jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haifa Qiu (21486059) | Ming Yang (109148) | Haitao Huang (182705)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

DFT-Machine LearningJoint Exploration of TransitionMetal-Doped Ferroelectric BaTiO<sub>3</sub> for Electrocatalytic HydrogenEvolution Doping regulation holds promise to modulate electrocatalytic performance, yet it remains largely unexplored for ferroelectric (FE) BaTiO<sub>3</sub> (BTO). By jointly employing first-principles calculations and machine learning (ML) analysis, we examine the effect of a broad range of transition metal (TM) doping in FE BTO on the electrocatalytic hydrogen evolution reaction (HER) activity and screen out the optimal TM dopants. We unveil that some early-to-middle group TM (V, Cr, Mo, Ta, Ru)-doped BTO surfaces feature higher synthesizability, which also exhibit noticeable HER activity with |Δ<i>G</i><sub>H*</sub>| < 0.2 eV owing to intermediate hydrogen adsorption strength. Among all doped surfaces, the Mo-doped one shows optimal HER activity under both out-of-plane and in-plane polarization states. We reveal an intense interplay between the hydrogen adsorption configuration and the corresponding hydrogen bonding interaction, which relies more on the TM group than the polarization state. Most importantly, we propose a physically informed descriptor of the surface oxygen <i>p</i> band, which better describes HER activity trends of TM-doped surfaces than conventional band descriptors.This indicates the significance of the fractional filling and bandwidth of occupied oxygen <i>p</i>-band states. Moreover, we establish a robust ML model that can well predict HER activity with surface-independent input parameters alone with <i>R</i><sup>2</sup> value above 0.93. From these parameters, we identify the inherent outer electron number of the TM dopant as the dominant feature, while the second ionization energy of the TM dopant and the initial polarization state show non-negligible feature importance. These findings could enlighten understanding, rational design, and accelerated discovery of element doping of FE materials for catalysis and other implications. Dopant ; Polarization (electrochemistry) ; Ferroelectricity ; Hydrogen ; Doping ; Transition metal ; Adsorption Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Multiferroics and related materials
Authors: Beihan Chen | Zilong Hua | Jennifer K. Watkins | Linu Malakkal | Marat Khafizov | David H. Hurley | Miaomiao Jin
Source: arXiv (Cornell University) (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for predicting the thermal properties of uranium nitride We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential (MTP) framework. The MLIP was trained on density functional theory (DFT) data and validated against various quantities including energies, forces, elastic constants, phonon dispersion, and defect formation energies, achieving excellent agreement with DFT calculations, prior experimental results and our thermal conductivity measurement. The potential was then employed in molecular dynamics (MD) simulations to predict key thermal properties such as melting point, thermal expansion, specific heat, and thermal conductivity. To further assess model accuracy, we fabricated a UN sample and performed new thermal conductivity measurements representative of single-crystal properties, which showed strong agreement with the MLIP predictions. This work confirms the reliability and predictive capability of the developed potential for determining the thermal properties of UN. Thermal conductivity ; Interatomic potential ; Thermal ; Phonon ; Molecular dynamics ; Reliability (semiconductor) ; Work (physics) ; Density functional theory Nuclear Materials and Properties ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Md Inzamam Ul Haque | Ramakrishnan Kannan | Jacob Hinkle | Sylwia A. Stopka | Nathalie Y.R. Agar | Olga S. Ovchinnikova | Debangshu Mukherjee
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Predicting High-Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion Abstract Recent advancements in molecular Mass Spectrometry Imaging (MSI) have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time- of-flight matrix-assisted laser desorption/ionization (MALDI-ToF), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging techniques. By leveraging FT-ICR’s unparalleled spectral resolution and ToF-SIMS’s exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies. Fusion ; Artificial intelligence ; Mass spectrometry ; Sensor fusion ; Superresolution ; Computer science ; Mass spectrometry imaging ; Resolution (logic) ; Pattern recognition (psychology) ; Image resolution ; Chemistry ; Chromatography ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Nikolay A. Tatus’ | Olga Volkova
Source: BIO Web of Conferences (article, 2025-01-01)
Field: Materials Science — General Materials Science

Comparative evaluation of approaches to determining biofibres characteristics: From mechanical tests to machine learning This article is devoted to a comparative analysis of methods for determining the characteristics of biofibers, a key component of modern environmentally friendly composites. The aim of the work is to systematize and compare approaches to the study of biofibers: from mechanical testing and microstructural analysis to digital image processing and machine learning methods. Particular attention is paid to the relationship between fiber characteristics and composite properties. As a result, a classification of methods has been developed, recommendations for their selection for different types of biofibers have been formulated, and promising areas of research have been identified. The analysis has made it possible to identify the optimal combinations of methods for effectively predicting the properties of composite materials. Component (thermodynamics) ; Key (lock) ; Selection (genetic algorithm) ; Feature selection ; Work (physics) Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Heat Transfer and Mathematical Modeling
Authors: Blake
G. Hudson (17283898) | Yueh-Lin Lee (1290285) | Harry W. Abernathy (6028721) | Wissam Saidi (22119460)
Source: Figshare (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Machine Learning-AcceleratedFirst-Principles MolecularDynamics Explains Anomalous Lattice Thermal Expansion in BaZr<sub>0.78</sub>Y<sub>0.22</sub>O<sub>3‑δ</sub> Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein, we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr<sub>0.78</sub>Y<sub>0.22</sub>O<sub>3‑δ</sub> using machine learning-accelerated <i>ab initio</i> molecular dynamics simulations. Our results reproduce the experimentally observed nonmonotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration, two mechanisms that influence the lattice expansion in opposite directions. This work provides fundamental insight into the coupling between hydration thermodynamics and lattice dynamics in BaZr<sub>1–<i>x</i></sub>Y<sub><i>x</i></sub>O<sub>3‑δ</sub>, a prototypical proton-conducting perovskite, and offers a predictive framework for modeling temperature- and humidity-dependent behavior, critical to solid oxide fuel cell performance. Thermal expansion ; Lattice (music) ; Molecular dynamics ; Thermal ; Work (physics) ; Coupling (piping) ; Lattice energy ; Solid oxide fuel cell Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials
Authors: Yi Yang | Arowa Yasmeen | Ovidiu Daescu
Source: IEEE Journal of Photovoltaics (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Uncovering Structure–Performance Relationships in Organic Photovoltaics: Interpretable Machine Learning Model for Predicting the Power Conversion Efficiency Organic photovoltaics (OPVs) represent a promising photovoltaic technology, but the design of candidate molecules has traditionally followed a trial-and-error approach, which is inefficient. However, machine learning provides a data-informed strategy by learning from large OPV material datasets, supporting the accelerated discovery and optimization of high-performance OPV materials. In this study, we use an extreme gradient boosting (XGBoost) model to predict the density functional theory-calculated power conversion efficiency (PCE) of OPV donor materials using structural features extracted from the Harvard Photovoltaic Dataset (HOPV15) dataset. To enhance predictive performance, we select the most informative molecular fingerprints based on the averaged feature importance scores from both random forest and XGBoost. Our XGBoost model achieves state-of-the-art predictive accuracy on HOPV15 with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> = 0.918 and RMSE = 0.302%, outperforming prior methods. Using SHapley Additive exPlanations, we identify key Morgan and PubChem substructures that influence PCE, offering interpretable insights. This framework supports accurate, explainable OPV prediction and holds promise for high-throughput screening. Photovoltaics ; Computer science ; Organic solar cell ; Power (physics) ; Artificial intelligence ; Energy conversion efficiency ; Photovoltaic system ; Machine learning ; Materials science ; Optoelectronics ; Electrical engineering ; Engineering Machine Learning in Materials Science
Authors: Qinglin Wei (8723880) | Jiaxiang Qiu (14676524) | Ruirui Wang (1493308) | Zhongti Sun (2820652) | Yuee Xie (3901618) | Yuanping Chen (3901612) | Yangyang Wan (3354701)
Source: Figshare (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Accelerating BandGap Prediction and High-ThroughputScreening of Covalent Organic Frameworks Based on Transfer Learning Covalent organic frameworks (COFs) are a vast class of materials with nearly infinite structural possibilities, making it challenging to quickly identify COFs with specific properties, particularly their electronic properties. In this study, we apply transfer learning to overcome these limitations by fine-tuning a pretrained model (PMTransformer) on various COF data sets, enabling the rapid prediction of COF band gaps. Our model accurately predicts COF band gaps with a mean absolute error of 0.23 eV and <i>R</i><sup>2</sup> of 0.89, outperforming the crystal graph convolutional neural network model. We validate the model’s predictions using density functional theory (DFT) calculations on a separate COF data set, confirming the consistency of predicted and DFT-calculated band gaps. By applying the model to a large COF database, we identify promising (sim) conductive COFs, demonstrating the model’s potential as an efficient screening tool for discovering COFs with optimized electronic properties for future applications in electronics and optics. Consistency (knowledge bases) ; Band gap ; Convolutional neural network ; Transfer of learning ; Electrical conductor ; Deep learning ; Electronics ; Artificial neural network Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hawon Choi (21731089) | Suyong Jung (1451815) | Sang Wook Lee (433729)
Source: Figshare (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Data-Efficient MachineLearning for Layer Number Identificationof 2D Materials Using Color Difference Analysis The identification of the number of layers in two-dimensional (2D) materials is critical for exploiting their unique quantum and optoelectronic properties. In this study, we propose a machine learning-assisted approach to classify the number of layers of five different 2D materials based on pixel-level microscopic image data, accommodating diverse experimental conditions. Our methodology leverages RGB and YIQ color spaces to capture the color differences between 2D material flakes and their substrates, with YIQ demonstrating superior performance. Among various models, such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision tree models, we demonstrate that the decision tree algorithm with YIQ data provides the highest classification accuracies across all materials, particularly effective for intermediate layers with values of 2–5. Even with the limited data for training, our pixel-level approach demonstrates a superb layer-number identification capability for up to five layers. Our study highlights the versatility of machine learning-assisted layer-number classification methods with minimal image data, offering a time-efficient and cost-effective solution for identifying various 2D materials with diverse imaging environments. RGB color model ; Color space ; Decision tree ; Support vector machine ; Identification (biology) ; Color model ; Machine vision ; Pattern recognition (psychology) 2D Materials and Applications ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: Musa Shamilevich Adygamov | Э Р Сайфуллин | Timur Gimadiev | Nikita Serov
Source: Preprints.org (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

Surfactants Critical Micelle Concentration Prediction with Uncertainty-Aware Graph Neural Network The critical micelle concentration (CMC) is a fundamental physicochemical property of surfactants with significant implications across multiple industries. This paper presents uncertainty-aware graph neural network that integrates molecular structure and temperature to simultaneously predict CMC values and prediction uncertainties. Trained on a curated dataset of 1,829 surfactants with temperature annotations, our GNN achieves competitive performance (RMSE = 0.352, MAE = 0.244) on an external test set, outperforming previous models in RMSE. The model provides statistically sound, adequately calibrated uncertainty estimates that reliably quantify prediction confidence. This dual-output approach enables reliable CMC prediction with quantifiable confidence intervals, addressing a practical need for safety-critical applications where underestimation of uncertainty could have serious consequences. Artificial neural network ; Graph ; Micelle ; Property (philosophy) ; Critical micelle concentration ; Applicability domain ; Uncertainty quantification ; Graph theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Artur Grigoryan | Stefan Helfrich | Valentin Lequeux | Benjamine Lapras | Chloé Marchand | Camille Merienne | Fabien Bruno | Roseline Mazet | Fabrice Pirot
Source: Preprints.org (preprint, 2025-06-25)
Field: Chemistry — Analytical Chemistry

Smart Formulation: AI-Driven Web Platform for Optimization and Stability Prediction of Compounded Pharmaceuticals Using KNIME Smart Formulation is a machine learning based platform designed to predict the Beyond Use Dates (BUDs) of compounded oral solid dosage forms by integrating molecular, formulation, and environmental parameters. Using a curated dataset of 3,166 active pharmaceutical ingredients (APIs), the model combines molecular descriptors with experimental stability data from Stabilis to train a tree 35 ensemble regression algorithm. This approach enables accurate prediction of API degradation under varying storage conditions, offering a scalable and cost-effective alternative to traditional stability testing. The analysis reveals a significant influence of formulation variables including the nature and number of excipients, and storage temperature on predicted stability. A negative correlation between LogP and BUD was identified, suggesting that hydrophilic APIs generally exhibit greater stability, especially when 40 formulated with a single excipient. Certain excipients such as cellulose, silica, sucrose, and mannitol were associated with enhanced stability. In contrast, excipients like HPMC and lactose, which have higher hydrogen bond donor and acceptor counts, were linked to faster degradation. The combination of two excipients instead of one often resulted in decreased stability, potentially due to moisture redistribution or phase separation effects. 45 Smart Formulation contributes to the field of computational pharmaceutics by bridging theoretical design and practical compounding. Its implementation in hospital and community pharmacies could mitigate drug shortages, streamline workflows, and support high-quality patient care. Future development will explore real-time stability monitoring and adaptive machine learning to further enhance predictive capabilities. Stability (learning theory) ; Computer science ; Machine learning Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; 3D Printing in Biomedical Research
Authors: Courtney Kunselman | Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: ArXiv.org (preprint, 2025-08-01)
Field: Materials Science — Materials Chemistry

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System This work introduces PhaseForgePlus -- a computationally efficient, fully open-source workflow for physically-informed CALPHAD model generation and parameter fitting. Using the Pt-W system as an example, we show that the integration of Machine Learning Potentials into the Alloy Theoretic Automated Toolkit can produce physically grounded Gibbs energy descriptions requiring only slight adjustments to produce accurate phase diagrams. Employing the Jansson derivative method in the context of experimental observations, such adjustments can be efficiently and robustly determined through gradient-informed optimization procedures. CALPHAD ; Workflow ; Context (archaeology) ; Work (physics) ; Gibbs free energy ; Experimental data ; Phase (matter) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Liang Yuan | Zongyi Ma | Zhiliang Pan
Source: Computational Materials Science (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Multi-task learning of solute segregation energy across multiple alloy systems Alloy ; Task (project management) ; Materials science ; Computer science ; Metallurgy ; Engineering ; Systems engineering Crystallization and Solubility Studies ; Ion-surface interactions and analysis ; Advanced Materials Characterization Techniques
Authors: Nikita Volzhin | Soowhan Yoon
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials The recent development of Kolmogorov-Arnold Networks (KANs) has found its application in the field of Graph Neural Networks (GNNs) particularly in molecular data modeling and potential drug discovery. Kolmogorov-Arnold Graph Neural Networks (KAGNNs) expand on the existing set of GNN models with KAN-based counterparts. KAGNNs have been demonstrably successful in surpassing the accuracy of MultiLayer Perceptron (MLP)-based GNNs in the task of molecular property prediction. These models were widely tested on the graph datasets consisting of organic molecules. In this study, we explore the application of KAGNNs towards inorganic nanomaterials. In 2024, a large scale inorganic nanomaterials dataset was published under the title CHILI (Chemically-Informed Large-scale Inorganic Nanomaterials Dataset), and various MLP-based GNNs have been tested on this dataset. We adapt and test our own KAGNNs appropriate for eight defined tasks. Our experiments reveal that, KAGNNs frequently surpass the performance of their counterpart GNNs. Most notably, on crystal system and space group classification tasks in CHILI-3K, KAGNNs achieve the new state-of-the-art results of 99.5 percent and 96.6 percent accuracy, respectively, compared to the previous 65.7 and 73.3 percent each. Artificial neural network ; Graph ; Multilayer perceptron ; Set (abstract data type) ; Field (mathematics) ; Perceptron Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zhihao Ding | Ting Zhang | Yiran Li | J. Y. Shi | Chen Zhang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property Prediction Organic Solar Cells (OSCs) are a promising technology for sustainable energy production. However, the identification of molecules with desired OSC properties typically involves laborious experimental research. To accelerate progress in the field, it is crucial to develop machine learning models capable of accurately predicting the properties of OSC molecules. While graph representation learning has demonstrated success in molecular property prediction, it remains underexplored for OSC-specific tasks. Existing methods fail to capture the unique structural features of OSC molecules, particularly the intricate ring systems that critically influence OSC properties, leading to suboptimal performance. To fill the gap, we present RingFormer, a novel graph transformer framework specially designed to capture both atom and ring level structural patterns in OSC molecules. RingFormer constructs a hierarchical graph that integrates atomic and ring structures and employs a combination of local message passing and global attention mechanisms to generate expressive graph representations for accurate OSC property prediction. We evaluate RingFormer's effectiveness on five curated OSC molecule datasets through extensive experiments. The results demonstrate that RingFormer consistently outperforms existing methods, achieving a 22.77% relative improvement over the nearest competitor on the CEPDB dataset. Transformer ; Solar cell ; Materials science ; Computer science ; Engineering ; Electrical engineering ; Optoelectronics ; Voltage Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics