrates an optimized forward model with a non-dominated sorting genetic algorithm II (NSGA-II). The forward model is constructed by evaluating the performance of different ML algorithms, with the random forest (RF) algorithm experimentally validated to accurately describe the relationship between chemical composition and mechanical properties. RF-NSGA-II simultaneously optimizes multiple mechanical properties, and validation through experimental measurements demonstrates its effectiveness. Using target mechanical properties as inputs, chemical compositions and processing parameters for solid-solution treatment and extrusion are efficiently determined for a high-strength Mg-11.5Gd-6.0Y-1.0Zn-0.2Mn (wt.%) alloy and a high-ductility Mg-2.5Gd-1.0Zn (wt.%) alloy, achieving tensile strength/elongation values of 417 MPa/3.2% and 223 MPa/34%, respectively. These results provide a transparent and effective route for the inverse design of advanced Mg alloys based on desired mechanical properties. Inverse ; Sorting ; Ultimate tensile strength ; Extrusion ; Alloy ; Genetic algorithm ; Inverse problem ; Magnesium Magnesium Alloys: Properties and Applications ; Magnetic Properties of Alloys ; Heusler alloys: electronic and magnetic properties
Authors: Qin Zhu | Yanwei Tang | Xinyao Ge | Chong Zhang | Xun Fu | Yongxia Wang | Dong Jin | Lizhu Dong | Jinyi Zhang | Qiang Zhao | Ying Wei | Xiaogang Cheng | Linghai Xie
Source: Talanta (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Generating a chemical database of organic nanomers and applying active learning to predict HOMO, LUMO and band gap: Accelerating optoelectronic nanopolymer materials discovery HOMO/LUMO ; Range (aeronautics) ; Chemical database ; Construct (python library) ; Active layer ; Molecular descriptor ; Density functional theory ; Sample (material) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Hyun-Su Park | In Woo Park | Dowoo Kim | Ha-Yoon Nah | Jian-tan Yang | Joo Hye Yeo | Jaesung Choi | JG Choi | Hyung‐Ho Park | Heon‐Jin Choi
Source: ACS Applied Materials & Interfaces (article, 2025-02-28)
Field: Chemistry — Spectroscopy

Pd-Modified Microneedle Array Sensor Integration with Deep Learning for Predicting Silica Aerogel Properties in Real Time The continuous global effort to predict material properties through artificial intelligence has predominantly focused on utilizing material stoichiometry or structures in deep learning models. This study aims to predict material properties using electrochemical impedance data, along with frequency and time parameters, that can be obtained during processing stages. The target material, silica aerogel, is widely recognized for its lightweight structure and excellent insulating properties, which are attributed to its large surface area and pore size. However, production is often delayed due to the prolonged aging process. Real-time prediction of material properties during processing can significantly enhance process optimization and monitoring. In this study, we developed a system to predict the physical properties of silica aerogel, specifically pore diameter, pore volume, and surface area. This system integrates a 3 × 3 array Pd/Au sensor, which exhibits high sensitivity to varying pH levels during aerogel synthesis and is capable of acquiring a large data set (impedance, frequency, time) in real-time. The collected data is then processed through a deep neural network algorithm. Because the system is trained with data obtained during the processing stage, it enables real-time predictions of the critical properties of silica aerogel, thus facilitating process optimization and monitoring. The final performance evaluation demonstrated an optimal alignment between true and predicted values for silica aerogel properties, with a mean absolute percentage error of approximately 0.9%. This approach holds great promise for significantly improving the efficiency and effectiveness of silica aerogel production by providing accurate real-time predictions. Aerogel ; Materials science ; Nanotechnology Aerogels and thermal insulation ; Advanced Fiber Optic Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Maicon Pierre Lourenço | Mosayeb Naseri | Lizandra Barrios Herrera | Hadi Zadeh‐Haghighi | Daya Ram Gaur | Christoph Simon | Dennis R. Salahub
Source: Journal of the Brazilian Chemical Society (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Quantum Active Learning for Structural Determination of Doped Nanoparticles - A Case Study of 4Al@Si11 Active learning (AL) has been widely applied in chemistry and materials science. In this work, we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by Density Functional Theory (DFT) or Density Functional Based Tight Binding (DFTB) and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/ Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si11 with 4 Al (4Al@Si11) and the results indicate the QAL method is able to find the optimum 4Al@Si11 structure. The aim of this work is to present the QAL method, formulated in a noise-free quantum computing framework, for automatic structural determination of doped nanoparticles and materials defects. Doping ; Nanoparticle ; Quantum chemical ; Quantum dot ; Nanotechnology ; Chemistry ; Materials science ; Optoelectronics ; Molecule ; Organic chemistry Boron and Carbon Nanomaterials Research ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science
Authors: Can Polat | Hasan Kurban | Mustafa Kurban
Source: Physica Scripta (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Enabling Ease of Access to Quantum Chemistry with Transformer-Based Text Encoding and Physics-Informed Multilayer Perceptron Abstract Accurate prediction of large-scale material properties is a critical challenge in materials science, driven by the need for efficient computational methods to explore and design advanced materials for various applications. Traditional approaches often rely on quantum chemistry simulations, which are computationally intensive and require domain-specific expertise. This work introduces Text-To-Eigenvalue (T2E), an innovative approach that combines text-to-vector encoding with a multilayer perceptron for rapid and precise material simulations. T2E converts key material attributes into vector representations, processed through an MLP framework enriched with critical physical data. This fusion of textual, physical, and quantum information enables T2E to predict material properties efficiently and accurately, significantly reducing the computational burden typically associated with particle interactions and eliminating the need for extensive quantum chemistry expertise. Rigorous validation across diverse elements and small molecules demonstrates that T2E outperforms state-of-the-art (SOTA) models like FermiNet and PsiFormer. Moreover, T2E successfully predicts multiple physical properties using the benchmark molecule datasets, such as QM9 and MD17, underscoring its versatility and effectiveness when compared SOTA graph neural networks. Computer science ; Benchmark (surveying) ; Quantum ; Theoretical computer science ; Physics ; Cartography Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Yu Yang | Mingliang Deng | Tianhao Rui | Zhuangzhuang Ma | Linyuan Lu | Yunhao Wang | Tao Lan | Yulin Lan | Hengcheng Wan | Yiyan Li | Zhipeng Li | Haibin Zhang
Source: Crystals (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Formation Energy Prediction of Doped Perovskite Structures Based on Transfer Learning with Small Datasets Doped perovskites are widely studied in the domain of perovskite material design. However, due to the limited data available for the target materials, machine learning methods based on small datasets become particularly important. In this study, we propose a transfer learning strategy aimed at predicting doped perovskites on limited data samples. This strategy first utilizes the ABO3-type perovskite dataset to develop a deep learning source model based on its formation energies. Then, fine-tuning is performed on the doped perovskite structure dataset to obtain a model with good transferability, applicable to the doped perovskite oxide target domain. Based on the transfer learning model, we further predict the formation energies of 12,897 A2BB′O6 compounds, 10,401 AA′B2O6 compounds, and 49,723 AA′BB′O6 compounds. With the tolerance factor t ∈ [0.7–1.1], octahedral factor μ ∈ [0.45–0.7], and the modified tolerance factor τ ∈ [0, 4.18] for screening, we successfully predict 3389 A2B′BO6, 3002 AA′B2O6, and 13,563 AA′BB′O6 structures as potential stable doped perovskite candidates. Among these filtered results, 821 A2B′BO6, 69 AA′B2O6, and 6 AA′BB′O6 compounds have been reported in the OQMD database. For each doped perovskite, we select the candidate with the lowest formation energy and perform DFT validation. This resulted in three newly reported stable doped perovskite materials: CaSrHfScO6, BaSrHf2O6, and Ba2HfNdO6. The transfer learning-based perovskite material design method proposed in this study not only effectively addresses the challenges of model training on small datasets but also significantly improves the accuracy and stability of doped perovskite material predictions. Through transfer learning, the model can fully leverage the data and knowledge from the ABO3-type perovskite, effectively overcoming the problem of limited data. This strategy provides a new approach for efficient perovskite material design, enabling broader structural and performance predictions under limited experimental data conditions, and offering a powerful tool for the development of novel functional materials. Perovskite (structure) ; Leverage (statistics) ; Doping ; Transfer of learning ; Stability (learning theory) ; Energy (signal processing) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Corin Wagen
Source: ChemRxiv (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning Enables Rapid Macroscopic pKa Prediction Accurate prediction of macroscopic pKa values remains a central challenge in computational chemistry, critical for modeling pH-dependent properties like solubility, membrane permeability, and charge state. Here we introduce Starling, a physics-informed neural network based on the Uni-pKa architecture trained to predict per-microstate free energies and compute macroscopic pKavalues via thermodynamic ensemble modeling. Unlike approaches that treat protonation events in isolation, Starling explicitly resolves protonation and tautomeric microstates, enabling robust handling of complex molecules with multiple ionizable sites. We show that Starling achieves comparable or superior accuracy to leading commercial tools on multiple benchmark datasets, and demonstrate its utility in predicting isoelectric points, logD profiles, and blood–brain-barrier permeability. By maintaining thermodynamic consistency and enabling rapid microstate-ensemble generation, Starling enables accurate physicochemical property prediction with broad relevance to drug discovery and molecular design. Machine learning ; Artificial intelligence ; Computer science Machine Learning in Materials Science
Authors: Mengxin Yang | Shiyu Wang | Guang‐Ling Song | Long Cheng | Haisheng Ren
Source: The Journal of Physical Chemistry A (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

CBH-BDC Enhanced Δ-ML for Predicting the Accurate Standard Enthalpy of Formation The standard enthalpy of formation (Δ<sub>f</sub><i>H</i>°) is a fundamental thermodynamic property that is essential for understanding various physicochemical processes. Our group recently developed the connectivity-based hierarchy with the bond difference correction (CBH-BDC) method for calculating the accurate Δ<sub>f</sub><i>H</i>°. However, it encounters challenges in high-accuracy electron energy calculations and is restricted by BDC parameters that are limited to specific elements. In this work, we introduce a CBH-BDC enhanced delta machine learning (Δ-ML) approach that utilizes effective and interpretable molecular descriptors derived from connection-based hierarchy fragments and BDC, enabling the accurate prediction of Δ<sub>f</sub><i>H</i>° while bypassing high-level quantum calculations. The approach is validated using 464 species with experimental Δ<sub>f</sub><i>H</i>° and applied to extrapolate Δ<sub>f</sub><i>H</i>° from density functional theory (DFT) accuracy to CCSD(T) accuracy for 120,416 stable organic molecules in the QM9 database. It demonstrates significant improvements in accuracy, enabling the construction of a high-quality Δ<sub>f</sub><i>H</i>° database for chemical deep learning. Enthalpy ; Standard enthalpy change of formation ; Standard enthalpy of formation ; Thermodynamics ; Chemistry ; Materials science ; Physics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Xingyu Wang | Yu Mao | Ziyun Wang
Source: Chem Catalysis (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Machine learning approaches for transition state prediction ML approaches have revolutionized transition state searching, evolving from traditional methods to sophisticated architectures. Recent advances, particularly in diffusion models and neural networks, have dramatically reduced computational time while maintaining accuracy. While data limitations and validation standards present ongoing challenges, these emerging methods offer promising solutions for understanding complex reaction mechanisms and exploring chemical reaction landscapes more efficiently. Transition (genetics) ; State (computer science) ; Machine learning ; Artificial intelligence ; Computer science ; Chemistry ; Algorithm Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Shengbo Ling | Jie Mei | Yubin Lu | Wu Zhen
Source: Composite Structures (article, 2025-06-27)
Field: Materials Science — Polymers and Plastics

A multi-fidelity data-driven model for predicting stress-strain relationships of thermoplastic polymers Thermoplastic ; Materials science ; Stress (linguistics) ; Stress–strain curve ; Strain (injury) ; Polymer ; Fidelity ; Composite material ; Thermoplastic polymer ; Structural engineering ; Computer science ; Deformation (meteorology) ; Engineering Polymer crystallization and properties ; Mechanical Behavior of Composites ; Probabilistic and Robust Engineering Design
Authors: Juan Iván Gómez-Peralta | Xim Bokhimi | P. Quintana
Source: Scripta Materialia (article, 2025-04-12)
Field: Materials Science — Materials Chemistry

Insights from the reciprocal space revealed by a convolutional neural network and transfer learning Reciprocal ; Materials science ; Convolutional neural network ; Transfer of learning ; Reciprocal lattice ; Space (punctuation) ; Artificial intelligence ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Neural Networks and Applications ; Image Processing and 3D Reconstruction
Authors: Yi Wang | Yingchao Xu | Xiangyou Wang | Hailong Wang | Haipeng Liu | Shuwei Liu
Source: Food Control (article, 2025-05-29)
Field: Chemistry — Analytical Chemistry

A deep learning-based algorithm for online detection and evaluation of starch content in multiple potato varieties Starch ; Computer science ; Content (measure theory) ; Artificial intelligence ; Machine learning ; Algorithm ; Food science ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Potato Plant Research
Authors: Yongxiang Li | Xiao Liu | Xiangdong Wang | Wei Xie | Di Qiu | Jiong Yang
Source: Journal of Materials Informatics (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Effect of material properties on the thermal responses of the carbonization and pyrolysis layers of polymer matrix composites for charring-ablators Ablative materials, a special type of thermal protection material, are widely used in extremely high-temperature environments such as hypersonic vehicles and re-entry capsules. They effectively mitigate heat conduction to the interior through ablation at the material surface. Based on traditional physical models and machine learning techniques, we systematically investigated the mapping relationship between multiple material parameters and thermal responses within the carbonized layer and pyrolysis layer of ablative materials. By employing high-throughput physical modeling and the sure independence screening and sparsity operator (SISSO) method for feature selection, we first revealed that the thermal responses of different layers are dominated by distinct material properties (e.g., density, thermal conductivity, heat capacity, etc. ). The explicit relationships between the functioning material parameters and the features of the thermal response curves associated with single-/double-layer structures are well established. After the key parameter screening based on SISSO, we further developed a deep neural network surrogate model, capable of accurately predicting the entire thermal response process within the carbonized and pyrolysis layers. Charring ; Carbonization ; Pyrolysis ; Materials science ; Composite material ; Polymer ; Matrix (chemical analysis) ; Thermal ; Waste management ; Engineering Thermal and Kinetic Analysis ; Thermal properties of materials ; Tribology and Wear Analysis
Authors: Ramona Crainic | Petru Păşcuţă | Florin Popa | Radu Fechete
Source: Materials (article, 2025-10-25)
Field: Materials Science — Biomaterials

NMR, FT-IR, XRD, SEM, and ANN Complex Characterization of Some Nonwoven Materials Produced by Electrospinning Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (<sup>1</sup>H), for which the spin-spin relaxation time distributions <i>T</i><sub>2</sub> were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY <i>T</i><sub>2</sub>-<i>T</i><sub>2</sub> and COSY <i>T</i><sub>1</sub>-<i>T</i><sub>2</sub> exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric. Electrospinning ; Nanofiber ; Polyvinyl alcohol ; Characterization (materials science) ; Polymer ; Porosity ; Polyethylene glycol ; Drug delivery Electrospun Nanofibers in Biomedical Applications ; NMR spectroscopy and applications ; Cancer Research and Treatment