jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: F. J. Dominguez-Gutierrez | T. Stasiak | G. Markovic | A. Kosinska | K. Mulewska
Source: ArXiv.org (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations Refractory complex concentrated alloys (RCCA) exhibit exceptional strength and thermal stability, yet their plastic deformation mechanisms under complex contact loading remain insufficiently understood. Here, the nanoindentation response of an equiatomic MoTaW alloy is investigated through a combined experimental and atomistically resolved modeling approach. Spherical nanoindentation experiments are coupled with large scale molecular dynamics simulations employing a tabulated low dimensional Gaussian Approximation Potential (tabGAP), enabling near DFT accuracy. A physics based similarity criterion, implemented via PCA of load-displacement curves, is used to identify mechanically representative experimental responses for quantitative comparison with simulations. Indentation stress-strain curves are constructed yielding excellent agreement in the elastic regime between experiment and simulation, with reduced Young's moduli of approximately 270 GPa. Generalized stacking fault energy calculations reveal elevated unstable stacking- and twinning-fault energies in MoTaW relative to pure refractory elements, indicating suppressed localized shear and a preference for dislocation-mediated plasticity. Atomistic analyses demonstrate a strong crystallographic dependence of plastic deformation, with symmetric {110}<111> slip activation and four-fold rosette pile-ups for the [001] orientation, and anisotropic slip, strain localization, and enhanced junction formation for [011]. Local entropy and polyhedral template matching analyses further elucidate dislocation network evolution and deformation-induced local structural transformations. Overall, this study establishes a direct mechanistic link between fault energetics, orientation-dependent dislocation activity, and experimentally observed nanoindentation behavior in RCCA. Nanoindentation ; Stacking-fault energy ; Molecular dynamics ; Plasticity ; Indentation ; Dislocation ; Deformation mechanism ; Anisotropy ; Deformation (meteorology) ; Alloy 2D Materials and Applications ; High Entropy Alloys Studies ; Boron and Carbon Nanomaterials Research
Authors: Chunxiao Gao | Ranyou Zhao | Xinyi Liu | Zhaoxi Yu | Ning Xue | Runqi Sun | Xiaoheng Shangguan | Jiajun Wang | Qi Zhang | Hao Wang | Kunteng Huang | Shuai Deng | Li Zhao
Source: ACS Sustainable Chemistry & Engineering (article, 2026-01-12)
Field: Chemistry — Inorganic Chemistry

A Multi-Indicator Weighted Screening Strategy of Materials: Integrating Process Evaluation into Machine Learning-Assisted High-Throughput Screening for SF <sub>6</sub> Recycling The rapid development of metal–organic frameworks (MOFs) has created opportunities for identifying efficient adsorbents for SF6 recycling. However, existing studies have primarily relied on single-performance criteria in high-throughput screening and have overlooked a systematic assessment of material behavior under the temperature swing adsorption (TSA) cycle. To address this gap, this paper first proposes a multi-indicator weighted screening strategy of materials that integrates process evaluation into machine learning (ML)-assisted high-throughput screening for SF6 recycling. By integrating molecular simulations, this strategy is applied to identify MOFs exhibiting superior SF6/N2 separation potential during the TSA process. A novel decision index (ZTOPSIS) combining regenerability and the trade-off between selectivity and working capacity (TSN) is first introduced, enabling the successful selection of the top 10 MOFs from prescreened candidates. Six ML models are developed to predict material performance and identify key influencing features. The TSA cycle simulations are conducted for the top 10 materials, providing a comprehensive evaluation and multiobjective optimization of cycle performance indicators. The results indicate that 10 MOFs have ZTOPSIS values exceeding 0.8. The XGB model performs optimally in predicting TSN and ZTOPSIS, with R2 of 0.835 and 0.816, respectively. SF6 adsorption heat, PLD, C%, VF, and N2 adsorption heat are identified as the primary factors affecting the material performance. Among the top 10 MOFs ranked by ZTOPSIS, AZIVAI performs best in recovery and exergy efficiency, reaching 99.36 and 23.66%, respectively, along with a high purity of 99%. This study contributes to selecting ideal SF6 recycle materials that achieve both effective separation and low energy consumption. Process (computing) ; Exergy ; Performance indicator ; Selection (genetic algorithm) ; Adsorption ; Multi-objective optimization ; Metal-organic framework ; Pressure swing adsorption ; Exergy efficiency Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Anqi Li | Rena Oh | Yanrong Ren | Liang Zhao | Xiaoyang Huang
Source: Molecular Catalysis (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

Machine learning for advanced oxidation catalysis: from descriptors to inverse design Inverse ; Inverse problem ; Support vector machine ; Pattern recognition (psychology) ; Design of experiments Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Natrayan Lakshmaiya | Naveen Kilari | Naga Dheeraj Kumar Reddy Chukka | Seeniappan Kaliappan | Anand Rajendran | Ramya Maranan
Source: Results in Engineering (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Bayesian-optimized deep neural network surrogate for orientation-driven anisotropic thermal conductivity prediction in hybrid polymer nanocomposites • Proposes a physics-informed Bayesian-optimized DNN surrogate for anisotropic thermal conductivity prediction. • Introduces three engineered descriptors (OAI, ISD, SHF) capturing orientation and hybrid synergy effects. • Achieves perfect predictive accuracy with strong generalization. • Enables rapid orientation–composition mapping for hybrid BN–rGO nanocomposites. • Provides a scalable AI-driven alternative to experimental trial-and-error composite design. Efficient prediction of anisotropic thermal conductivity in hybrid polymer nanocomposites remains a major challenge due to the nonlinear coupling among filler morphology, interfacial physics, and processing-induced orientation. This study presents a physics-informed, data-driven methodology for constructing a high-fidelity surrogate model that accurately estimates in-plane and through-plane thermal conductivity of nanocomposites reinforced with boron nitride (BN), silane-treated reduced graphene oxide (rGO), and silver nanoparticles (AgNPs). A comprehensive secondary dataset was aggregated from published experimental studies, capturing filler characteristics, hybrid ratios, processing parameters, alignment strategies, and anisotropic conductivity responses. After extensive preprocessing including missing-value imputation, min–max normalization, and three composite-specific engineered descriptors were formulated: the Orientation Anisotropy Index to represent directional heat-flow pathways, the Interfacial Surface Density to quantify matrix–filler thermal coupling, and the Synergistic Hybrid Factor to describe cooperative multi-filler effects. A Deep Neural Network surrogate was implemented using Python and trained with stratified data splits. To achieve optimal architectural depth and generalization, Bayesian Optimization with a Gaussian Process surrogate and Expected Improvement acquisition function was employed. This approach enabled efficient exploration of the hyperparameter space, resulting a substantially enhanced predictive architecture. The optimized DNN achieved an R² of 0.99995, with RMSE and MAE values reduced to 0.00131 and 0.000998, demonstrating a notable improvement over existing baseline models such as Gradient Boosting Decision Tree, Multi-Layer Perceptron, FFANN, etc. (≈12–18% accuracy gain. Overall, the proposed Bayesian-optimized surrogate establishes a fast, reliable, and physics-consistent predictive engine, offering significant potential to accelerate composite design and guide orientation-controlled fabrication strategies in next-generation thermal interface materials. Thermal conductivity ; Anisotropy ; Artificial neural network ; Polymer ; Conductivity ; Polymer nanocomposite Thermal properties of materials ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Tien-Sinh Vu | Minh-Quyet Ha | Hieu‐Chi Dam
Source: Molecular Physics (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Attention-based machine learning analysis of Pt–SWNT structure and catalysis Catalysis ; Process (computing) ; Feature (linguistics) ; Computational learning theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Sarfaraz Pathan
Source: Technix International Journal for Engineering Research (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Non Destructive Curcumin Quantification in Turmeric Rhizomes: A Comprehensive Review of Spectral, Spatial, and Deep Learning Approaches This chapter presents a comprehensive review of hyperspectral imaging (HSI)–based approaches for non-destructive quantification of curcumin concentration in fresh turmeric rhizomes. Conventional laboratory techniques such as High-Performance Liquid Chromatography (HPLC), although accurate, are destructive, time-consuming, and unsuitable for large-scale or real-time quality assessment. Hyperspectral imaging integrates imaging and spectroscopy to capture both spatial and spectral information, enabling pixel-level chemical analysis. This chapter reviews curcumin chemistry, hyperspectral acquisition systems, spectral preprocessing techniques, chemometric and machine learning methods, and recent advances in deep learning–based spectral–spatial models. Insights from experimental studies using fresh turmeric rhizomes and HPLC ground truthing are incorporated to position deep learning frameworks, particularly three-dimensional convolutional neural networks (3D-CNNs), as a robust and scalable solution. Challenges, limitations, and future research directions, including cloud-based deployment, are also discussed. Hyperspectral imaging ; Deep learning ; Convolutional neural network ; Curcumin ; Preprocessor ; Pattern recognition (psychology) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Curcumin's Biomedical Applications ; Remote-Sensing Image Classification
Authors: Zhixin Qin | Zhihui Song | Heying Feng | Li Jin
Source: Carbon and Hydrogen (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Computational and Machine‐Learning Studies of Ethylene Oligomerization ABSTRACT Linear alpha olefins (LAOs) are important petrochemical precursors for the synthesis of polymers, lubricants, plasticizers, and detergents. Currently, LAOs are usually synthesized via ethylene oligomerization using transition metal catalysts. Stimulated by the industrial advances, the questions on the reaction mechanism, origins of reactivity and selectivity have emerged, as well the need of rational design of catalysts. In this context, computational and machine‐learning studies not only elucidate the experimental observations from an energetic and molecular perspective but also provide essential insights into the structure–property relationship and the design of catalysts in the oligomerization. This review focuses on recent advances in computational and machine‐learning studies of ethylene oligomerization, highlighting mainstream catalyst systems based on Co, Ta, Ti, Zr, and Hf, with particular emphasis on Fe‐ and Cr‐based catalysts and their controlling factors governing reactivity and LAO distribution. Reactivity (psychology) ; Ethylene ; Catalysis ; Petrochemical ; Rational design ; Selectivity Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Organometallic Complex Synthesis and Catalysis
Authors: Ivo Neefjes | Jakub Kubečka | Jonas Elm
Source:  (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Supplementary material to "Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules" Molecular dynamics ; Collision ; Interatomic potential ; Dynamics (music) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Gas Dynamics and Kinetic Theory
Authors: Pragyan Goswami | R. K. Singh Raman | Alankar Alankar
Source: Small Structures (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning Strategies to Circumvent Variabilities in Chemical Vapour‐Deposited Graphene Coatings for Remarkable Corrosion Resistance of Metals Graphene coatings, derived through chemical vapour deposition (CVD), offer a promising solution for corrosion mitigation due to their unique combination of impermeability, inertness and toughness. While pristine graphene coatings significantly enhance corrosion resistance, variabilities in the properties and the performance of these coatings, when influenced by CVD process parameters, pose a challenge. Variations in defect content, largely due to differing CVD methodologies across research groups, significantly affect corrosion resistance, ranging from notable improvements to potential acceleration of corrosion. To systematically address these variabilities, this review proposes the integration of machine learning (ML) techniques to optimise CVD parameters, thereby achieving more consistent and high‐quality graphene coatings. Moreover, recent advancements in artificial intelligence, specifically large language models (LLMs), have the potential to revolutionise this domain by enabling sophisticated data analysis and hypothesis generation from vast quantities of literature. These capabilities can accelerate the optimisation of graphene production processes. By integrating LLMs, researchers can quickly synthesise and assess vast amounts of published data, refine process parameters and predict outcomes more accurately. This approach not only enhances the understanding of CVD graphene's behaviour under different conditions but also drives the innovation of robust methodologies for the development of corrosion‐resistant coatings. Graphene ; Corrosion ; Chemical vapor deposition ; Process (computing) ; Deposition (geology) Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Electrocatalysts for Energy Conversion
Authors: Yunwei Gui | Y.F. Li | Yihuan Cao | Zihan Jia | Yuchao Lei
Source: Journal of Alloys and Compounds (article, 2026-01-01)
Field: Materials Science — Biomaterials

Hierarchical microstructure evolution and strengthening in electron beam remelted Mg-RE alloy with machine learning-predicted processing window Microstructure ; Process window ; Texture (cosmology) ; Alloy ; Thermal conduction ; Classification of discontinuities ; Grain size ; Cathode ray ; Heat transfer Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Ossi Laurila | Tiia Jacklin | Ouail Zakary | Perttu Lantto
Source: The Journal of Physical Chemistry A (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Path Integral Molecular Dynamics and <sup>13</sup> C NMR Simulations Unlock New Insights into Quantum Effects in C <sub>60</sub> Fullerene A definitive answer to the existence and magnitude of the negative thermal expansion (NTE) and the <sup>13</sup>C nuclear magnetic resonance (NMR) signature in C<sub>60</sub> fullerene has been previously demonstrated using quantum-mechanical treatments of thermal rovibrational motion. This approach, while accurate, is computationally expensive, lacks the implementation of dispersion corrections, and is fundamentally limited to systems with well-defined equilibrium geometries and sufficiently strong restoring forces, making it inapplicable to weakly bound van der Waals complexes. Alternative methods, such as ab initio path integral molecular dynamics (PIMD), are more flexible but remain computationally expensive, especially when combined with calculations of the <sup>13</sup>C NMR parameters. To overcome these limitations, we introduce an accurate and efficient neural network-based approach that combines machine learning interatomic potentials (MLIPs) with an NMR machine learning (NMR-ML) model. MLIPs enable machine learning PIMD (MLPIMD) simulations, while the NMR-ML model computes <sup>13</sup>C isotropic magnetic shielding, σ<sub>iso</sub>, directly from MLPIMD snapshots. We perform temperature-dependent MLPIMD simulations with MLIPs trained at different levels of theory. In all cases, NTE is observed, and the results reveal how both dispersion effects and atomic basis set choices influence its magnitude. Furthermore, we confirm that NTE is a quantum-mechanical phenomenon, and hence, classical MD simulations cannot reproduce it. To further test our approach, we investigate fully quantum-mechanical secondary isotope shifts of <sup>13</sup>C NMR magnetic shielding due to the isotope change from <sup>12</sup>C to <sup>13</sup>C of the immediate neighbor with hexagon-hexagon or hexagon-pentagon bonds with the observed nucleus. The results show good agreement with the experimental data, highlighting the accuracy of our approach. This work demonstrates that ML-accelerated simulations enable accurate and efficient modeling of thermally activated quantum mechanical phenomena. van der Waals force ; Molecular dynamics ; Ab initio ; Dispersion (optics) ; Isotropy ; Fullerene ; Path (computing) ; Kernel (algebra) ; Quantum Thermal Expansion and Ionic Conductivity ; Fullerene Chemistry and Applications ; Thermal properties of materials
Authors: Aishwarya Sudhama | Z. Zhang | Chao Zhang
Source: ChemRxiv (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Foundation model distillation with PiNNAcLe: application to the ZnCl2 aqueous electrolyte solution Aqueous zinc chloride solutions are central to zinc-based electrochemical energy storage and electrochemical sensors, yet their local structure and dynamics remain challenging to characterize without atomistic simulation. Foundation models for materials chemistry can substantially accelerate such simulations, but they often impose significantly higher computational overhead than bespoke machine-learning interatomic potential. Here we bridge these two regimes by developing a workflow that distils a foundation model into our lightweight PiNet2 architecture, enabled by an extension of PiNNAcLe (Pair-wise Interaction Neural Network with Active Learn-on-the-fly). We demonstrate the approach for ZnCl2 (aq), and show that the framework can seamlessly switch between two foundation models at different density functional theory levels. Structural and dynamical properties from the distilled models have been validated against the foundation models as well as the density functional theory-based molecular dynamics simulations at matched levels of theory. This work provides a model-agnostic route to harnessing foundation models with low-cost bespoke models, enabling routine, scalable simulations of electrolyte systems. Foundation (evidence) ; Bespoke ; Molecular dynamics ; Density functional theory ; Aqueous solution ; Electrolyte ; Workflow ; Distillation Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Qianzhuo Lei | Yihan Zhang | Pengcheng Liu | Xihui Bian | Xijun Liu | Jia He
Source: ChemPhysChem (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Electrocatalysis Informatics Driven Design of Formic Acid‐Producing Alloy Catalysts for CO <sub>2</sub> Electroreduction Machine learning (ML)-enabled high-throughput screening to predict potential electrocatalysts for the CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR) offers new insights for energy conversion and environmental remediation. In this work, for the first time, we established a comprehensive electrocatalytic database containing ≈400 entries of CO<sub>2</sub>RR catalysts. Through decision tree analysis, correlation heatmaps, and feature importance ranking, we systematically decoded structure-property relationships. Among the tested algorithms, the nonlinear tree-ensemble method Random Forest Regression demonstrated superior predictive performance for CO<sub>2</sub>RR systems. Subsequent screening of 500 000 catalyst configurations generated by the the sequential model-based algorithm configuration method, using Expected Improvement as the evaluation metric, identified promising multinary alloy catalysts for C1 molecule production. Notably, BiSb-based alloys emerged as high-potential candidates for CO<sub>2</sub>RR applications. This ML-driven paradigm highlights the growing significance of artificial intelligence in materials discovery, synergistically combining screening efficiency, prediction accuracy, and proficiency in big data processing. Electrocatalyst ; Decision tree ; Alloy ; Catalysis ; Materials informatics ; Random forest ; Feature (linguistics) ; Reduction (mathematics) ; Formic acid Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Sehyeok Oh | Jaimyun Jung | Junyong Choi | Yoon-Ho Lee | Young-Hee Cho | Mingi Oh | Ho Won Lee
Source: Journal of Alloys and Compounds (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Prediction of elastic modulus, Poisson’s ratio, and phase volume fraction from XRD pattern in high-elasticity aluminum alloys for casting using deep learning This study presents a deep learning framework for rapid prediction of mechanical properties–elastic modulus E and Poisson’s ratio ν –and phase volume fraction in high-elasticity Al-Si-Ni casting alloys directly from X-ray diffraction (XRD) data. A total of 421 experimentally measured datasets were constructed, each comprising XRD pattern, chemical composition, casting cooling rate, and mechanical properties, while phase volume fraction was obtained from thermodynamic calculation. Chemical composition is used as an additional input for elastic modulus prediction, with cooling rate further included for Poisson’s ratio, whereas phase volume fraction is predicted solely from XRD data. To overcome limited amount of data, a variance-preserving denoising diffusion probabilistic models (DDPM)-based augmentation method expands the number of training data tenfold, improving prediction accuracy while maximally preserving peak information and mass fractions for critical phases. Convolutional ResNet-based regressors achieves high predictive performance, yielding mean absolute errors of 0.91 GPa for elastic modulus, 0.0050 for Poisson’s ratio, and 0.014 vol.% for phase fractions. A multi-task integrated model enables simultaneous prediction of both mechanical properties with competitive accuracy. Quantitative explainable AI analysis reveals key diffraction peaks and confirms that the model learns physically interpretable representations. • 421 datasets included composition, cooling rate, XRD, E , ν , and phase fractions. • DDPM forward diffusion augmented XRD tenfold, improving prediction R² by 5–20%. • ResNet models predicted E , ν , and phase volume fraction with high accuracies. • A multi-head regressor jointly predicted E and ν with good performance. • Explainable AI showed that predictions used physically meaningful features. Volume fraction ; Aluminium ; Casting ; Phase (matter) ; Deep drawing Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties