jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengmei, Zihan | Guo, Spencer C. | Lorpaiboon, Chatipat | Dinner, Aaron R.
Source: arXiv (Cornell University) (preprint, 2025-12-06)
Field: Materials Science — Materials Chemistry

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability. Scalability ; Context (archaeology) ; Deep learning ; Feature (linguistics) ; Graph ; Artificial neural network ; Deep neural networks Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Graph Neural Networks
Authors: Sayak Chakrabarty (22852958) | Kamal Choudhary (2813026) | Daniel Wines (9455510) | Youjia Li (7858268) | Vishu Gupta (11113577) | Muhammed Nur Talha Kilic (22852961) | Alok Choudhary (3618293) | Ankit Agrawal (1354770)
Source: Figshare (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Predicting LatticeParameters from Atomic-Scale Imagesof Two Dimensional Materials Using Deep Learning Determining lattice parameters in two-dimensional (2D) materials is essential for materials characterization and discovery. In this work, we propose a deep-learning-driven pipeline that addresses the regression task of estimating the lattice constants <i>a</i> and <i>b</i> and the angle γ directly from 2D images using computer vision. We evaluate our approach on three different 2D material data sets: JARVIS-2D (JV2D) and Computational 2D Materials Database (C2DB), and a newly created data set derived from the Alexandria database. Multiple architectures are compared, including DenseNet121, Vision Transformers (ViT-L/14) paired with Multi Layer Perceptron (MLP) heads, and GoogleNet. DenseNet121 achieves accurate performance, with mean absolute errors as low as 0.18 Å for the Alexandria-based data set and 0.17 Å for C2DB and 0.59 Å for JV2D, as well as up to 96% accuracy in classifying Bravais lattice types for the Alexandria-based data set. Bravais lattice ; Lattice (music) ; Deep learning ; Pattern recognition (psychology) ; Perceptron ; Lattice constant ; Artificial neural network Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Hong Zhu | Hui Guo | Zhihui Liu | Zhao‐Xu Chen
Source: The Journal of Chemical Physics (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Prediction of adsorption energies of CmHnOp (m ≤ 2, n ≤ 6, p ≤ 2) on transition metals and alloys with machine learning methods Metal-based catalysts are widely used in many kinds of reactions, including the hydrogenation of CO2 to alcohols. Adsorption energies of key intermediates have often been used as descriptors in high-throughput catalyst screening. However, establishing machine learning models to accurately predict adsorption energies of widely spanned species is still challenging. In the present article, we explored the predictive power of sure independence screening and sparsifying operator (SISSO), multilayer perceptron regression (MLPR), random forest regression (RFR), kernel ridge regression (KRR), support vector regression (SVR), eXtreme Gradient Boosting (XGBoost), and 20 ensemble machine learning (ML) methods for adsorption energies of 57 species involved in CO2 hydrogenation to ethanol using surface features, adsorbate features, and adsorption site features. The results show that SISSO and the five base ML methods cannot furnish models with the maximum absolute error (MAX) comparable to DFT errors. On the other hand, the MAX of most two-component and three-component ensemble ML methods is less than 0.3 eV, and the KRR+MLPR+XGBoost ensemble ML model performs the best, with the mean absolute error being 0.03 eV and MAX of only 0.17 eV. Feature importance analysis reveals that the condensed local softness is the most important feature, and there are linear relations between the condensed local softness and adsorption energies of 10 C1 species on all considered surfaces. The present work shows that ensemble ML methods outperform the base ML methods for predicting adsorption energies of widely ranged species with satisfactory accuracy and deserve further studies. Adsorption ; Extreme learning machine ; Ensemble learning ; Support vector machine ; Perceptron ; Gradient boosting ; Chemistry ; Random forest ; Boosting (machine learning) ; Feature selection ; Linear regression ; Artificial intelligence ; Machine learning ; Computer science ; Mathematics ; Artificial neural network ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Dhamodhara Pandi S | A. Naveen Arasu | R. Ruthra | M K Logesh | Ashwin K Kanakasapay | K. Karthikeyan
Source:  (article, 2025-04-07)
Field: Materials Science — General Materials Science

Deep Hybrid Neural Network Models for Estimating Compressive Strength in Eco-Friendly Materials The cement industry's significant carbon dioxide emissions considerably impact greenhouse gas levels, establishing it as a major polluter. Moreover, the extraction of natural resources for concrete manufacture has been excessive due to rapid population growth and escalating construction demands. This depletion has created an urgent need for environmentally benign alternatives that do not compromise compressive strength. The paper examines four data transformation methods: statistical normalisation, min-max normalisation, non-linear transformation, and whitening transformation. The Autofeat approach is employed for automatic feature engineering and selection to enhance data preprocessing. The application of inter- and intra-periodic convolutional blocks facilitates the extraction of superficial features from sequences of varying dimensions utilising a CNN model with a DSDB architecture. To enhance the extraction of profound sequence information, a transformer module using GWT captures comprehensive temporal dependencies. The proposed approach facilitates the prediction of the compressive strength of eco-friendly materials with an accuracy of 97.46%. These results illustrate that advanced data transformation and feature extraction enhance the prediction of material performance, highlighting the potential of deep learning to optimise sustainable construction materials, thereby facilitating more efficient and environmentally sustainable building solutions. Artificial neural network ; Environmentally friendly ; Compressive strength ; Computer science ; Materials science ; Artificial intelligence ; Composite material Material Properties and Applications ; Materials Engineering and Processing ; Additive Manufacturing and 3D Printing Technologies
Authors: Yufan Xia | Giuseppe M. J. Barca
Source: ChemRxiv (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Machine-Learning Adaptivity, DFT-Level Accuracy, and Semi-Empirical Quantum-Chemistry Speed with Neural-Network Extended Tight-Binding Quantum ; Sensitivity (control systems) ; Operator (biology) ; Observable ; Set (abstract data type) ; Equivariant map ; Path (computing) ; Topology (electrical circuits) ; Component (thermodynamics) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Xiaoyuan Zhang | Lifei Wang | Shi Qiu | Xiaoyu Feng | Yunqi Ning | Bin Xing | Jingyu Yang | Ruidi Li | Liwei Lu | K.K Deng | Hongxia Wang | Kwang Seon Shin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Multi-Objective Optimization Accelerated Design of Novel High-Performance Biodegradable Mg Alloys Via Machine Learning Materials science ; Computer science Magnesium Alloys: Properties and Applications
Authors: Princy Randhawa | Ruchita R Patil | V. Shwetha | Harshada Mhetre | Arunkumar Bongale | Satish Kumar
Source: Research Square (preprint, 2025-09-08)
Field: Materials Science — Biomaterials

Explainable Machine Learning Framework for Optimizing Electrospinning Parameters in ZnO-PVP Nanofiber Synthesis Electrospinning ; Nanofiber ; Polyvinylpyrrolidone ; Support vector machine ; Fiber ; Segmentation ; Volumetric flow rate ; Linear regression Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Erfan Mohammadipour | Faezeh Adib | Mohammad Ghorbani
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

“Machine Learning–Guided Optimization of PEO Coating Parameters Using Support Vector Regression for Enhanced Corrosion Resistance of AZ31 Magnesium Alloy” Corrosion ; Coating ; Plasma electrolytic oxidation ; Process optimization ; Magnesium alloy ; Process (computing) ; Magnesium ; Support vector machine ; Current density Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings
Authors: Yuanhui Sun (1485985) | Austin Ellis (18769758) | Xin Chen (14149) | Maosheng Miao (1728712)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Batch Discovery ofComplex Metal Superhydrides viaan Effective Machine Learning Method Structured by Chemical TemplateConcept Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Despite extensive studies on binary metal superhydrides, there remains a large, unexplored chemical space, particularly regarding noninteger hydrogen-to-metal ratios. By integrating the “chemical template effect” with machine learning algorithms, we developed a specialized structure discovery workflow that significantly enhances the efficiency of predicting stable superhydrides. Within the compiled data set used in this work, our method led to the identification of 13 new structural prototypes and 31 stable metal superhydrides, representing a 23% increase in discoveries. The 3D hydrogen clathrates in these compounds are significantly correlated with high superconducting transition temperatures (<i>T</i><sub>c</sub>), and our approach achieves a remarkable 65% increase. Most of these structures contain over 50 atoms per primitive cell, with the <i>I</i>4/<i>m</i> M<sub>10</sub>H<sub>84</sub> prototype having the largest unit cell, containing 94 atoms. Additionally, 19 of the newly identified superhydrides exhibit <i>T</i><sub>c</sub> > 100 K, highlighting the potential for higher <i>T</i><sub>c</sub> materials within the 3D hydrogen clathrate structures. The method also shows good potential to search for ternary superhydrides on a large scale. Workflow ; Hydrogen ; Ternary operation ; Set (abstract data type) ; Identification (biology) ; Metal ; Binary number Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Inorganic Chemistry and Materials
Authors: Hua Yan | Hongxu Jiang
Source: Informatica (article, 2025-11-23)
Field: Materials Science — Polymers and Plastics

A Modular Deep Learning Pipeline (CNN+U-Net+GAN) for Color-Accurate, Cross-Material Digital Textile Printing with Transfer-Learning-Based Material Adaptation Precise color reproduction and efficient pattern generation are the core goals of digital printing on clothing. To break through the limitations of traditional processes that rely on manual parameter adjustment and sample fabric trial and error, this paper proposes an intelligent printing generation framework based on deep learning. This framework integrates CNN color management, deep segmentation and loop optimization, GAN-driven 3D virtual rendering and transfer learning material adaptation, and can achieve end-to-end pattern generation and computational optimization on multi-material data such as cotton fabric, silk and polyester. The system not only captures the spatial detail features of the patterns (such as edge sharpness and color gradation), but also maintains color consistency and detail restoration among different materials through cross-domain modeling. The experimental results show that on multi-material datasets, this scheme achieves ΔE 1.9±0.2across cotton/silk/polyester (mean over 3 runs), which corresponds to a 30–45% reduction versus screen printing (ΔE≈4.1) and 15–25% versus a commercial inkjet baseline (ΔE≈2.3). It reduces splicing fracture rate to &lt;4%, shortens average processing time by ~60% (12 h→4.8–8.5 h depending on batch size), and increases SSIM to 0.93±0.01.All statistics are mean±std over three independent runs; significance is assessed with paired t-tests or ANOVA with Bonferroni correction at α=0.05. This research not only verified the effectiveness of deep learning in digital printing, but also provided an expandable intelligent path for the integration of the clothing design and production chain, offering significant support for the transformation of the fashion industry towards personalization, greenness and intelligence. RGB color model ; Modular design ; Deep learning ; Rendering (computer graphics) ; Segmentation ; Pipeline (software) ; Transformation (genetics) ; Color space Textile materials and evaluations ; Color Science and Applications ; Dyeing and Modifying Textile Fibers
Authors: Walter Bonke Mahlangu | Nomasonto Rapulenyane | Taurai Hungwe | Somandla Ncube 
Source: International Journal of Innovative Research and Scientific Studies (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Classification of organometallic reactions using machine learning Classifying organometallic reactions into distinct reaction types is fundamentally important for understanding mechanisms and predicting reactions, for synthesis optimisation. Fundamental to classification of organometal reactions is reaction representation, but current methods often fail to capture organometal chemical transformation adequately. The study has adopted a hybrid fingerprinting approach, whereby new fingerprints were concatenated with permutation important Morgan fingerprints to create 49 to 63 bits fingerprints. The hybrid fingerprints were used to build KMeans clustering, Guassian mixtures, Affinity propagation and Heirarchical clustering models for classification of organometal reactions. The models successfully classified reactions into 6-8 major organometal reaction types. Of note, the fingerprints consistently outperformed Morgan fingerprints across all clustering models with good Davies–Bouldin Index (DBI) ranging from 0.3 to 0.6 and Silhouette score from 0.3 to 0.8. Furthermore, the clustering models were visualized using Principal Component Analysis. Affinity Propagation and KMeans demonstrated superior performance over Hierarchical and GMM algorithms in distinguishing major reaction categories. In contrast, the Hierarchical model excelled at identifying sub-level classifications compared to the other methods. Consequently, Affinity Propagation and KMeans are recommended for broad reaction type classification, while the Hierarchical approach is better suited for resolving detailed subclass distinctions. The observed variability in model performance further highlighted the importance of feature selection and representation in clustering models. Future studies should look into improving the fingerprints to recognize subcategories. Cluster analysis ; k-means clustering ; Pattern recognition (psychology) ; Silhouette ; Representation (politics) ; Principal component analysis ; Feature (linguistics) ; Hierarchical clustering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Vikram G. Kamble
Source: Next Materials (article, 2025-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A multiscale review of magnetic permeability in polymer composites: Theory, simulation, and machine learning frontiers Polymer-based magnetic composites (PMCs) are vital in applications such as sensors, EMI shielding, and inductors, yet predicting their effective magnetic permeability (μ eff ) remains challenging. This review critically analyzes classical models like Maxwell Garnett and Bruggeman, which offer foundational insights but fall short in systems with clustering, nonlinearity, or high filler fractions. Advanced techniques—such as finite element methods, phase-field modeling, and micromechanical frameworks—address these gaps by accounting for real geometries and multiphysics interactions, including magnetoelastic coupling and domain evolution. The role of elasticity networks and interfacial dynamics is emphasized, alongside the rise of machine learning for modeling complex structure-property relationships and enabling multi-objective optimization. Despite progress, challenges persist in dynamic hysteresis modeling, interphase parameterization, and morphology-percolation linkage. This review advocates for hybrid modeling approaches that integrate physics-based and data-driven methods to design next-generation PMCs with predictive accuracy and enhanced functionality across electromagnetic and multifunctional domains. Multiphysics ; Finite element method ; Coupling (piping) ; Interphase ; Hysteresis ; Magnetic nanoparticles ; Permeability (electromagnetism) Magnetic Properties and Applications ; Electromagnetic wave absorption materials ; Composite Material Mechanics
Authors: Yufan Xia | Joshua Soon | Albert Thie | Giuseppe M. J. Barca
Source: ChemRxiv (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Machine-Learning Adaptivity, DFT-Level Accuracy, and Semi-Empirical Quantum-Chemistry Speed with Neural-Network Extended Tight-Binding Hamiltonian (control theory) ; Observable ; Quantum ; Operator (biology) ; Set (abstract data type) ; Charge (physics) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Junhao Liang | Caiyuan Ye | Xinyi Lin | Chunlin Yu | Yilimiranmu Rouzhahong | Chao Liang | Huashan Li
Source: The Journal of Physical Chemistry Letters (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Machine Learning Integrating Surface Features and Crystal Similarity for Exploring 2D Materials The property prediction of two-dimensional (2D) materials has been constrained to specific systems or the highly self-similar Computational 2D Materials Database (C2DB) due to limited data availability and inadequate feature extraction. These challenges impede extrapolated predictions and large-scale exploitation across diverse databases. We developed a model named crystal surface and cluster network (CCSN) that integrates crystal surface and cluster features while leveraging transfer learning from bulk material databases to enhance model performance. Compared to the widely adopted CGCNN model, our approach achieved a 30% reduction in mean absolute error for bandgap prediction on the Materials Cloud 2D crystals database, characterized by low self-similarity and limited data volume. To enable extrapolated predictions, we developed a prediction workflow based on crystal similarity, which selects the most similar database for model training and determines the necessity of applying transfer learning. This method was applied to predict bandgaps of 8,218 crystals without bandgap values in the C2DB database and subsequently validated through our DFT calculations. The proposed prediction workflow, based on the CCSN model, would enable the discovery of 2D materials through rapid property prediction and can be generalized to improve any prediction models dealing with scarce or biased data sets. Similarity (geometry) ; Surface (topology) ; Materials science ; Crystal (programming language) ; Nanotechnology ; Artificial intelligence ; Computer science ; Geometry ; Mathematics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Mihandoost, sara | Rezvantalab | Imanpour, Aylar | Dabiri | Pourghasem | Turňa | Stuchlík
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Molecular Dynamics for Predicting Polymeric Nanocarriers' Interactions with RNA RNA ; Molecular dynamics ; Dynamics (music) ; Identification (biology) Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Computational Drug Discovery Methods
Authors: L Torrealba M
Source:  (article, 2025-11-18)
Field: Materials Science — Polymers and Plastics

Machine Learning-Based Prediction of Tensile Strength in Pineapple Fiber Reinforced Polymer Composites Incorporating CuO Filler Using Random Forest, Decision Tree, and XGBoost Algorithms The purpose of this study is to use sophisticated machine learning (ML) techniques to estimate tensile strength in pineapple fiber reinforced polymer composites containing CuO. Experimental tensile strength data were gathered and utilized to train and assess three prominent machine learning algorithms, such as Random Forest (RF), Decision Tree (DT), and XGBoost. The predictive efficacy of these models was evaluated by comparing real and expected tensile strength values, which revealed insights into their ability to capture nonlinear correlations in composite behavior. The results showed that all three models could reasonably mimic the experimental data, demonstrating the promise of machine learning-based approaches for predicting composite material properties. The evaluation criteria demonstrated that Random Forest and Decision Tree obtained nearly matched accuracy, while XGBoost performed competitively with somewhat higher error rates. The comparison between the actual and predicted tensile strength values confirms the reliability of the ML models, as their outputs closely follow the experimental trends. This reinforces advancements in Industry, Innovation and Infrastructure by promoting data-driven material characterisation, and supports Responsible Consumption and Production through more efficient, informed, and resource-conscious composite design. Ultimate tensile strength ; Decision tree ; Random forest ; Composite number ; Fiber ; Nonlinear system ; Reliability (semiconductor) ; Filler (materials) Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Innovative concrete reinforcement materials