jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas E. Robertson | Samuel B. Inman | Ashley Lenau | Ricardo A. Lebensohn | Dongil Shin | Brad Boyce | Remi Dingreville
Source: ArXiv.org (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior. The VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture. Using an analytic propagation scheme based on Taylor-series expansion and automatic differentiation, the VDMN efficiently propagates uncertainty through the network during training and prediction. We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties. Together, these results establish the VDMN as a foundation for uncertainty-robust materials digital twins. Uncertainty quantification ; Nonlinear system ; Probabilistic logic ; Calibration ; Embedding ; Inverse problem ; Artificial neural network ; Inverse ; Propagation of uncertainty Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Matthias Höfler | Francesco Regazzoni | Stefano Pagani | Elias Karabelas | Christoph M. Augustin | Gundolf Haase | Gernot Plank | Federica Caforio
Source: ArXiv.org (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical properties of the tissue. The accurate assessment of active stress parameters is fundamental for a precise understanding of myocardial function but remains difficult to achieve in a clinical setting, especially when only displacement and strain data from medical imaging modalities are available. This work investigates, through an in-silico study, the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models from these types of imaging data. In particular, by parametrising the sought state and parameter field with two neural networks, respectively, and formulating an energy minimisation problem to search for the optimal network parameters, we are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution. To this end, we also advance the vanilla PINN learning algorithm with the use of adaptive weighting schemes, ad-hoc regularisation strategies, Fourier features, and suitable network architectures. In addition, we thoroughly analyse the influence of the loss weights in the reconstruction of active stress parameters. Finally, we apply the method to the characterisation of tissue inhomogeneities and detection of fibrotic scars in myocardial tissue. This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis. Artificial neural network ; Biomechanics ; Weighting ; Cardiac electrophysiology ; Contractility ; Displacement field ; Stress (linguistics) ; Active contour model ; Bidomain model Machine Learning in Materials Science ; Elasticity and Material Modeling ; Cardiovascular Function and Risk Factors
Authors: Lionel Saudan (2644183) | Anna Xiao Tan (22625593) | Daniel Pacheco Gutierrez (22625596) | Laurent Maggi (22625599) | Loïc Roch (22625602) | Clélia Fantini (22625605) | Eric Walther (22625608) | Julien Coulomb (2718961) | Francesco Santoro (2426011)
Source: Figshare (article, 2025-11-15)
Field: Chemistry — Organic Chemistry

Bayesian Optimizationfor Resource-Efficient Hydroformylation Hydroformylation remains a cornerstone transformation in industrial catalysis, especially for the production of fine chemicals, fragrances, and pharmaceuticals. However, achieving high linear selectivity in the hydroformylation of vinyl arenes remains challenging due to intrinsic substrate preferences and the high cost of rhodium-based catalysts. In this work, we employed Bayesian optimization (BO) to explore a seven-dimensional (7D) space of reaction conditions, spanning catalyst type and loading, ligand identity and loading, solvent, temperature, and pressure for a total of ∼2.9 billion possible combinations, to enhance selectivity toward the linear aldehyde at reduced catalyst usage and reaction time within just 88 experiments. Despite the vast search space, our optimization campaign using the <i>Atinary FalconGPBO</i> algorithm was able to maintain conversion and linear selectivity at a halved reaction time and with a 10–30 times reduction in rhodium catalyst loading (from 0.1 to <0.01 mol %) compared to high-loading prescreening conditions. Specifically, for two promising ligands, we were able to reduce the rhodium cost contribution by 95% (from 102 to 5 €/kg of product) and 97% (from 127 to 4 €/kg of product). This work showcases the potential of using machine learning (ML) tools such as BO to accelerate reaction optimization, improve process efficiency, and reduce resource consumption in fine chemical production. Hydroformylation ; Rhodium ; Selectivity ; Catalysis ; Bayesian optimization ; Aldehyde ; Ligand (biochemistry) ; Bayesian probability Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Parthasarathi Mishra
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

AI and Computational Intelligence Methods for Modeling and Optimization of Fiber-Reinforced Metal and Polymer Matrix Composites The advancement of fiber-reinforced composites has revolutionized the design and development of high- performance materials for aerospace, automotive, and structural applications. However, the complexity of composite systems—arising from heterogeneous microstructures, multiple reinforcement mechanisms, and nonlinear mechanical behavior—poses challenges in predictive modeling and performance optimization. Recent developments in artificial intelligence (AI) and computational intelligence (CI) have enabled efficient modeling, simulation, and optimization of these materials by integrating machine learning, deep learning, and evolutionary algorithms. This study reviews and analyzes AI-driven methods applied to fiber-reinforced metal matrix composites (MMCs) and polymer matrix composites (PMCs), emphasizing data-driven prediction of mechanical, thermal, and tribological properties. The paper further explores hybrid computational models combining finite element analysis (FEA) with neural networks, genetic algorithms (GA), and fuzzy logic to achieve enhanced predictive accuracy and process parameter optimization. The proposed framework demonstrates the potential of AI–CI methods in accelerating material design cycles and optimizing composite fabrication processes with improved precision, reduced experimental cost, and higher reliability. Computational intelligence ; Metal matrix composite ; Finite element method ; Artificial neural network ; Fuzzy logic ; Matrix (chemical analysis) ; Computational model ; Process (computing) ; Genetic algorithm Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Jinrong Huo (10657343) | Pengfei Liu (136115) | Yuxin Tang (639327) | Kai Zhang (102844) | Chaozheng He (1768702)
Source: Figshare (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Prediction of NewCrystal of Titanium Oxides and PropertyCalculations Using Structure Prediction Software and First-PrinciplesTheory The crystal structure prediction software CBD-GM, based on the Bayesian optimization algorithm combined with deep learning, was used to predict the crystal structure of titanium oxide. One known bulk structure and three new structures were predicted. The first-principles of density functional theory (DFT) have been used to calculate and analyze the properties associated with three new material structures. The results show that these structures have outstanding thermodynamic and kinetic stability. The elastic constants, bulk modulus, and Young’s modulus indicate that the structure materials have excellent mechanical properties. Calculation of the energy band structure using hybrid density flooding shows that the prediction structures are all broadband semiconductors and also have a high dielectric constant and high optical response in the UV region, which has great scope for development in semiconductor devices and deep exotic optoelectronics, providing a completely new research direction for the development and experimental synthesis of new structures. Crystal structure prediction ; Crystal (programming language) ; Crystal structure ; Semiconductor ; Density functional theory ; Dielectric ; Material properties ; Titanium ; Bulk modulus Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Lionel Saudan | Adrian Tan | Daniel Pacheco Gutiérrez | Laurent Maggi | Loı̈c M. Roch | Clélia Fantini | Eric Walther | J Coulomb | Francesco Santoro
Source: ACS Catalysis (article, 2025-11-15)
Field: Chemistry — Organic Chemistry

Bayesian Optimization for Resource-Efficient Hydroformylation Hydroformylation remains a cornerstone transformation in industrial catalysis, especially for the production of fine chemicals, fragrances, and pharmaceuticals. However, achieving high linear selectivity in the hydroformylation of vinyl arenes remains challenging due to intrinsic substrate preferences and the high cost of rhodium-based catalysts. In this work, we employed Bayesian optimization (BO) to explore a seven-dimensional (7D) space of reaction conditions, spanning catalyst type and loading, ligand identity and loading, solvent, temperature, and pressure for a total of ∼2.9 billion possible combinations, to enhance selectivity toward the linear aldehyde at reduced catalyst usage and reaction time within just 88 experiments. Despite the vast search space, our optimization campaign using the Atinary FalconGPBO algorithm was able to maintain conversion and linear selectivity at a halved reaction time and with a 10–30 times reduction in rhodium catalyst loading (from 0.1 to <0.01 mol %) compared to high-loading prescreening conditions. Specifically, for two promising ligands, we were able to reduce the rhodium cost contribution by 95% (from 102 to 5 €/kg of product) and 97% (from 127 to 4 €/kg of product). This work showcases the potential of using machine learning (ML) tools such as BO to accelerate reaction optimization, improve process efficiency, and reduce resource consumption in fine chemical production. Hydroformylation ; Rhodium ; Catalysis ; Selectivity ; Bayesian optimization ; Aldehyde ; Ligand (biochemistry) ; Substrate (aquarium) Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Dasari Soyasree
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-05-12)
Field: Chemistry — Analytical Chemistry

Identification of Different Medicinal Plants/ Raw Materials Through Image Processing Using Machine Learning Algorithms Problem statement Identifying medicinal plants and their raw materials precisely and efficiently is essential for industries such as pharmaceutical, nutraceutical, cosmetics and traditional medicine products. Traditionally, this process depends on the expert knowledge of the appearance, structure and classification of a plant. However, these methods are often slow, intensive in labor and can be prone to errors. In many remote areas or resources little resources, where a large number of valuable medicinal plants grow, qualified botanists cannot be available. This can lead to erroneous identification or adulteration of plant materials, which not only reduces the effectiveness of products, but can also raise serious health risks and cause financial losses. To address these challenges, this research focuses on developing a faster, more reliable and profitable solution through the use of image processing and automatic learning to automate the identification of medicinal plants and their raw forms. Identification (biology) ; Computer science ; Image processing ; Artificial intelligence ; Image (mathematics) ; Computer vision ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Traditional Chinese Medicine Studies
Authors: Cristóbal Galleguillos Ketterer | José Luis Valín Rivera | María Elena Fernández | Nicolás Norambuena | Meylí Valin Fernández
Source: Applied Sciences (article, 2025-08-20)
Field: Materials Science — Polymers and Plastics

A Hybrid Experimental–Machine Learning Framework for Designing Fire-Resistant Natural Fiber Composites This work presents an integrated experimental and machine learning study on the fire performance of sisal fiber-reinforced polyester composites treated with magnesium hydroxide as a flame retardant. A total of 43 small-scale fire resistance tests were conducted in a custom-built gas-fired furnace following ISO 834 and NCh935/2 standards. Key parameters—including fiber content, flame retardant proportion, catalyst, and accelerator—were correlated with burn time and mass loss. Linear regression revealed negligible to weak correlations, while nonlinear models (Random Forest, Support Vector Regression, and Deep Neural Network) showed improved predictive capacity. The Deep Neural Network achieved the best performance (MSE = 0.061, R2 = 0.334). Experimental results confirm that magnesium hydroxide substantially increases burn time, whereas sisal fiber content alone has a minimal effect on fire resistance. This study highlights an affordable strategy for enhancing the fire safety of bio-based composites and demonstrates the potential of machine learning to optimize material formulations. Future research should expand the dataset and validate the models through standardized large-scale fire tests. However, the findings are limited to small-scale fire resistance tests under controlled laboratory conditions and should not be generalized to full-scale structural applications without further validation. Materials science ; Composite material Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: 直希 野平 | Naoki Nohira | 太一 市澤 | Taichi Ichisawa | 正樹 田原 | Masaki Tahara | 逸夫 熊澤 | Itsuo Kumazawa | 秀樹 細田 | Hideki Hosoda
Source: Institutional Repositories DataBase (IRDB) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of the mechanical properties of β titanium shape memory alloys Shape-memory alloy ; Titanium alloy ; Titanium ; Deformation (meteorology) ; Nickel titanium Titanium Alloys Microstructure and Properties ; Shape Memory Alloy Transformations ; Machine Learning in Materials Science
Authors: Tao Huang | Zhen-Feng Tong | Yiding Wang | Shuyi Jiang | Lu Sun
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of alloying element effects on irradiation swelling in austenitic steels Swelling ; Austenite ; Irradiation ; Alloy ; Neutron ; Neutron irradiation ; Neutron flux Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Johannes Lindenblatt | M. Mei | Rüdiger Daub
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Chemistry — Analytical Chemistry

Inline Residual Moisture Measurement of Electrodes in Lithium-Ion Battery Production Air drying in the production environment constitutes a significant part of the manufacturing cost of battery production. Most manufacturing processes in battery production require a dry air environment; otherwise, reactions between moisture and battery materials, especially active material and electrolyte, can adversely affect the battery life. Current industrial research focuses on smaller drying rooms to save cost. However, with fully automated and digitized production processes, more inline measurement tools are needed to ensure quality and efficiency. Inline residual moisture measurement during the battery production process is, therefore, crucial. The potential influencing factors of moisture in electrodes were evaluated. Near Infrared Spectroscopy was used to detect the residual moisture of electrodes. Images from a hyperspectral camera and measurements with a near infrared spectrometer were compared. Based on Karl-Fischer-Titration results, Machine Learning models were trained to predict the residual moisture using these non-destructive sensors. Multivariate image analysis techniques were required to handle the large data sets of the hyperspectral camera, and an optimal model using artificial intelligence was developed. The robustness of the Machine Learning models was further tested in moving samples to align with the actual battery manufacturing setting. Generally, inline residual moisture measurements using hyperspectral imaging combined with near infrared spectroscopy was feasible in a dry-room atmosphere. For atmospheres with higher moisture contents the measuring principle had to much deviation. Hyperspectral imaging ; Residual ; Moisture ; Battery (electricity) ; Near-infrared spectroscopy ; Water content ; Electrode ; Robustness (evolution) Spectroscopy and Chemometric Analyses ; Advanced Battery Technologies Research ; Microwave and Dielectric Measurement Techniques
Authors: José Ferraz-Caetano (16859533) | Filipe Teixeira (1720633) | M. Natália D. S. Cordeiro (8212974)
Source: Figshare (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Optimizing Vanadium-CatalyzedEpoxidation Reactions:Machine-Learning-Driven Yield Predictions and Data Augmentation Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds. This study presents an innovative supervised machine learning (ML) model to predict the reaction yield of the vanadium-catalyzed epoxidation of small alcohols and alkenes. Our framework uncovers relevant chemical characteristics for structure design, offering a pathway for automated optimization of epoxidation reactions. The study also incorporates the concept of data augmentation, handling experimental variability by generating synthetic reactions to densify under-represented data segments. Trained on a curated data set of 273 experimental epoxidation reactions with vanadyl catalyst groups, the model achieved a predictive <i>R</i><sup>2</sup> test score of 90%, with a mean absolute yield prediction error of 4.7%. The ML model offers a high degree of explainability, as descriptor analysis identified key experimental and chemical descriptors that influence catalytic reaction predictions. This represents a significant development in catalytic epoxidation studies, highlighting the critical role of data science in reaction research and catalyst optimization. Yield (engineering) ; Catalysis ; Experimental data ; Training set ; Chemical reaction ; Key (lock) ; Set (abstract data type) ; Reaction conditions Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Polyoxometalates: Synthesis and Applications
Authors: Yong Chen (109188) | Zihao Wang (845023) | Chenglin Chang (17136710) | Weifeng Shen (4333636)
Source: Figshare (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Active Learning-OptimizedDynamic Sampling Strategyfor Enhanced Toxicity Prediction of Chemical Compounds Toxicity prediction of chemical compounds is critical for environmental risk mitigation and safe material synthesis, particularly in catalytic reactor safety, solvent screening for sustainable processes, industrial material hazard assessment, and chemical product design. Recent advancements in machine learning (ML) have enabled efficient and reliable prediction of chemical compounds through data-driven modeling approaches. However, the generalization ability of models constructed using ML methods is largely constrained by the quality of the original experimental data sets and the complexity of feature engineering. This work proposes a novel dynamic data sampling approach based on the idea of active learning, which is derived from ML methods. The proposed approach entails quantifying chemical space distances and employing visualization methods to detect outlier samples. Subsequently, multiple rounds of weighted training and dynamic sample weight adjustment mechanisms were employed to achieve dynamic sampling. Threshold-based selection was implemented to filter samples for upgrading training models, therefore substantially improving the predictive accuracy of new models. When applied to existing toxicity data sets, the proposed sampling approach resulted in a 25.68% enhancement in the accuracy of random forest models based on molecular fingerprints and a 23.92% improvement in the accuracy of convolutional neural network models. These results illustrate that the optimized toxicity prediction models created with the proposed approach show greater accuracy than conventional approaches, demonstrating the capability of the proposed approach as a dependable tool for toxicity assessment in chemical safety evaluation. The interpretative study of molecular features associated with toxicity, performed with this model, provides significant insights for risk mitigation in chemical process and material development. Chemical space ; Chemical toxicity ; Process (computing) ; Chemical process ; Outlier ; Sampling (signal processing) ; Generalization ; Artificial neural network ; Support vector machine ; Chemical industry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems