jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Scottish Science Society | RICHARD MONTGOMERY
Source: Scottish Science Society Periodic (article, 2025-06-26)
Field: Chemistry — Inorganic Chemistry

Bridging Scales in Heterogeneous Catalysis:A Multiscale Computational Framework forOperando Conditions The accurate modelling of heterogeneous catalytic processes under operando con-ditions remains one of the most formidable challenges in computational chemistryand materials science. Traditional approaches employing static surface modelsfail to capture the dynamic nature of active sites, adsorbate-induced reconstruc-tions, and temperature-dependent phenomena that characterise real catalytic sys-tems. This work presents a comprehensive multiscale computational frameworkthat bridges density functional theory (DFT) calculations with kinetic Monte Carlo(kMC) simulations, enhanced by a high-fidelity machine learning potential (MLP),to achieve realistic timescales commensurate with experimental turnover frequen-cies. We demonstrate the methodology through the exemplar case of CO oxidationon Pt(111). The framework reveals previously unidentified dynamic phenomena,including a non-monotonic dependence of activation energy on coverage driven bydynamic site creation and temperature-induced surface restructuring. The frame-work successfully predicts turnover frequencies within an order of magnitude ofexperimental values across a wide temperature range, a significant improvementover static models. Our results indicate that incorporating surface dynamics andrealistic reaction conditions fundamentally alters predicted catalytic behaviour, sug-gesting that static models may systematically misrepresent catalytic mechanisms.This methodology opens new avenues for rational catalyst design based on operandobehaviour rather than idealised surface structures. Bridging (networking) ; Computer science Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: I. I. Khasanov | A. N. Alyunov | K. A. Gorshkov | E. F. Veliyev
Source: Proceedings of OilGasScientificResearchProjects Institute SOCAR (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Application of long short-term memory neural networks for predicting paraffin deposition in crude oil pipelines The paper investigates the application of long short-term memory (LSTM) neural networks for forecasting the formation of asphaltene, resin, and paraffin deposits (ARPD) during crude oil transportation through trunk pipelines. The accumulation of ARPD represents a serious operational issue that reduces pipeline throughput, increases pumping energy consumption, and complicates inspection and maintenance procedures. A detailed overview of conventional mathematical and software-based approaches for predicting wax deposition is provided, highlighting their limitations in terms of adaptability and accuracy. Taking into account the properties of various neural network architectures and the specifics of available input data, an LSTM-based predictive model was developed and trained using datasets simulated in the Schlumberger OLGA software package for typical operating conditions of pipelines in the Republic of Bashkortostan. The model achieved mean absolute error (MAE) and root mean squared error (RMSE) values of 0.1163 and 0.1564, respectively, demonstrating its ability to reproduce ARPD thickness along the pipeline with satisfactory precision. The results confirm that the proposed neural network approach can serve as a reliable alternative to conventional physics-based models. The framework can be further improved by incorporating additional parameters such as crude oil composition variability and the effect of chemical inhibitors. The developed predictive tool can be used by pipeline operators to optimize cleaning schedules and improve the efficiency of crude oil transport systems. Overall, the findings contribute to enhancing digital modeling practices in oil pipeline management. Keywords: recurrent neural networks; asphaltenes; resins; paraffin deposits; forecasting; long short-term memory; trunk pipelines; oil transport. Pipeline transport ; Artificial neural network ; Pipeline (software) ; Crude oil ; Adaptability ; Mean squared error ; Approximation error ; Deposition (geology) Petroleum Processing and Analysis ; Engineering Diagnostics and Reliability ; Enhanced Oil Recovery Techniques
Authors: Dudakov, Ivan V. | Radzikovitsky, Pavel M. | Popov, Dmitry S. | Firsov, Denis A. | Korolev, Vadim V. | Chistikov, Daniil N. | Bochenkov, Vladimir E. | Bochenkova, Anastasia V.
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Machine learning has emerged as a transformative tool for constructing the necessary potential energy surfaces, but applying it to excited states faces a fundamental barrier: the cost of generating high-level quantum chemistry data. We overcome this challenge by developing machine-learning interatomic potentials (MLIPs) that achieve multi-state multi-reference perturbation theory accuracy through various techniques, such as transfer, multi-state, and $Δ$-learning. Applied to the methaniminium cation, our highest-fidelity transfer-learning model uncovers its complete photodissociation landscape following S$_2$ photoexcitation. The comprehensive XMCQDPT2/SA(3)-CASSCF(12,12) electronic structure description captures all competing decay channels, including S$_1$ branching into photoisomerization and direct H$_2$-loss pathways. Our results show that the population dynamics generally depends on the MLIP model, correlating with its performance. At the same time, the introduction of MLIP-uncertainty corrections based on the predictions of an ensemble of models brings different approaches into agreement, validating this metric as essential for reliable dynamics. To interpret the population dynamics, we introduce a wavepacket oscillation model - a mechanistically transparent, power-law kinetics framework that extracts state-specific lifetimes directly from first-principles simulations. The model quantitatively reproduces the ultrafast decay, creating a direct link between quantum transition probabilities and classical rate constants. The kinetic fits yield channel-specific lifetimes, supporting the recently discovered photochemical pathway mediated by a novel $σπ^*/S_0$ conical intersection. Wave packet ; Conical intersection ; Excited state ; Population ; Quantum ; Photodissociation ; Oscillation (cell signaling) ; Kinetic energy ; Photoisomerization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Dudakov, Ivan V. | Radzikovitsky, Pavel M. | Popov, Dmitry S. | Firsov, Denis A. | Korolev, Vadim V. | Chistikov, Daniil N. | Bochenkov, Vladimir E. | Bochenkova, Anastasia V.
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Machine learning has emerged as a transformative tool for constructing the necessary potential energy surfaces, but applying it to excited states faces a fundamental barrier: the cost of generating high-level quantum chemistry data. We overcome this challenge by developing machine-learning interatomic potentials (MLIPs) that achieve multi-state multi-reference perturbation theory accuracy through various techniques, such as transfer, multi-state, and $Δ$-learning. Applied to the methaniminium cation, our highest-fidelity transfer-learning model uncovers its complete photodissociation landscape following S$_2$ photoexcitation. The comprehensive XMCQDPT2/SA(3)-CASSCF(12,12) electronic structure description captures all competing decay channels, including S$_1$ branching into photoisomerization and direct H$_2$-loss pathways. Our results show that the population dynamics generally depends on the MLIP model, correlating with its performance. At the same time, the introduction of MLIP-uncertainty corrections based on the predictions of an ensemble of models brings different approaches into agreement, validating this metric as essential for reliable dynamics. To interpret the population dynamics, we introduce a wavepacket oscillation model - a mechanistically transparent, power-law kinetics framework that extracts state-specific lifetimes directly from first-principles simulations. The model quantitatively reproduces the ultrafast decay, creating a direct link between quantum transition probabilities and classical rate constants. The kinetic fits yield channel-specific lifetimes, supporting the recently discovered photochemical pathway mediated by a novel $σπ^*/S_0$ conical intersection. Wave packet ; Conical intersection ; Excited state ; Population ; Quantum ; Photodissociation ; Oscillation (cell signaling) ; Kinetic energy ; Photoisomerization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Eymana Maria | Jonathan J. Denney | Yusu Wang | Peter G. Khalifah | Katsuyo Thornton
Source: Crystal Growth & Design (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible. Heat transfer ; Materials science ; Geometry ; Distribution (mathematics) ; Crystal growth ; Sample (material) ; Crystal (programming language) ; Crystallography ; Optics ; Chemistry ; Mechanics ; Thermodynamics ; Mathematics ; Physics ; Computer science Solidification and crystal growth phenomena ; ZnO doping and properties ; Metallurgical Processes and Thermodynamics
Authors: Jie Zhao (49409) | Changqing Dong (2450665) | Junjie Xue (1265085) | Xiaoying Hu (2079796) | Junjiao Zhang (4246519)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Prediction of OxygenVacancy Formation Energy in OxidesBased on the Graph Neural Network Approach Oxygen vacancy formation energy (<i>E</i><sub>ovf</sub>) prediction plays an important role in addressing the issue of performance evaluation in various applications of materials. In this study, a convolutional and attentional graph neural network (CAGNN) was proposed for the prediction of charge-neutral <i>E</i><sub>ovf</sub> at arbitrary oxygen sites of oxide crystals. The crystal structure and atomic properties were the only needed input. For the elements in the training set distribution, the mean absolute error (MAE) of predicting <i>E</i><sub>ovf</sub> with CAGNN was 0.50 eV. The computational cost of CAGNN was significantly reduced compared to density functional theory (DFT). The prediction of <i>E</i><sub>ovf</sub> (oxides containing n cationic elements, <i>n</i> = 2, 3, 4) with the CAGNN model trained by the data of oxides containing <i>m</i> (<i>m</i> < <i>n</i>) cationic elements were carried out, which validated the model’s generalization and extrapolation performance. Finally, through the materials rediscovery tests, CAGNN shows promising application prospects in material discovery for fields with a strong correlation to the <i>E</i><sub>ovf</sub>. Extrapolation ; Artificial neural network ; Generalization ; Convolutional neural network ; Vacancy defect ; Graph ; Density functional theory ; Oxygen ; Training set Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Michael R. Hoffmann | Sudarsan Manjunatha Pai | Rodrigo Q. Albuquerque | Simon Kastl | Holger Ruckdäschel
Source: EPub Bayreuth (University of Bayreuth) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Active Learning-Driven Inverse Design of Polyurethane Foams for EV Battery Applications The rapid evolution of the electric vehicle (EV) industry demands advanced materials for battery protection, with polyurethane (PUR) foams emerging as a promising solution due to their thermal insulation, mechanical adaptability, and fire resistance properties. This study introduces an active learning-driven inverse design (AL-ID) framework, leveraging machine learning (ML) to systematically optimize PUR foam compositions exhibiting desired density and mechanical strength. AL was employed to iteratively refine the ML model by targeting high-uncertainty regions, reducing experimental effort while improving predictive accuracy. Bayesian optimization (BO) further enhanced the search for optimal compositions by balancing exploration and exploitation. The framework demonstrated significant improvements in model performance, with Mean Absolute Error (MAE) and R 2 scores for density and mechanical strength predictions efficiently improving as the dataset grew. Besides successfully selecting 11 good material candidates out of 616,008 virtual compositions, the final ML models have shown small MAE values and good R 2 scores. This study underscores the potential of ML-driven frameworks to accelerate material discovery. Inverse ; Battery (electricity) ; Bayesian optimization ; Inverse problem ; Polyurethane ; Thermal ; Bayesian probability Machine Learning in Materials Science ; Polymer composites and self-healing ; Advanced Battery Technologies Research
Authors: Kevin V. Thomas | Amith Khandakar | Puvaneswaran Chelvanathan | Brahim Aïssa | Mohammad Istiaque Hossain
Source: Scientific Reports (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Enhanced prediction and optimization of thin metal film optical properties using optimized ensemble learning models Thin metal films are essential for expanding sensors, optoelectronic, and photovoltaic technologies. The intricate relationship between material composition, thickness, and production presents significant challenges in optimizing optical properties. The paper introduces an AI-driven framework for the simultaneous prediction and optimization of metal film optical characteristics, such as transmittance(%T), reflectance(%R), and absorptance(%A) using a dataset of 1320 experimentally measured samples across material films of gold, aluminum, nickel, tin, copper, and molybdenum over 200-2000 nm wavelength range. The input features to the model include wavelength and material type. Ensemble Models such as Random Forest, Gradient Boosting, XGBoost, and Extra Trees were trained and optimized through GridSearchCV with stratified K-fold cross-validation. A multitask learning model was also implemented to explore potential improvements from joint prediction. Among all models, the CatBoost Regressor demonstrated superior performance, achieving R<sup>2</sup> = 0.99928, MAE = 0.21924, and MSE = 0.28203 on average across all outputs. To enhance interpretability, the feature importance analysis was employed, revealing that Material Type had a slightly more predictive influence than Wavelength. Additionally, material-specific error analysis identified challenging prediction zones tied to spectral extremes. The best performing machine learning model was deployed via a web-based GUI, enabling real-time prediction of thin-film optical properties. Overall, the proposed framework provides a scalable, interpretable, and deployable solution for AI-assisted material design and optical characterization. These findings accelerate thin metal film optimization by offering a reliable data-driven route for quick material property identification and enhancement through machine learning. Random forest ; Ensemble learning ; Ensemble forecasting ; Thin film ; Wavelength ; Feature (linguistics) ; Mean squared prediction error ; Predictive modelling ; Identification (biology) Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Transition Metal Oxide Nanomaterials
Authors: Pietro Novelli | Giacomo Meanti | Pedro J. Buigues | Lorenzo Rosasco | Michele Parrinello | Massimiliano Pontil | Leo H. Bonati
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

