jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: George T. Tebbutt | Christopher S. Allen | Anna Fabijańska | Barbara M. Maciejewska | Nicole Grobert
Source: Nature Communications (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Capturing atomic wetting dynamics in real time Atomic-scale wetting governs material formation at the nanoscale but remains poorly understood under confinement, where classical capillarity models fail. The growth of metallic nanowires within multi-wall carbon nanotubes (MWCNTs) exemplifies this challenge, requiring precise control over wetting, nucleation, and vapour-phase condensation. Here we show that nanowire formation proceeds through a two-stage mechanism: curvature-driven nucleation at open tube ends followed by capillary-driven elongation sustained by continuous vapour condensation. Using in situ atomic-resolution transmission electron microscopy (ARTEM) coupled with a deep learning convolutional neural network (CNN) capable of classifying liquid, solid and intermediate Sn<sub>x</sub>O phase transitions, we directly capture the cascade of thermally induced nanowire growth within CNTs. Growth requires a wetting interface (contact angle, θ <90°) between liquid Sn<sub>x</sub>O and the nanotube wall-conditions not described by Kelvin or Lucas-Washburn models. These results establish a predictive framework for vapour-phase nanowire encapsulation, linking nanoscale wetting dynamics to the fabrication of advanced nanomaterials. Nanowire ; Wetting ; Nucleation ; Carbon nanotube ; Nanoscopic scale ; Molecular dynamics ; Dewetting ; Fabrication Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Minghao Yu | Jing Liu | Cheng Chen | M. H. Li
Source: SHILAP Revista de lepidopterología (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Enhancing phase change thermal energy storage material properties prediction with digital technologies IntroductionIn the field of materials science, the prediction of material properties plays a critical role in designing new materials and optimizing existing ones. Traditional experimental approaches, while effective, are resource-intensive and time-consuming, often requiring extensive trial-and-error methods. To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. The method integrates MD simulations for atomic-level interactions using Lennard-Jones and embedded-atom method (EAM) potentials, FEM-based continuum mechanics for stress-strain analysis and thermal response evaluation, and ML techniques trained on multiscale descriptors (e.g., bond energy, stress tensor, coordination number) to model nonlinear property relations and accelerate design iteration. Hierarchical feature fusion modules combine low-level atomistic descriptors with high-level continuum features.ResultsBenchmark evaluations show improved performance in predicting elastic modulus, thermal conductivity, and phase transition temperature across five material classes. Our experimental results demonstrate that this integrated methodology outperforms conventional methods in both prediction speed and accuracy, particularly in complex or multicomponent systems.DiscussionThis approach significantly reduces computational costs and accelerates material design workflows by predicting properties with high precision across a wide range of materials. It aligns with current trends in leveraging advanced digital technologies to enhance materials discovery, offering a robust, scalable, and extensible framework for the optimization and design of advanced materials in various industrial, technological, and scientific applications. Computer science ; Finite element method ; Benchmark (surveying) ; Multiscale modeling ; Uncertainty quantification ; Artificial neural network ; Nonlinear system ; Continuum mechanics ; Computational model ; Thermal conductivity ; Material properties ; Artificial intelligence ; Algorithm ; Computational science ; Machine learning ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Jianfeng Hu | Heng Gao | Yixuan Liu | Aoyuan Peng
Source:  (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Transfer Analysis of Machine Learning Models Trained on Simulated XRD Patterns to Real Powder XRD Experimental Data Accurate crystal structure identification is crucial for material property prediction, functional design, and the discovery of new materials. Machine learning models trained on simulated X-ray diffraction (XRD) patterns based on the International Crystal Structure Database (ICSD) have shown promising results under idealized conditions. However, real experimental data often contain complex factors such as peak shifts, peak broadening, multiphase overlap, and background noise, which can lead to performance degradation when applying these models directly. In this study, we constructed a hybrid feature learning model by training on ICSD-based standard XRD patterns, combining handcrafted physical features with deep convolutional features. We then systematically evaluated its transfer performance on real-world powder XRD data collected from various sources. The results showed that, despite domain differences, the model achieved a crystal system classification accuracy of 91.5 %, a space group Top-1 accuracy of 76.8 %, and a Top-5 accuracy of 89.2 %, demonstrating good transferability. Through detailed error analysis, we found that multiphase coexistence, peak loss, and instrumental noise were the main sources of classification errors. Furthermore, we discuss potential strategies to improve transfer performance, including transfer learning, data augmentation, and the integration of physical prior knowledge. This work provides new insights into applying machine learning models trained on standard data for the intelligent analysis of real experimental XRD patterns, thereby promoting the broader application of AI in materials research. Transfer of learning ; Computer science ; Materials science ; Artificial intelligence ; Data modeling ; Transfer (computing) ; Machine learning ; Database ; Operating system X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Jaewook Lee | Xinyang Sun | Ed Errington | Calum Drysdale | Miao Guo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Kan-Based Interpretable Framework for Prediction of Global Warming Potential Across Chemical Space Global warming ; Space (punctuation) ; Chemical space ; Climatology ; Environmental science ; Computer science ; Artificial intelligence ; Climate change ; Geology ; Chemistry ; Oceanography History and advancements in chemistry ; Machine Learning in Materials Science
Authors: Jing Yang | Bingxin Li | Samuel Henrique Mattoso | Ahmed Abdelkawy | Mira Todorova | Jörg Neugebauer
Source: ArXiv.org (preprint, 2025-10-01)
Field: Chemistry — Electrochemistry

Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces Large thermal fluctuations of the liquid phase obscure the weak macroscopic electric field that drives electrochemical reactions, rendering the extraction of reliable interfacial charge distributions from ab initio molecular dynamics extremely challenging. We introduce SMILE-CP (Scalar Macro-dipole Integrated LEarning - Charge Partitioning), a macro-dipole-constrained scheme that infers atomic charges using only the instantaneous atomic coordinates and the total dipole moment of the simulation cell - quantities routinely available from standard density-functional theory calculations. SMILE-CP preserves both the global electrostatic field and the local potential without invoking any explicit charge-partitioning scheme. Benchmarks on three representative electrochemical interfaces - nanoconfined water, Mg2+ dissolution in water, and a kinked Mg vicinal surface under anodic bias - show that SMILE-CP eliminates the qualitative errors observed for unconstrained charge decompositions. The method is computationally inexpensive and data-efficient, opening the door to charge-aware machine-learning potentials capable of bias-controlled, nanosecond-scale simulations of realistic electrochemical systems. Dipole ; Electrochemistry ; Electric field ; Electrostatics ; Electrochemical potential ; Thermal ; Charge (physics) ; Molecular dynamics ; Vicinal ; Atomic charge Electrochemical Analysis and Applications ; Non-Destructive Testing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Jiacheng Zhou (2245543) | Bo Xiao (347938) | Qi Liu (33068) | Lifeng Liu (164546) | Lei Zhang (38117)
Source: Figshare (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

MatPC: PromptingLarge Language Model, Crystal StructurePrediction, and First-Principles for Semantic-Driven Material Design In this study, an AI-guided framework is developed for semantic-driven material design, integrating large language models (LLMs) with first-principles methods and crystal structure prediction (MatPC) to identify novel photovoltaic materials. By utilizing prompt-engineered LLMs, semantic embeddings of material property descriptions are leveraged to identify uncommon materials candidates with strong alignment to desired functionalities. The material discovery pipeline combines LLMs, similarity scoring, dimensional reduction, formula screening, crystal structure prediction, and DFT validation into a cohesive computational workflow. The candidates undergo crystal structure prediction to generate polymorphs using a hybrid genetic algorithm-graph neural network (GA-GNN) approach, followed by validation through DFT calculations on atomic and electronic properties, optical absorption, and theoretical power conversion efficiencies. As a case study, an unconventional Bi<sub>2</sub>WO<sub>6</sub> polymorph is identified as a promising photovoltaic material, with its electronic and optical properties thoroughly analyzed via first-principles calculations. Our study presents an efficient material discovery pipeline leveraging large language models (LLMs) to accelerate the material design process. Pipeline (software) ; Crystal structure prediction ; Material properties ; Photovoltaic system ; Property (philosophy) ; Crystal structure ; Material Design ; Crystal (programming language) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Photocatalysis Techniques
Authors: Fan Shun Meng | Jiu Hui Li | Shuhei Shinzato | Kazuki Matsubara | W. T. Geng | Shigenobu Ogata
Source: Institutional Repositories DataBase (IRDB) (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials Twist ; Interatomic potential ; Dislocation ; Artificial neural network ; Grain boundary Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Cheng-Chu Chung | Ruipeng Li | Gabriel M. Veith | Honghu Zhang | Fernando Camino | Ming Lu | Nikhil Tiwale | Sheng Zhang | Kevin G. Yager | Yu-chen Karen Chen-Wiegart
Source: ArXiv.org (preprint, 2025-01-22)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Solid-State Thin-Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment Thin-film solid-state metal dealloying (thin-film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high-throughput characterization of thermal treatment parameters while probing local nanostructures of thin-film samples is needed. In this work, a laser-based thermal treatment is demonstrated to create temperature gradients on single thin-film samples of Nb-Al/Sc and Nb-Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X-ray multimodal and high-throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified within this framework. While the oxidation process may contribute to nanostructure formation during thin-film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. This approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin-film SSMD systems with targeted nanostructures. Materials science ; Solid-state ; Thin film ; Laser ; Thermal ; Metal ; Temperature gradient ; Laser treatment ; Nanotechnology ; Metallurgy ; Engineering physics ; Optics ; Engineering Nanoporous metals and alloys ; Laser-Ablation Synthesis of Nanoparticles
Authors: Jaewook Lee | Xinyang Sun | Ed Errington | Calum Drysdale | Miao Guo
Source: Carbon Capture Science & Technology (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

A KAN-based interpretable framework for prediction of global warming potential across chemical space Accurate yet interpretable prediction of Global Warming Potential (GWP) is essential for the sustainable design of novel molecules, chemical processes and materials. This capability is valuable in the early-stage screening of compounds with potential relevance to carbon management and emerging CCUS applications. However, conventional models often face a trade-off between predictive accuracy and interpretability. In this study, we propose an AI-based GWP prediction framework that integrates both molecular and process-level features to improve accuracy while employing white-box modeling techniques to enhance interpretability. First, by incorporating molecular descriptors (MACCS keys, Mordred descriptors) and process-level information (process title, description, location), the Deep Neural Network (DNN) model achieved an R² of 86% on the test data, representing a 25% improvement over the most comparable benchmark reported in prior studies. XAI analysis further highlights the crucial role of process-related features, particularly process title embeddings, in enhancing model predictions. Second, to address the need for model transparency, we employed a Kolmogorov–Arnold Network (KAN) model to develop a symbolic, white-box GWP prediction model. While achieving a lower R² of 64%, this model provides explicit mathematical representations of GWP relationships, enabling interpretable decision-making in sustainable chemical and process design. Our findings demonstrate that integrating molecular and process-level features improves both predictive accuracy and interpretability in GWP modelling. The resulting framework can support early-stage environmental assessment of novel compounds, offering a useful tool to inform the sustainable design of chemicals, including those with potential applications in CCUS. Global warming ; Space (punctuation) ; Global-warming potential ; Chemical space ; Computer science ; Environmental science ; Climatology ; Artificial intelligence ; Climate change ; Geology ; Biology ; Oceanography ; Greenhouse gas ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Kaihua Ji | Luning Sun | Shusen Liu | Fei Zhou | Tae Wook Heo
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics Microstructural pattern formation, such as dendrite growth, occurs widely in materials and energy systems, significantly influencing material properties and functional performance. While the phase-field method has emerged as a powerful computational tool for modeling microstructure dynamics, its high computational cost limits its integration into practical materials design workflows. Here, we introduce a machine-learning framework using autoregressive deep surrogates trained on short trajectories from quantitative phase-field simulations of alloy solidification in limited spatial domains. Once trained, these surrogates accurately predict dendritic evolution at scalable length and time scales, achieving a speed-up of more than two orders of magnitude. Demonstrations in isothermal growth and in directional solidification of a dilute Al-Cu alloy validate their ability to predict microstructure evolution. Quantitative comparisons with phase-field benchmarks further show excellent agreement in the tip-selection constant, morphological symmetry, and primary spacing evolution. Microstructure ; Isothermal process ; Dendrite (mathematics) ; Scalability ; Autoregressive model ; Multiscale modeling ; Alloy ; Directional solidification Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Yuqing Zhang | Xianqiang Meng | Jing Chen | Jingtao Wang
Source: Chemical Physics (article, 2025-11-03)
Field: Chemistry — Organic Chemistry

Machine learning and DFT elucidation of proton-coupled resonance mechanisms in acid-catalyzed ethyl 4-chloro-2-methoxyiminoacetoacetate isomerization Isomerization ; Protonation ; Density functional theory ; Resonance (particle physics) ; Intramolecular force ; Diradical ; Proton ; Conjugated system Chemical Reaction Mechanisms ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Shota Tankano | Takao Kotani | Masao Obata | Kazunori Satō | Harutaka Saito | Tatsuki Oda
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Machine Learning Band Gap Predictions: Linking Quasiparticle Self-Consistent GW and LDA-Derived Partial Density of States Accurately calculating band gaps for given crystal structures is highly desirable. However, conventional first-principles calculations based on density functional theory (DFT) within the local density approximation (LDA) fail to predict band gaps accurately. To address this issue, the quasi-particle self-consistent GW (QSGW) method is often employed as it is one of the most reliable theoretical approaches for predicting band gaps. Despite its accuracy, QSGW requires significant computational resources. To overcome this limitation, we propose combining QSGW with machine learning. In this study, we applied QSGW to 1,516 materials from the Materials Project [https://materialsproject.org/] and used machine learning to predict QSGW band gaps as a function of the partial density of states (PDOS) in LDA. Our results demonstrate that the proposed model significantly outperforms linear regression approaches with linearly-independent descriptor generation [https://github.com/Hitoshi-FUJII/LIDG]. This model is a prototype for predicting material properties based on PDOS. Quasiparticle ; Band gap ; Function (biology) ; Electronic band structure ; Local-density approximation ; Stability (learning theory) ; Density functional theory Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials
Authors: Jefferson Zhe Liu | Yucheng Wang | Zhen Xing | Songyang Xie | T.J. Cui | Runze Zhan | Yanzhao Ke | Shixi Tang | Huanjun Chen | Li‐Hua Shao | Y. R. Shen | Shaozhi Deng
Source: Photonics Research (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-ultraviolet plasmonic molybdenum nanoholes for adenine detection via deep-learning-enhanced Raman scattering spectroscopy Deep ultraviolet surface-enhanced Raman scattering (DUV-SERS) technology demonstrates its potential for biomolecule detection. Materials exhibiting localized surface plasmon resonance (LSPR) effects generate intense electromagnetic field enhancements, which are critical for improving DUV-SERS detection sensitivity. However, the chemical stability of SERS substrates in complex biological environments remains a substantial challenge in practical applications. In this work, we have developed an LSPR-enhanced DUV-SERS substrate material utilizing molybdenum (Mo) random nanoholes fabricated on monocrystalline films, providing an efficient and anti-corrosion solution for ultrasensitive molecular detection in complex biological environments. Under 325 nm DUV laser excitation, the Mo random nanoholes substrate achieves a Raman enhancement factor (EF) of approximately 10 7 for adenine detection and a detection limit as low as 10 −10 mol/L. Finite-difference time-domain (FDTD) simulations further confirm the Mo’s significant localized field enhancement effect, providing theoretical support for the high EF value. Additionally, a deep-learning-based spectral analysis approach was implemented to achieve a classification accuracy of 92.89% for adenine as a representative of biological molecules. The sensitive and robust Mo nanoholes material offers a cost-effective, high-reliability platform for detection of biomolecules, promoting the future development of portable biosensing and rapid molecular recognition devices. Plasmon ; Raman scattering ; Molybdenum ; Spectroscopy ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Scattering ; Light scattering Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Plasmonic and Surface Plasmon Research