jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yihao Zheng | Xiangcui Qiu | Haibo Li | Konggang Qu | Hui Yan | Rui Li
Source: Langmuir (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Predicting and Understanding Work Functions of Double Transition Metal MXenes via Interpretable Machine Learning Methods In this study, we employed interpretable machine learning models to predict and understand the work functions of double transition metal MXenes with the formula (M1)<sub>2</sub>(M2)X<sub>2</sub>O<sub>2</sub>. We created a comprehensive data set of 242 structures covering 11 transition metals and two X elements (C, N) using first-principles calculations. Various machine learning methods, including linear regression, support vector regression, random forest regression, and artificial neural networks, were thoroughly evaluated. The random forest model achieved the best performance on the test set, with a mean absolute error of 0.17 ± 0.02 eV, root-mean-square error of 0.24 ± 0.03 eV, and <i>R</i><sup>2</sup> of 0.86 ± 0.03. The feature importance analysis revealed a hierarchical influence mechanism: the properties of the outer transition metal (M1) dominate the work function, followed by the X element, while the inner transition metal (M2) has minimal impact. Furthermore, we employed the Sure Independence Screening and Sparsifying Operator method to derive analytical expressions that relate element features to work functions, achieving comparable accuracy (<i>R</i><sup>2</sup> = 0.82 ± 0.04) while providing physical insights. Our findings not only enable rapid prediction of MXene work functions but also provide valuable guidance for the rational design of MXene-based materials. MXenes ; Work (physics) ; Transition metal ; Transition (genetics) ; Machine learning ; Artificial intelligence ; Computer science ; Chemistry ; Materials science ; Nanotechnology ; Thermodynamics ; Physics ; Organic chemistry MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Lumin Tian | Wentan Wang | Xiaobo Ji | Zhibin Xu | Wenyan Zhou | Wencong Lu
Source: Materials (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Data-Mining-Aided-Material Design of Doped LaMnO3 Perovskites with Higher Curie Temperature The Curie temperature (Tc) of LaMnO<sub>3</sub>-based perovskites is one of the most important properties associated with their magnetic and spintronic applications. The search for new perovskites with even higher Tc is a challenging problem in material design. Through the systematic optimization of support vector regression (SVR) architecture, we establish a predictive framework for determining the Curie temperature (Tc) of doped LaMnO<sub>3</sub> perovskites, leveraging fundamental atomic descriptors. The correlation coefficient (R) between the predicted and experimental Curie temperatures demonstrated high values of 0.9111 when evaluated through the leave-one-out cross-validation (LOOCV) approach, while maintaining a robust correlation of 0.8385 on the independent test set. The subsequent high-throughput screening of perovskite compounds exhibiting higher Curie temperatures was implemented via our online computation platform for materials data mining (OCPMDM), enabling the rapid identification of candidate materials through systematic screening protocols. The findings demonstrate that machine learning exhibits significant efficacy and cost-effectiveness in identifying lanthanum manganite perovskites with elevated Tc, as validated through comparative computational and empirical analyses. Furthermore, a web-based computational infrastructure is implemented for the global dissemination of the predictive framework, enabling the open-access deployment of the validated machine learning model. Curie temperature ; Spintronics ; Machine learning ; Materials science ; Curie ; Manganite ; Perovskite (structure) ; Correlation coefficient ; Computer science ; Artificial intelligence ; Condensed matter physics ; Algorithm ; Physics ; Ferromagnetism ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Satoshi TOYODA | Masatake MACHIDA
Source: Vacuum and Surface Science (article, 2025-02-09)
Field: Materials Science — Surfaces, Coatings and Films

High-Speed Processing of Multidimensional XPS Spectral Big Data and Accurate Parameter Extraction Using Noise2Noise Denoising Technology X-ray Photoelectron Spectroscopy (XPS) is widely used to investigate the chemical composition and electronic states of surfaces in thin films, particularly in semiconductor applications where non-destructive, fast, and multidimensional analysis is increasingly needed. However, the growing complexity of surface structures and experimental conditions generates large amounts of spectral data, requiring more efficient processing techniques. In this study, we present a novel approach that combines high-speed processing of multidimensional XPS big data with the Noise2Noise denoising technique using deep learning. This integration enables the extraction of highly accurate parameters from noisy spectra, providing a powerful tool for visualizing spatiotemporal data and enhancing precision in surface analysis. Noise reduction ; Extraction (chemistry) ; Computer science ; Data extraction ; Big data ; Data processing ; Artificial intelligence ; Data mining ; Chromatography ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Yinhe Wang (4811550) | Chao Wang (146527) | Li-Hui Mou (5007050) | Jun Jiang (149215)
Source: Figshare (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Deciphering Experimental Reactivity of Metal ClustersToward N<sub>2</sub> Activation Using Graph Neural Networks Machine learning (ML) analysis of gas-phase metal cluster reactivity has emerged as a pivotal approach in this field. However, existing ML studies relying on electronic properties have primarily focused on discrete features, with less consideration of continuous structural factors that also govern cluster reactivity. Here, we present the first graph neural network (GNN) framework to model N<sub>2</sub> activation reactivity across 245 metal clusters, combining DFT-optimized structures with experimental reaction rates collected from the literature and a public data set. Through encoding both topological connectivity and atomic-level features (e.g., natural charge, valence electron occupancy, and atomic number), the graph isomorphism network (GIN) achieves superior predictive performance on reaction rates of unseen clusters. Explainable analysis reveals that natural charge redistribution likely serves as the primary mechanism for ligand-mediated reactivity modulation. Furthermore, subgraph charge polarization shows potential as a reactivity descriptormetal-core subgraphs in highly active clusters exhibit significantly lower charge polarization than mixed metal–ligand subgraphs in less active clusters. This work establishes a graph-based interpretable framework for understanding structure–activity relationships of small-molecule activation by metal clusters. Cluster (spacecraft) ; Reactivity (psychology) ; Valence (chemistry) ; Polarization (electrochemistry) ; Artificial neural network ; Graph ; Metal Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nanocluster Synthesis and Applications
Authors: Hiroki Koiso | Shoei Kawasoe | Jinfu Zhang | Toshihiro Isobe | Akira Nakajima | Yasuhide Mochizuki
Source: The Journal of Physical Chemistry C (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Discovery of Biaxial Negative Thermal Expansion in KNbSi<sub>2</sub>O<sub>7</sub> from Computations and Experiments Framework-type negative-thermal expansion (NTE) materials are often composed of corner-sharing octahedra and tetrahedra. Considering the insertion of K into a framework structure, aiming at increasing the average atomic volume (AAV), we have focused on KNbSi2O7 (KNSO). The thermal expansion behavior of KNSO has been predicted by molecular-dynamics (MD) calculations using machine-learning force field (MLFF). We have discovered that the a and b axes of KNSO show NTE behavior, while the c axis possesses positive thermal expansion (PTE) behavior through its synthesis and temperature-dependent X-ray diffraction (XRD) patterns, which are consistent with the MD calculation results. We present that the thermal expansion behavior of KNSO cannot be explained only by the anisotropic negative Grüneisen parameters based on the quasi-harmonic approximation (QHA). Our results imply that (i) anisotropic thermal expansion behaviors can be reproduced by the MD calculations and (ii) the second-order Jahn–Teller (SOJT) effect elongates the c-axis lattice constant, leading to the increase of lattice flexibility that enhances NTE behavior. The present results stimulate the exploration of NTE materials from first-principles calculations combined with MLFF. Thermal expansion ; Computation ; Materials science ; Negative thermal expansion ; Thermal ; Physics ; Thermodynamics ; Composite material ; Mathematics ; Algorithm Thermal Expansion and Ionic Conductivity ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Thiago
H. da Silva (21442890) | Jalen Lu (21442893) | Zayah Cortright (21442896) | Denis Mulumba (21442899) | Md. Sharif Khan (12978110) | Oliviero Andreussi (1659112)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Automating theAnalysis of Substrate Reactivity throughEnvironment Interaction Mapping Exploring the interaction configurations between substrates and atomic or molecular systems is crucial for various scientific and technological applications, such as characterizing catalytic reactions, solvation structures, and molecular interactions. Traditional approaches for generating substrate-reactant configurations often rely on chemical intuition, symmetry operations, or random initial states, which can be inefficient and challenging for systems with low symmetry or unknown interaction mechanisms. This work introduces a systematic and automated methodology to explore the configuration space between substrates and adsorbates using symmetry-invariant features that characterize the local atomistic topology of the substrate. The approach involves three key components: (1) defining and discretizing a contact space surrounding the substrate, (2) utilizing symmetry-invariant descriptors to capture local atomic environments, and (3) employing unsupervised machine-learning techniques for clustering and hierarchical analysis of interaction sites. The method ensures comprehensive yet nonredundant sampling of the configuration space, independent of the substrate dimensionality. Applications to simple ideal substrates show that symmetry intuition and high-symmetry sites are correctly recovered. Moreover, the method is shown to translate seamlessly to less symmetric substrates. Intuition ; Solvation ; Molecular dynamics ; Topology (electrical circuits) ; Key (lock) ; Cluster analysis ; Configuration space ; Space (punctuation) ; Simple (philosophy) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Florent HAFFNER | Marion Lacoue-Nègre | Aurélie Pirayre | David Gonçalves | Maxime Moreaud
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ipa Under the Light: An Explainability Study (Xai) of a Deep Cnn for Near-Infrared Spectroscopy Spectroscopy ; Infrared ; Near-infrared spectroscopy ; Psychology ; Optics ; Neuroscience ; Physics ; Astronomy Machine Learning in Materials Science
Authors: Hangxu Liu | Yifei Zhu | Zhenggang Lan
Source: ArXiv.org (preprint, 2025-11-17)
Field: Materials Science — Materials Chemistry

An Automated Framework for Analyzing Structural Evolution in On-the-fly Non-adiabatic Molecular Dynamics Using Autoencoder and Multiple Molecular Descriptors A major challenge in nonadiabatic molecular dynamics is to automatically and objectively identify the key reaction coordinates that drive molecules toward distinct excited-state decay channels. Traditional manual analyses are inefficient and rely heavily on expert intuition, creating a bottleneck for interpreting complex photochemical processes. To overcome this, we introduce a fully automated machine-learning framework that directly extracts these coordinates from on-the-fly trajectory surface hopping data. By combining an Autoencoder for nonlinear dimensionality reduction with clustering and information entropy analysis, our method autonomously maps reaction channels and pinpoints their governing structural motions. When applied to keto isocytosine and the methaniminium cation, the framework objectively revealed invovled reaction channels and corresponding active coordinates with high efficiency and accuracy. This work establishes an effective paradigm for mechanistic insight in excited-state dynamics, transforming raw trajectory data into clear, interpretable reaction mechanisms. Autoencoder ; Bottleneck ; Cluster analysis ; Curse of dimensionality ; Information bottleneck method ; Dimensionality reduction ; Molecular dynamics ; Key (lock) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Bassam S F Elsaied | Iryna Tretiak
Source: Bristol Research (University of Bristol) (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

UNDERSTANDING THE IMPACT OF ARCHITECTURAL DEFECTS ON COMPOSITE MATERIALS PERFORMANCE USING ARTIFICIAL INTELLIGENCE Composite materials exhibit complex mechanical behaviours primarily influenced by their internal architectures, whether unidirectional, woven, or non-crimp fabrics. Variabilities in these internal architectures, such as defects, can locally alter the material's mechanical performance, which in turn can change of response on the structural scale under certain loading condition. Developing an understanding of the link between the internal architecture and mechanical response can be challenging in most cases. Experimental and modelling approaches can be cost-prohibitive and resource-intensive. Additionally, its often-complex task to extract meaningful patterns from the large datasets needed to fully characterise a material system. This study explores the use of Artificial Intelligence (AI) techniques to address these challenges and enhance our understanding of defect-induced variabilities in composite material performance.<br/><br/>In this work, the application of several AI techniques to composite materials will be presented. Deep Convolutional Recurrent Neural Networks are employed to model the non-linear, time-dependent mechanical response of composites taking into account the presence of defects. Additionally, pattern recognition methods are used to identify and quantify the influence of repeatable architectural features on material performance. Furthermore, deep learning techniques leveraging are applied to X-ray CT scan data to isolate key architectural elements that dominate the composite’s mechanical response using Gradient Weighted Activation Maps. This work demonstrates that AI-driven approaches offer a powerful and efficient means to providing new insights into the relationship between internal defects and composite materials performance. These methods present significant potential for improving the design, manufacturing, and optimization of high-performance composite structures. Task (project management) ; Convolutional neural network ; Composite number ; Deep learning ; Scale (ratio) ; Artificial neural network ; Component (thermodynamics) Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Jorge Suárez-Recio | Pablo M. Piaggi | F. J. Domínguez-Gutiérrez | R. González-Arrabal | R. Iglesias
Source: ArXiv.org (preprint, 2025-05-19)
Field: Materials Science — Materials Chemistry

Early Stages of Self-Healing at Tungsten Grain Boundaries from Ab Initio Machine Learning Simulations Nanostructured tungsten has been reported as a possible alternative plasma-facing material due to its potential ability to self-heal radiation-induced defects, a property that is attributed to its high density of grain boundaries (GB). Here, we study the initial stages of self-healing at tungsten interfaces with molecular dynamics simulations driven by a machine-learning interatomic potential tailored to one of the most common GBs found in experiments. Our model accurately reproduces the ab initio potential energy surface derived from density functional theory (DFT) calculations and outperforms previously reported empirical and machine learning interatomic potentials in predicting defect energetics. The simulations reveal low-temperature defect migration to GBs driven by rapid dumbbell-like ordering and subsequent accommodation along GB grooves. In contrast to empirical potentials, which predict unexpected GB degradation at high temperatures after defect migration, our model maintains stable GB motifs over the investigated temperature range. The temperature-dependent defect counts, evaluated using an Arrhenius-like fit, yield an average interstitial migration energy of 0.048 eV, in agreement with experiment. This work underscores the capabilities of ab initio machine learning simulations in accurately modeling defect-GB interactions and highlights their potential to contribute to the development of radiation tolerant materials. Ab initio ; Interatomic potential ; Tungsten ; Molecular dynamics ; Grain boundary ; Ab initio quantum chemistry methods ; Density functional theory Fusion materials and technologies ; Nuclear materials and radiation effects ; Ion-surface interactions and analysis
Authors: Bo Gong | Jieping Wang | Hao-Dong Liu | Rui Guo | Jinxiao Dou | Guang‐Yue Li | Yinghua Liang
Source: Fuel (article, 2025-09-09)
Field: Chemistry — Analytical Chemistry

Molecular insight into the structure and formation mechanism of fluid phase in the classical coking theory Aromaticity ; ReaxFF ; Molecule ; Vitrinite ; Phase (matter) ; Alkyl ; Aromatization ; Graphite ; Molecular model Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Subcritical and Supercritical Water Processes
Authors: Mingyu Yu (8704095) | Isaiah A. Moses (21438162) | Wesley F. Reinhart (19540878) | Stephanie Law (3074904)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

MultimodalMachine Learning Analysis of GaSe MolecularBeam Epitaxy Growth Conditions Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling real-time process optimization and reducing the need for manual tuning. However, their application to molecular beam epitaxy (MBE) remains underdeveloped. Here, we present a machine learning-guided framework for MBE growth of GaSe films, leveraging reflection high-energy electron diffraction (RHEED) as an in situ diagnostic alongside <i>ex situ</i> characterization via X-ray diffraction and atomic force microscopy. Unsupervised learning on RHEED patterns reveals a well-defined boundary between high- and low-quality samples, capturing physically meaningful features. Mutual information analysis shows a strong correlation between RHEED embeddings and rocking curve full-width at half-maximum (fwhm), while the correlation with AFM root-mean-square (RMS) roughness is weak. Among key growth conditions, growth rate most strongly influences fwhm, whereas the Se/Ga flux ratio primarily affects RMS roughness and the RHEED embeddings. Supervised learning models trained to predict fwhm and RMS roughness demonstrate moderate accuracy, with significant improvement achieved by incorporating RHEED embeddings. Furthermore, anomaly detection via residual analysis in supervised learning aligns well with unsupervised classification from RHEED, reinforcing the reliability of the predictive models. This study establishes a data-driven framework for machine learning-assisted MBE, paving the way for real-time process control and accelerated optimization of thin-film synthesis. Reflection high-energy electron diffraction ; Molecular beam epitaxy ; Surface roughness ; Surface finish ; Electron diffraction ; Reflection (computer programming) ; Unsupervised learning ; Characterization (materials science) Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Raphaël Kou | Cong Wang | R. Wan | Heng Xu | Mingliang Su | Zechun Liu | Yun Lin | Le Zhao | Junwei Guo | Hongbo Wang | Yongcai Fu | Song Yang | Cong Nie
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Geographical Origin Discrimination and Chemical Characterization of Tobacco Leaves from Multiple Origins Using Chemical Composition and Chemometrics Approaches Chemometrics ; Chemical composition ; Characterization (materials science) ; Composition (language) ; Chemistry ; Nanotechnology ; Materials science ; Chromatography ; Art ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Ming‐Qiang Qi | Ruiwu Yang | Jielong Wu | Jianan Chen | Youxiang Qi | X. Diao | Ming‐Hao Du | Yibin Jiang | Cheng Wang | La‐Sheng Long | Lan‐Sun Zheng | Xiang‐Jian Kong
Source: Angewandte Chemie (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Assembly of a Nested Gd<sub>20</sub>@Gd<sub>32</sub> @Ni<sub>36</sub> Cluster via Urea Controlled Carbonate Release Abstract Lanthanide‐based polyhedral clusters are of great interest due to their unique geometries and functional properties, but their controlled synthesis remains a major challenge. Here, we report a nested three‐shell cluster, [Gd 52 Ni 36 (MeIDA) 36 (OH) 114 (CO 3 ) 12 (H 2 O) 48 ]·(ClO 4 ) 18 ·(H 2 O) 21 ( Gd 52 Ni 36 , H 2 MeIDA = N ‐Methyliminodiacetic acid), achieved by using urea as a slow‐release carbonate source to direct the formation of a dodecahedral inner shell. Single‐crystal X‐ray diffraction reveals a unique Gd 20 @Gd 32 @Ni 36 arrangement, with the innermost Gd 20 forming a Platonic dodecahedron templated by carbonate. A machine learning–guided, high‐throughput synthesis platform enabled the exploration of 780 reaction conditions, uncovering key parameters and phase boundaries governing cluster formation. This work demonstrates data‐driven strategies can accelerate the discovery of complex lanthanide architectures. Carbonate ; Cluster (spacecraft) ; Urea ; Chemistry ; Inorganic chemistry ; Materials science ; Chemical engineering ; Computer science ; Metallurgy ; Engineering ; Organic chemistry Nanocluster Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science