Fast and Fourier Features for Transfer Learning of Interatomic Potentials Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight transfer learning framework that extracts atomic descriptors from pretrained graph neural networks and transfer them to new systems using random Fourier features-an efficient and scalable approximation of kernel methods. Franken enables fast and accurate adaptation of general-purpose potentials to new systems or levels of quantum mechanical theory without requiring hyperparameter tuning or architectural modifications. On a benchmark dataset of 27 transition metals, franken outperforms optimized kernel-based methods in both training time and accuracy, reducing model training from tens of hours to minutes on a single GPU. We further demonstrate the framework's strong data-efficiency by training stable and accurate potentials for bulk water and the Pt(111)/water interface using just tens of training structures. Our open-source implementation (https://franken.readthedocs.io) offers a fast and practical solution for training potentials and deploying them for molecular dynamics simulations across diverse systems. Scalability ; Hyperparameter ; Fourier transform ; Transfer of learning ; Artificial neural network ; Kernel (algebra) ; Generalization ; Molecular dynamics ; Benchmark (surveying) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Yaozhong Cui | Qixiang Yan | Yifan Yang | Junnan Ren | Guangyou Chen
Source: International Journal of Protective Structures (article, 2025-11-25)
Field: Materials Science — Polymers and Plastics

Deep learning-based study on fiber stress of basalt fiber grid reinforced wood-cement composite under blast loading Nowadays, energy and environmental issues are becoming increasingly severe, and green, low-carbon, and environmentally friendly building materials are gaining increasing favor. Moreover, to counter the threat of terrorist attacks, research in green civil air defense engineering is urgently required. Basalt fiber grid reinforced wood-concrete composite materials effectively combine the advantages of basalt fiber reinforced polymer (BFRP) and wood-cement, thereby meeting the requirements of mechanical performance and living comfort. To achieve automatic monitoring of fiber stress in basalt fiber grid reinforced wood-concrete composite materials under blast loading, ABAQUS software is used to simulate the blast response of the composite material. By integrating the advantages of Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Kolmogorov-Arnold network (KAN) architecture, a deep fusion neural network model named ConvM-KAN is designed. This model establishes a mapping relationship between the displacement and stress of basalt fibers, enabling accurate fiber stress prediction. The code-based experimental results indicate that the basalt fiber grid can reduce the maximum tensile and compressive damage, as well as displacement deformation of wood-concrete under blast loading by 21.5%, 9.06%, and 24.8%, respectively. The ConvM-KAN model can accurately predict the stress conditions of the fibers, with the coefficient of determination R 2 for stress in the x and y directions reaching 0.998 and 0.992, correspondingly. Compared to standalone CNN and LSTM models, the proposed model exhibits higher accuracy, better generalization performance, and excellent robustness. In summary, this study promotes the use of green and renewable composite materials in civil air defense engineering. Basalt fiber ; Composite number ; Fiber ; Stress (linguistics) ; Deformation (meteorology) ; Displacement (psychology) ; Compressive strength Natural Fiber Reinforced Composites ; Cellular and Composite Structures ; Innovative concrete reinforcement materials
Authors: Alperen Dalkıran | Ahmet Süreyya Rifaioğlu | Rengül Çetin-Atalay | Aybar C. Acar | Tunca Doğan | Volkan Atalay
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-19)
Field: Materials Science — Materials Chemistry

Molecular Contrastive Learning with Graph Attention Network (MoCL-GAT) for Enhanced Molecular Representation Abstract Learning the representation of molecules is crucial for drug discovery but is often hindered by the scarcity of labeled experimental data, which limits the performance of supervised machine learning models. While self-supervised learning (SSL) offers a solution by leveraging vast unlabeled chemical databases, many existing methods focus on learning from either local structural information or global molecular properties, but not both simultaneously. We introduce MoCL-GAT, a novel contrastive and transfer learning-based SSL framework that addresses this gap by simultaneously learning from two complementary objectives. It combines a local contrastive task on molecular subgraphs to capture fine-grained chemical environments with a global predictive task to learn holistic molecular descriptors. This dual-objective approach, powered by a Graph Attention Network, is designed to create more robust, versatile, and transferable molecular representations. Pre-trained on 1.9 million compounds, MoCL-GAT was fine-tuned on diverse benchmarks. It achieved state-of-the-art performance on molecular property prediction tasks, with an AUROC of 0.928 on BBBP and 0.749 on SIDER, and top-ranking RMSEs of 0.570 for ESOL and 1.818 for FreeSolv. Critically, fine-tuned models consistently and significantly outperformed models trained from scratch, confirming the value of pre-training. These results validate that MoCL-GAT’s dual-objective approach learns highly effective and transferable representations, enabling more accurate and data-efficient predictions for key cheminformatics challenges. Cheminformatics ; Graph ; Representation (politics) ; Task (project management) ; Multi-task learning ; Property (philosophy) ; Feature learning ; Molecular graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics