jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xue Qiyang | Wei Jiang
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Adaptive Fractal Representation Learning for Data-Efficient Materials Property Prediction Predicting material properties is a cornerstone of materials discovery but remains challenging for geometrically complex crystals (e.g., those with large unit cells or low symmetry), where conventional descriptors fail to capture multiscale structural irregularities and graph-based models incur high computational costs. Existing approaches often struggle to balance representational expressivity with data efficiency. In this study, we propose FractalNet-Plus, a dual-branch architecture that addresses this gap by combining domain-informed geometric features with multiscale fractal characterizations. Our method extracts six complementary fractal metrics—box-counting, mass, correlation, and information dimensions, lacunarity, and multiscale entropy—applying adaptive weighting to identify the most informative scales. Evaluations on complexity-filtered subsets of five Matbench tasks (thermodynamic stability, band gap, shear modulus, perovskite band gap, 2D exfoliation energy) demonstrate that our hybrid representation consistently improves accuracy, achieving MAE reductions of 17% for thermodynamic stability and 13% for band gaps compared to state-of-the-art baselines. For geometrically complex crystals, FractalNet-Plus achieves substantially lower prediction error while using fewer parameters and training more efficiently than graph neural networks, offering a computationally efficient and interpretable approach for materials property prediction. Fractal ; Property (philosophy) ; Representation (politics) ; Weighting ; Stability (learning theory) ; Graph ; Embedding ; Artificial neural network ; Lattice (music) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; CO2 Reduction Techniques and Catalysts
Authors: Kawther Laajimi | Radhouane Laajimi | R. M’nassri
Source: Emergent Materials (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning based technique for characterization of magnetocaloric entropy performance: a study on formula (La0.67Ca(0.33-x)SrₓMn0.98Ni0.02O3)1-y/(La0.67Ca0.33MnO3)y manganite composites Magnetic refrigeration ; Manganite ; Mean squared error ; Artificial neural network ; Entropy (arrow of time) ; Magnetic field ; Composite number ; Characterization (materials science) ; Coefficient of determination Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Multiferroics and related materials
Authors: İsmail Enes Parlak | Cantekin Kaykılarlı | Taha Yasin Eken
Source: Journal of Innovative Science and Engineering (JISE) (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models The growth of industrial development has increased attention to sustainability and efficiency, resulting in greater research advances toward improved performance of materials. The tribological behavior of materials, specifically friction and wear, is one of the most primary topics of interest in material performance advancement. This work provides an advanced machine learning regression-based model for the quantitative prediction of the Coefficient of Friction (CoF) and wear rate of the Ti6Al4V alloy. The novel approach employs an extensive pipeline of advanced feature engineering to inform an ensemble model based on a dataset compiled from the literature. The optimized Gradient Boosting Regressor achieved F1 results in excess of 95% accuracy on an unseen data set (R2 = 0.944; RMSE = 0.020) for CoF predictions, and a stacking regressor/model markedly improved wear rate predictions (R2 = 0.730) compared to baseline models and the CoF predictions for clarification of real-time engineering applications. The ensemble regression model is designed to provide high-fidelity, quantitative benchmarks for Ti6Al4V, which can be used as critical tools for materials design and optimization. The methodology confirmed the models' important physical relevance through feature-importance analysis: Hardness × Load for the CoF models, and Sliding Distance for wear rate. Boosting (machine learning) ; Gradient boosting ; Feature engineering ; Ensemble learning ; Feature (linguistics) ; Pipeline (software) ; Ensemble forecasting ; Regression ; Tribology ; Support vector machine Titanium Alloys Microstructure and Properties ; Tribology and Wear Analysis ; Lubricants and Their Additives
Authors: Liang Zhang | Xihao Chen | Yichao Liu | Zhilian Dai | Donglin Guo | Xiang Meng | Peng Gao
Source: Physica Scripta (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Heat transport investigation of 2D Eu <sub>2</sub> ZnSb <sub>2</sub> monolayer by anharmonic phonon renormalization with inclusion of four-phonon scattering Abstract In this study, we employed the previously proposed anharmonic phonon renormalization technique to analyze heat transport in two-dimensional (2D) alloying materials. To further enhance the methodological framework, we incorporated a machine learning (ML)-based approach to perform a comparative analysis. We specifically focused on the material Eu 2 ZnSb 2 . Traditional calculations using the Boltzmann transport equation (BTE) with harmonic phonons are not suitable for estimating thermal conductivity in these class of materials due to the presence of imaginary frequencies. To overcome this limitation and better understand the dominant factors influencing heat transport, we developed a computational framework based on first-principles theory. Our approach combines the self-consistent phonon (SCP) theory with the BTE to examine the thermal conduction behavior. By resolving the BTE in conjunction with SCP theory, we comprehensively address the effects of quartic anharmonicity, including four-phonon scatterings and the temperature-induced shift of phonon frequencies. Through careful analysis of the calculation results, we have found that considering anharmonicity renormalization is crucial for accurately evaluating thermal conductivity of 2D Eu 2 ZnSb 2 and qualitatively describing the temperature dependency. Overall, our study aims to validate the proposed methodology for investigating the thermal transport mechanisms within 2D alloying phases. By gaining insights into the intricate relationships among structural, mechanical, electronic, and thermal characteristics of these 2D materials, we hope to contribute to a better understanding of their properties and potential applications in microelectronics and thermoelectric devices. Anharmonicity ; Phonon ; Thermal conductivity ; Boltzmann equation ; Renormalization ; Thermoelectric materials ; Thermoelectric effect Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: John Crosby | Haoran Cui | Mehrab Lotfpour | Yan Wang | Lei Cao
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Enhanced Graphene-Water Thermal Transport via Edge Functionalization without Compromising In-Plane Thermal Conductivity Interfacial thermal transport between graphene and water plays a critical role in a wide range of thermal and energy applications. Although chemical functionalization can significantly enhance graphene-water interfacial thermal conductance, it often degrades graphene's intrinsic in-plane phonon transport. In this work, we perform a systematic deep neural network molecular dynamics study comparing edge-functionalized graphene nanoribbons with surface-functionalized graphene in aqueous environments. We demonstrate that functionalizing only 10% of the ribbon edges with hydroxyl groups increases the graphene-water interfacial thermal conductance by more than eightfold, primarily due to strengthened interfacial interactions and improved wettability at the edges. In contrast to basal-plane oxidation, edge functionalization largely preserves in-plane thermal conductivity. Importantly, hydroxyl edge groups exert competing effects on phonon transport: they introduce additional boundary scattering that suppresses heat conduction, while simultaneously passivating dangling bonds at bare edges, thereby reducing phonon localization and edge-induced scattering. This competition leads to a non-monotonic dependence of in-plane thermal conductivity on edge functionalization ratio. These results establish edge functionalization as an effective strategy for enhancing graphene-water interfacial thermal transport without sacrificing intrinsic phonon transport properties. Surface modification ; Thermal conductivity ; Graphene ; Phonon ; Thermal ; Wetting ; Phonon scattering ; Interfacial thermal resistance Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Graphene research and applications
Authors: Wei Li | Siyi Wang | Shengguan Xu | Qiuwang Wang
Source: The Innovation Energy (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven paradigm shift in thermochemical energy storage &lt;p&gt;Thermochemical energy storage (TCES) technology, which enables high-density energy storage and flexible dispatch through reversible chemical reactions, is a critical solution to address the spatiotemporal mismatch of renewable energy. However, TCES technology has been difficult to widely diffuse because of the difficulty in discovering TCES materials with even better properties and the difficulty of TCES systems in accurately predicting the dynamic heat load demand of users in smart energy systems. Traditional research methods relying on experimental trial and error and simplified numerical simulations face challenges such as high costs, prolonged experimental cycles, and insufficient prediction accuracy. It is imperative that novel research tools be employed as soon as possible to address these two major issues. Machine Learning (ML), as a data-driven intelligent tool, transforms the exploration of causal relationships into correlational analyses, demonstrating exceptional potential in TCES for novel material screening and system optimization due to its advanced data processing capabilities, low hardware cost requirements, and high prediction accuracy. This perspective intensively describes the methodology for embedding ML algorithms in TCES, as well as describing the characteristics of the various algorithms. Furthermore, it outlines strategies to enhance the application of ML algorithms in TCES technologies, proposing pathways such as establishing standardized TCES databases through extensive data collection to broaden ML applicability, implementing weighted parameter optimization for input variables or combining physical theory in the input process of the underlying data to improve algorithmic stability, integrating transfer learning with ML frameworks to maintain accuracy under data scarcity, and training ML models with region-specific operational data to explore multi-dimensional and multi-objective optimization across the 4E criteria ( Energy, Environment, Economic, Exergy) and interdisciplinary convergence. While current applications of ML in the TCES domain remain in their nascent stages, the technology holds significant potential to drive transformative advancements in future research and industrial deployment.&lt;/p&gt; Process (computing) ; Embedding ; Data collection ; Energy (signal processing) ; Perspective (graphical) ; Energy storage ; Big data ; Renewable energy Machine Learning in Materials Science ; Chemical Looping and Thermochemical Processes ; Adsorption and Cooling Systems
Authors: Ali Forooghi | Shaghayegh Sadeghi | Luis Rueda | Alioune Ngom
Source: Briefings in Bioinformatics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A survey of contrastive learning methods in molecular representation Molecular representation is fundamental to the field of cheminformatics, facilitating accurate prediction and exploration of molecular properties. Since the nineteenth century, methods for representing molecules have evolved significantly, with recent advances in deep learning offering state-of-the-art performance across various tasks. Among these, contrastive learning (CL) has emerged as one of the most powerful techniques for training deep learning models. CL aims to optimize the representation of similar molecules by reducing the distance between their vector embeddings, while simultaneously increasing the distance between dissimilar ones. Driven by the growing success of CL in enhancing representation learning, this paper presents the first comprehensive review dedicated to CL methods for molecular representation. We begin by surveying existing literature in the field, providing context for the evolution of molecular representation. Next, we introduce the core principles of the CL framework and examine its application to molecular representation learning tasks. Finally, we highlight the key challenges faced by CL-based approaches and discuss potential future directions for advancing molecular representation with these methods. Representation (politics) ; Context (archaeology) ; Field (mathematics) ; Feature learning ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Hans Richter
Source: Scilight (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

A new phase of sensing phase change materials Infrared cameras inform a convolutional neural network that determines the melt-fraction level of phase change materials. Phase (matter) ; Phase change ; Convolutional neural network ; Artificial neural network ; Temperature measurement Phase-change materials and chalcogenides ; Phase Change Materials Research ; Transition Metal Oxide Nanomaterials
Authors: Neha Rajput | Nidheesh Virakante | Hardik L. Kagdada | Ankit Jain
Source: Computational Materials Science (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Thermal transport properties of diamond and hard carbon allotrope using machine-learning driven atomistic simulations Thermal conductivity ; Molecular dynamics ; Diamond ; Carbon fibers ; Phonon ; Work (physics) ; Amorphous solid ; Amorphous carbon ; Thermal Thermal properties of materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Rajani D | Ravindranath J
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Interpretable Quantum-Enhanced Generative AI Model for Predictive Simulation of Electronic and Thermal Transport in 2D Functional Materials The availability of precise prediction of electronic and thermal transport properties in multidimensional (2D) functional materials is a key to the nextgeneration nanoelectronics, thermoelectric devices, and energy-efficient system. The conventional first-principles and numerical simulation methods though physically accurate, are computationally complex and not very scalable due to the large size of material design spaces that can be investigated. New deep learning and generative AI designs have made predictions more efficient but are limited by data dependency, poor physical interpretability and lack of generalization between material families. Additionally, a majority of current methods fail to utilize new quantum-enhanced learning state-of-the-art that has the potential to learn the electromagnetic scale interactions of 2D materials which are complex. In response to these issues, this paper will present an interpretable quantum-enhanced generative artificial intelligence to predictive simulation of electronic and thermal transport in 2D functional materials. The proposed model combines a quantum-inspired generative architecture with physics-aware feature encoding and explainability mechanisms to guarantee physically meaningful predictions. The framework is assessed on benchmark 2D material datasets that ushers in first-principles simulations, such as electronic band structure and thermal conductivity measurements. The results of the experiment indicate accuracy in prediction of 98.4 percent of electronic transport and a 24 percent smaller error in simulation of thermal transport as opposed to the state-of-the-art deep learning models. The findings affirm the existence of quantum-enhanced generative intelligence that facilitates precise, interpretable, and scalable simulation of transport phenomena in new 2D materials. Interpretability ; Scalability ; Generative grammar ; Benchmark (surveying) ; Generalization ; Deep learning ; Generative model Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Wenlong Xi | Zihan Wu | Patrick H.-L. Sit
Source: Chemical Engineering Journal Advances (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Adsorption energy prediction and mechanistic analysis of metal atoms on MXene surfaces based on DFT and machine learning • DFT and ML explore Li, Na, K, Ca, and Mg adsorption on MXenes. • XGBoost outperforms other ML models in predicting adsorption energy. • SHAP reveals first layer atom properties as key factors to affect adsorption. • Adsorption energetics correlates with the d-band theory. MXenes show promising application prospects in metal-ion battery electrodes due to their advantages like adjustable surface chemical properties and open layered structure. However, the conventional density functional theory (DFT) method faces challenges of low efficiency and high time consumption when used for systematic exploration of the adsorption performance a large variety of MXenes. This study proposes an efficient prediction framework for adsorption energy based on machine learning. By integrating DFT data, a cross-element data set containing 90 types of MXene substrates and 5 types of metal atoms is constructed. We propose a new set of features to divide the MXene surface atoms into three layers according to their distance from metal atoms and extract the key features, such as electron affinity, the number of atoms closest to the metal atom, and atomic radii in each layer. Using eXtreme Gradient Boosting (XGB), Random Forest Regression (RFR), Neural Network (NN) and Support Vector Regression (SVR) models for comparative analysis, it is found that XGB shows the best performance on the test set (MAE = 0.21 eV, R² = 0.93). The SHAP interpretability analysis shows that the electron affinity of the first layer atoms and of the adsorbed metal atoms are the core factors affecting the adsorption energy. Finally, we select the MXenes with high adsorption energies, namely Sc 2 NO 2 and Y 2 NO 2 with high d band center, as prototypical examples for further analysis. Their d-band centers (2.0325 eV and -1.8770 eV) lie closer to the Fermi level, leading to higher adsorption energies. MXenes ; Adsorption ; Interpretability ; Density functional theory ; Atom (system on chip) ; Support vector machine ; Artificial neural network ; Test set ; Metal MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Advancements in Battery Materials
Authors: Zuzanna Monika Malinowska-Trzmielak | Nicole Grobert | Mark Wilson
Source: Modelling and Simulation in Materials Science and Engineering (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Transferability of carbon potentials for novel carbon polymorphs Abstract Choosing a suitable potential model to study dynamic processes in novel structures is an ambitious task often relying on chemical intuition. This paper addresses this challenge through a case study of diaphites, diamond-graphite nanocomposites, that are the only naturally occurring crystalline form of carbon featuring both sp 3 and sp 2 hybridized atoms. Since their synthesis is expensive and difficult to control, molecular dynamics (MD) simulations of their formation would be highly valuable. However, none of the available carbon potentials explicitly includes diaphites in their parameterization. Here, we benchmark several well-established carbon potentials (Tersoff 1989, Tersoff 1994, REBO-II, LCBOP-I, AIREBO, AIREBO-M, GAP-20, ACE) against ab initio MD (AIMD) at the PBE+D2 level of theory. Comparison of structural labeling disqualified Tersoff 1989, Tersoff 1994, REBO-II, AIREBO, and AIREBO-M. To enable long-timescale simulations on systems of a few thousand atoms, an machine-learning (ML)-AIMD model was developed using AIMD acceleration with an on-the-fly Gaussian approximation potential (GAP). ML-AIMD accurately reproduced AIMD results and was therefore used as a benchmark. Extended testing revealed that ACE is the most transferable and computationally efficient potential for MD simulations of diaphites, reproducing the sp 2 fraction across all temperatures at a cost at least four times lower than GAP-20. LCBOP-I performed comparably below 2000 K and remains preferable when computational resources are limited. The presented benchmarking framework efficiently identifies the most suitable potentials and provides a general strategy for selecting MD models for novel materials. Transferability ; Molecular dynamics ; Carbon fibers ; Benchmark (surveying) ; Benchmarking ; Gaussian Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: J Benji Rowlands | Richard J. Lewis | Peter W. A. Howe | Jonathan M. Goodman
Source: ChemRxiv (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Evaluating 2D and 3D Graph Encoders for Infrared-Based Organic Structure Verification Infrared (IR) spectrum prediction is typically judged by global spectral error, but the practical task in analytical chemistry is structure verification: given a spectrum, can a model rank the correct structure highly among many candidates? We recast IR modelling around the structure verification problem, and extend the Phase-Aware Spectrum Generative Network (PASGeN) to test how 2D and 3D graph encoders perform when retrieval accuracy, instead of spectral similarity, is the metric of interest. Using 13568 experimental IR spectra in various phases, we train models with a 2D message-passing neural network (MPNN) and a 3D directional encoder (DimeNet), and fine-tune the 3D model on inexpensive force-field geometries to reduce inference cost. Across large-scale retrieval tests, the 3D encoder significantly outperforms the 2D baseline (74.4% vs 63.9% Top-1 accuracy). Both learned models exceed the verification performance of quantum chemical DFT and GFN2-xTB baselines by a wide margin. Descriptor analysis suggests that ring-rich, geometrically complex molecules benefit most from the inclusion of 3D information. By reframing IR prediction around the structure verification task and demonstrating that lightweight 3D geometries provide a measurable advantage, this work establishes a practical and scalable route to IR-based structure confirmation that surpasses physics-based baselines at a fraction of the computational cost. Encoder ; Inference ; Graph ; Scalability ; Task (project management) ; Artificial neural network ; Metric (unit) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods