jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valeriy A. Verkhov | Stepan A. Meshalkin | Alexander S. Antonov | Elena Yu. Tupikina
Source: The Journal of Chemical Physics (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

A data-driven and quantum chemistry-anchored framework for modeling and classifying carbon–lithium bonding in organolithium aggregates We present a quantum chemistry-based, data-driven framework for the automated classification of carbon-lithium bonding motifs in archetypal organolithium aggregates. Starting from ab initio potential energy surfaces-guided sampling, we constructed a chemically complete dataset of 81 optimized gas-phase aggregates of methyllithium, t-butyllithium, and phenyllithium (600 C-Li bonds in total) spanning all relevant nuclearities and bonding modes. Twenty geometric, electronic, and topological descriptors obtained from quantum theory of atoms in molecules and Electron Localization Function (ELF) analyses were evaluated via correlation clustering, yielding a minimal, non-redundant, chemically meaningful set dominated by the ELF basin electron population and key bond-path metrics. This reduced descriptor set was used to train two complementary supervised models-a multi-task fully connected neural network and a bootstrap-aggregated decision tree-achieving accuracies of 84% (nucleophile type), 89% (aggregation state), and 84% (bond type) on validation data. Both methods consistently identified ELF-derived descriptors as the most discriminative, enabling physically grounded separation of bonding regimes (2c-2e, multi-center, non-classical, π-Li) and providing an interpretable, transferable platform for high-throughput bonding analysis in organometallic chemistry. Quantum ; Set (abstract data type) ; Quantum chemical ; Ab initio ; Population ; Molecule ; Function (biology) ; Electron localization function ; Artificial neural network Machine Learning in Materials Science ; Coordination Chemistry and Organometallics ; Computational Drug Discovery Methods
Authors: Massimiliano Lupo Pasini | Pei Zhang | Jong Youl Choi | German Samolyuk | Markus Eisenbach | Ying Yang
Source: Computational Materials Science (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys. • We propose data-efficient GNN training for accurate predictions on refractory alloys. • Training on disordered binaries, minimal ternary data boosts GNN transferability. • Our approach shows GNN transferability from binaries to ternaries in Nb-Ta-V space. • Reversed transferability (ternary to binary) is challenging due to extrapolation. • This work shows promising path towards avoiding the curse of dimensionality. Refractory (planetary science) ; Solid solution ; Materials science ; Refractory metals ; Thermodynamics ; Metallurgy ; Chemistry ; Physics Nuclear Materials and Properties ; High Entropy Alloys Studies ; Intermetallics and Advanced Alloy Properties
Authors: Mao-Ken Hsu | Boyu Huang | Chi‐Hua Yu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Integration of Deep Learning and Reinforcement Learning for Efficient Woven Composite Design Reinforcement learning ; Composite number ; Reinforcement ; Artificial intelligence ; Computer science ; Engineering ; Structural engineering ; Algorithm Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Sofiia Chorna | Davide Tisi | Cesare Malosso | Wei Bin How | Michele Ceriotti | Sanggyu Chong
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

Comparing the latent features of universal machine-learning interatomic potentials The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors, and observing how the trends are affected by the choice of training set and training protocol. We find that uMLIPs encode the chemical space in significantly distinct ways, with substantial cross-model feature reconstruction errors. When variants of the same model architecture are considered, trends become dependent on the dataset, target, and training protocol of choice. We also observe that fine-tuning of a uMLIP retains a strong pre-training bias in the latent features. Finally, we discuss how atom-level features, which are directly output by MLIPs, can be compressed into global structure-level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system. ENCODE ; Feature (linguistics) ; Concatenation (mathematics) ; Encoding (memory) ; Set (abstract data type) ; Range (aeronautics) ; Pattern recognition (psychology) ; Representation (politics) ; Space (punctuation) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Yuchen Zou | Shipeng Sun | Weiwei Tang | Bin Li
Source: Advanced Science (article, 2025-11-19)
Field: Chemistry — Analytical Chemistry

Image Fusion for Super‐Resolution Mass Spectrometry Imaging of Plant Tissue Mass spectrometry imaging (MSI) is a vital tool in botanical research. Image fusion is introduced for resolution enhancement of MSI data from animal samples, but its application to plant MSI data resulted in unsatisfactory visualizations due to the distinct morphological characteristics of plant tissues. Herein, this study presents loss controlled residual network (LCRN), a workflow dedicated to the super-resolution fusion of plant MSI data. The pipeline used a residual connection-based neural network implemented with a novel loss metric called edge perceptual loss. Edge perceptual loss is developed for evaluating complex morphological information that can not be properly reflected by common image metrics, and its implementation in loss propagation is vital to the quality of the fusion result. Compared to existing deep learning-based methods, LCRN is able to generate a high-quality super-resolution fusion image of extra high magnification (up to 20-fold) that combined chemical and morphological information obtained from MSI and microscopy, respectively. Pipeline (software) ; Residual ; Fusion ; Workflow ; Image fusion ; Plant tissue ; Mass spectrometry imaging ; Sensor fusion ; Metric (unit) Spectroscopy and Chemometric Analyses ; Advanced Image Fusion Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rojas Murillo, Lina F
Source: Civil War Book Review (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

METAL FILMS FOR HIGH-PERFORMANCE OPTOELECTRONIC DEVICES: UNDERSTANDING BAND HYBRIDIZATION EFFECTS The development of alloyed metal films for high-performance optoelectronic devices, like ultrafast Schottky barrier photodetectors, is key to advancing technologies in ultrafast detection and communication. However, exploring these materials experimentally becomes increasingly difficult as the number of alloy components grows. For example, moving from binary to ternary or quaternary systems creates a combinatorial explosion of possible compositions, making it nearly impossible to test them all in reasonable timeframe. Additionally, there’s a lack of frameworks that not only predict the properties of these alloys but also explain why certain composition perform better- particularly in terms of band hybridization and its impact on hot carrier generation and lifetime. To address this, we propose a framework that combines transfer learning (TL) with physics-based modeling. TL allows us to leverage knowledge from simpler or well-studied systems to guide the exploration of more complex alloys, reducing the need for extensive experimentation. But more than just predicting properties, this approach helps us understand the underlying physics -such as how band hybridization influences electronic structure and hot carrier dynamics. This insight is critical for identifying which alloy compositions are most promising and why, rather than relying on trial and error. In this work, we develop a physics-informed model validated by spectroscopic ellipsometry, X-ray diffraction, and ultrafast pump-probe spectroscopy. This model generates synthetic data to train machine learning surrogate models, which are then refined using TL to adapt to new alloy systems. The goal is to optimize key properties like hot carrier lifetime and optical properties while providing clear physical explanations for why certain compositions excel. By bridging prediction with understanding, this approach accelerates the discovery of high-performance materials for next-generation optoelectronic devices. Leverage (statistics) ; Ultrashort pulse ; Ternary operation ; Binary number ; Key (lock) ; Alloy ; Schottky diode Machine Learning in Materials Science ; 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Tivasha Keiko Nurliam | Laily Ade Oktaviana | Dita Puspitasari | Fathur Rahman
Source:  (article, 2025-08-27)
Field: Materials Science — Surfaces, Coatings and Films

A Comparative Study of AI-Based SEM Image Processing for Automated Microstructure Analysis of Medical Electrode Materials Using U-Net, K-Means, and Otsu The characterization of microstructures in medical electrode materials is crucial for understanding their performance and functionality. Imaging with a Scanning Electron Microscope (SEM) delivers visual data with high resolution. However, manual fiber measurement using ImageJ is time-consuming and prone to inconsistent calculations. This study explores and compares the potential of three Artificial Intelligence (AI) or Python-based techniques in image processing, including Otsu thresholding, K-means clustering, and the U-Net deep learning model, to automate the microstructure analysis of conductive polymer composite materials used in biomedical electrodes. The methodology applied involves applying each technique to SEM images and comparing fiber width measurements obtained from ImageJ and SEM scale. The results are then evaluated based on fiber count and fiber width comparison. Preliminary findings indicate that deep learning approaches such as U-Net offer superior segmentation performance and fiber width measurement accuracy, with a significantly lower mean value of 10.70 μm compared to 16.58 μm in ImageJ and 17.02 μm in SEM. Meanwhile, classical methods such as Otsu and K-means remain computationally efficient alternatives. This comparative analysis highlights the strengths and limitations of each method and provides a foundational assessment for the development of more robust and automated SEM image analysis pipelines in biomedical materials research. Image processing ; Segmentation ; Image segmentation ; Otsu's method ; Fiber ; Deep learning ; Microstructure ; Scanning electron microscope Electron and X-Ray Spectroscopy Techniques ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science
Authors: Saeed Hasan Nabil
Source: International Journal of Sciences and Innovation Engineering (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

Hybrid Perovskite–CIGS Solar Cells with Machine Learning-Driven Performance Prediction Hybrid perovskite–Cu(In,Ga)Se₂ (CIGS) solar cells represent a promising pathway for next-generation photovoltaics due to their complementary optoelectronic properties, tunable bandgaps, and potential for low-cost tandem architectures. While perovskite layers offer high absorption coefficients and tunability, CIGS provides long-term stability and proven large-scale deployment. However, integrating these two materials into a high-performance hybrid solar cell introduces challenges in interface engineering, defect management, and process optimization. This paper presents a systematic framework that combines experimental studies with machine learning (ML)-driven performance prediction models to optimize hybrid perovskite–CIGS solar cells. We first provide a comprehensive review of the state-of-the-art in perovskite/CIGS tandem research and highlight key material and structural bottlenecks. Then, a hybrid architecture is proposed with ML-assisted predictive modeling to optimize bandgap alignment, defect passivation, and carrier transport. The system architecture integrates a neural network regression model trained on experimental and simulated datasets, enabling accurate prediction of power conversion efficiency (PCE) under varying fabrication and environmental conditions. The methodology leverages device physics simulation data, experimental results, and feature engineering from material descriptors. Experimental validation demonstrates that ML-guided optimization achieves improved fill factors and higher open-circuit voltage compared to conventional design approaches. The findings indicate that ML-augmented design can accelerate the development cycle of hybrid perovskite–CIGS solar cells, reduce experimental trial-and-error, and push device efficiencies closer to theoretical limits. The paper concludes with a discussion of future directions, including federated learning for collaborative optimization and scalable fabrication strategies to enable commercial deployment in the U.S. renewable energy sector. Tandem ; Scalability ; Photovoltaic system ; Photovoltaics ; Software deployment ; Renewable energy ; Process (computing) ; Artificial neural network ; Solar energy ; Copper indium gallium selenide solar cells Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Yikun Liu | Yurong He | Tianqi Tang | Jiaqi Zhu
Source: Science China Technological Sciences (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Machine learning-driven insights into biaxial strain-induced anomalous thermal conductivity enhancement of boron arsenide Thermal conductivity ; Strain (injury) ; Materials science ; Boron ; Gallium arsenide ; Conductivity ; Thermal ; Composite material ; Optoelectronics ; Condensed matter physics ; Chemistry ; Physics ; Thermodynamics ; Medicine Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Giovanni Beltrame | Erika Michela Dematteis | Vitalie Stavila | Paola Rizzi | Marcello Baricco | Mauro Palumbo
Source: Preprints.org (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction. Computer science ; Hydrogen storage ; Hydrogen ; Algorithm ; Machine learning ; Artificial intelligence ; Chemistry ; Organic chemistry Hydrogen Storage and Materials
Authors: Ahmed Elhalawani | R. F. Shi | M. Castellanos | Michał Tomza | Jesús Pérez‐Ríos
Source: ArXiv.org (preprint, 2025-07-14)
Field: Chemistry — Physical and Theoretical Chemistry

What is the diatomic molecule with the largest dipole moment? We present a machine learning model for predicting the electric dipole moment of diatomic molecules using only the atomic properties of the constituent atoms. Our model can screen the entire periodic table and identify the molecules with the largest dipole moment for applications in cold molecular sciences, or find the molecule with the largest dipole moment that contains a given atom. Similarly, our model identifies useful trends that explain the dipole moment of molecules, improving our intuition in chemical physics. Finally, we condense our model into an analytical expression to predict the dipole moment in terms of atomic properties. Dipole ; Diatomic molecule ; Bond dipole moment ; Moment (physics) ; Electric dipole moment ; Transition dipole moment ; Molecule Various Chemistry Research Topics ; Molecular Spectroscopy and Structure ; Atomic and Molecular Physics
Authors: Gabriele Raabe (1268097) | Viral P. Chheda (21743002) | Ulrich Römer (21743005)
Source: Figshare (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Sequential BayesianForce Field Calibration of Lennard–JonesParameters with Experimental Data Force field parameters for molecular simulations of thermophysical properties are typically optimized iteratively against experimental data. However, this requires extensive computational effort due to multiple parameter evaluations. Although machine learning has been successfully applied in bottom-up derivation of interatomic potentials from quantum chemical data, its use in top-down classical force field parametrization is limited. Moreover, existing machine learning frameworks struggle to build efficient, localized surrogate models while quantifying uncertainties. Here, we address these challenges by presenting a sequential design strategy based on Gaussian Process surrogate models, directly tailored for a Bayesian calibration task. We employ our approach to calibrate force field parameters of the refrigerant trans-1,2-dichloroethene using experimental vapor–liquid equilibria data, and achieve convergence with a low number of training points. Beyond reproducing VLE behavior, we employ the calibrated parameters in molecular dynamics studies of liquid density and viscosity, thereby assessing the impact of parameter uncertainties on the simulation results. Force field (fiction) ; Gaussian process ; Calibration ; Experimental data ; Parametrization (atmospheric modeling) ; Field (mathematics) ; Bayesian probability ; Convergence (economics) ; Kriging Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Gaussian Processes and Bayesian Inference
Authors: Fan Cheng | Maodong Li | Sihao Yuan | Zhi‐Ping Xie | Dechin Chen | Yi Yang | Yi Qin Gao
Source: ArXiv.org (preprint, 2025-03-31)
Field: Materials Science — Materials Chemistry

Performing Path Integral Molecular Dynamics Using Artificial Intelligence Enhanced Molecular Simulation Framework This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double proton transfer in formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems, using relatively low computational cost. Modular design ; Molecular dynamics ; Path (computing) ; Path integral formulation ; Computational complexity theory ; Complex system ; Computational model Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum, superfluid, helium dynamics