jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ravi Prakash Dwivedi | Richa Dubey | D. P. Mahapatra | Saurav Gupta
Source: International Journal of Energy Research (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ensemble Approach Assisted Specific Capacitance Prediction for Heteroatom‐Doped High‐Performance Supercapacitors Specific capacitance plays a critical role when assessing the performance of a supercapacitor. Hence, its prediction is crucial for evaluating the electrochemical performance of electric double‐layer capacitors (EDLCs). Machine learning (ML) offers the prospect of predicting capacitance with nominal investment in the synthesis and testing of electrode materials. Herein, six ML models: random forest (RF), artificial neural network (ANN), random tree (RT), random committee (RC), random subspace (RS), and support vector machine (SVM) regressor are used to analyze the effect of four hetero atom doping (nitrogen, boron, sulfur, and phosphorous) on the electrochemical performance of EDLCs. Amongst all, RF, ANN, and RS showed the highest correlation values of 0.9996, 0.9993 and 0.9867, respectively, and the lowest root mean square values of 0.93, 1.19, and 2.31, respectively, through selection of 12 key input descriptors on the basis of physical, structural, test, operational, and doping parameters. Furthermore, attribute prioritization was introduced to identify and rank important features within the dataset. It highlights that specific surface area, total pore volume, and nitrogen are the most significant descriptors among 12 selected input features. With fewer iterations, the developed models’ estimation accuracy surpassed other state‐of‐art models in literature. In perspective, this study considers an extensive dataset extracted from more than 250 research articles on heteroatom‐doped carbon electrodes. It also provides insights into the significance of ML modeling on the electrochemical technology. Supercapacitor ; Heteroatom ; Capacitance ; Doping ; Materials science ; Nanotechnology ; Optoelectronics ; Chemistry ; Electrode ; Physical chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Hamid Reza Sezavar | Saeed Hasanzadeh
Source: Electric Power Systems Research (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Integrating Weibull analysis and KAN-ODEs for enhanced flashover prediction in contaminated composite insulators Weibull distribution ; Ode ; Composite material ; Composite number ; Arc flash ; Materials science ; Contamination ; Mathematics ; Statistics ; Applied mathematics ; Insulator (electricity) ; Biology High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Power Transformer Diagnostics and Insulation
Authors: Mohd Zahid Ansari | Sajid Ali Ansari | Nazish Parveen | Ghayah M. Alsulaim | Ahmad Umar | Nagih M. Shaalan | Soo‐Hyun Kim
Source: Progress in Materials Science (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Exploring graphdiyne, MXene, borophene, and phosphorene as advanced 2D materials for next-generation metallic ion batteries Phosphorene ; MXenes ; Anode ; Battery (electricity) ; Energy storage ; Ion ; Surface engineering ; Heteroatom MXene and MAX Phase Materials ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Degang Jiang | Jizhen Zhang | Hongwei Zhou
Source: Chemical Engineering Journal (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

MXene-based solid-state supercapacitors: Advances, challenges, and opportunities MXenes ; Supercapacitor ; Energy storage ; Context (archaeology) ; Scalability ; Compatibility (geochemistry) MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Shuai Li | Sree Sourav Das | Haobo Wang | Kacper Pryga | Sujit Bati | Bartłomiej Wiendlocha | Junichiro Shiomi | Jerrold A. Floro | Prasanna V. Balachandran | Mona Zebarjadi
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High thermoelectric power factor in Ni–Fe alloy for active cooling applications Metallic thermoelectric materials are promising candidates for active cooling applications, where high thermal conductivity and a high thermoelectric power factor are essential to maximize effective thermal conductivity. While metals inherently possess high thermal and electrical conductivities, they typically exhibit low Seebeck coefficients. In this work, we create a database of the Seebeck coefficient of binary metallic alloys and apply machine learning techniques to identify alloys with large Seebeck coefficients. Specifically, we identify Ni-Fe as a promising candidate for active cooling around room temperature. We then fabricate Ni-Fe ingots and demonstrate thermoelectric power factor values as high as 120 μW cm<sup>-1</sup> K<sup>-2</sup> at 200 K for these stable alloys, which are composed of cost-effective and abundant elements. Furthermore, we demonstrate that the effective thermal conductivity of these alloys, under small temperature differences, can exceed that of pure copper within the 250-400 K temperature range. Seebeck coefficient ; Alloy ; Materials science ; Thermoelectric effect ; Thermoelectric materials ; Power factor ; Thermoelectric cooling ; Metal ; Thermoelectric generator ; Metallurgy ; Power (physics) ; Optoelectronics ; Condensed matter physics ; Composite material ; Thermodynamics ; Thermal conductivity ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics
Authors: Yong Gyun Shin | Belal Almomani | Yoon‐Suk Chang
Source: Nuclear Engineering and Design (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Machine learning-based mechanical properties estimation and structural integrity assessment of SNF cladding under hypothetical drop accident conditions Cladding (metalworking) ; Structural integrity ; Forensic engineering ; Drop (telecommunication) ; Nuclear engineering ; Engineering ; Materials science ; Mechanical engineering ; Structural engineering ; Reliability engineering ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Additive Manufacturing Materials and Processes
Authors: Dmytro Antypov | C. Matilda Collins | Matthew S. Dyer | John B. Claridge | Matthew J. Rosseinsky
Source: Journal of Applied Crystallography (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Classification and statistical analysis of structural disorder in crystalline materials Approximately 50% of entries in the Inorganic Crystal Structure Database (ICSD; https://www.fiz-karlsruhe.de/en) exhibit some form of structural disorder. This work aims to provide a thorough analysis of structurally disordered materials within the ICSD, using data extracted from crystallographic information files. To achieve this, we derive a classification of structurally disordered crystalline materials described by their spatially averaged structures and introduce a range of quantitative measures of structural disorder. The overarching aim of this classification and analysis is to facilitate high-throughput and machine learning studies of disordered materials. To demonstrate the application of our approach, we perform statistical analysis of the disordered compounds reported in the ICSD to identify general trends in the distribution of disorder across different chemical elements, structures and classes of materials. Statistical analysis ; Range (aeronautics) ; Computer science ; Crystal structure ; Materials science ; Statistical physics ; Crystallography ; Physics ; Chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Radioactive element chemistry and processing
Authors: Shakti P. Padhy | Karl Peter Davidson | Li Ping Tan | Vijaykumar B. Varma | Vinay Sharma | Xiao Tan | Yuefan Wei | Xuesong Xu | Kedar Hippalgaonkar | Mark Hyunpong Jhon | R.V. Ramanujan
Source: Journal of Alloys and Compounds (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

Integrated design framework for titanium aluminides through interpretable machine learning Titanium alloy ; Alloy ; Aerospace ; Ultimate tensile strength ; Titanium ; Titanium aluminide ; Structural material Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties
Authors: Koji Kimoto | Ovidiu Cretu | Koji Harano | Fumihiko Uesugi | Jun Kikkawa | Kohei Aso | Yoshifumi Oshima | Takashi Matsumoto | Yoshiki Sakuma
Source: Small Methods (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Unveiling Twist Domains in Monolayer MoS <sub>2</sub> through 4D‐STEM and Unsupervised Machine Learning Dichalcogenides, such as molybdenum disulfide (MoS<sub>2</sub>), are being studied extensively due to their 2D feature and various material properties. Although crystal structures are critical for applications, conventional atomic structure analyses have a limited field of view. In this study, the crystal domains of monolayer MoS<sub>2</sub> synthesized by metal-organic chemical vapor deposition (MOCVD) are analyzed using 4D scanning transmission electron microscopy (STEM) and unsupervised machine learning. Twist domains (±11°) are identified through the nonnegative matrix factorization (NMF) and hierarchical clustering of numerous (>22k) diffraction patterns from a wide field of view. Preprocessing for detecting noncentrosymmetry effectively visualizes the polarities of distinct MoS<sub>2</sub> domains by highlighting the violation of Friedel's law in diffraction physics. Analyses reveal that the specimen deposited on Al<sub>2</sub>O<sub>3</sub> (0001) at 850 °C consists of domains measuring ≈100 nm in size and featuring many mirror-twin boundaries. The findings provide valuable insights into optimizing the MOCVD process and elucidating crystal growth mechanisms. Molybdenum disulfide ; Non-negative matrix factorization ; Chemical vapor deposition ; Materials science ; Monolayer ; Metalorganic vapour phase epitaxy ; Crystal (programming language) ; Electron diffraction ; Diffraction ; Crystallography ; Chemical physics ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Optics ; Matrix decomposition ; Layer (electronics) 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Andrey A. Kistanov | Igor V. Kosarev | S. A. Shcherbinin | Alexander V. Shapeev | Elena A. Korznikova | Sergey V. Dmitriev
Source: Materials Today Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unified approach to generating a training set for machine learning interatomic potentials: The case of BCC tungsten Materials science ; Tungsten ; Interatomic potential ; Set (abstract data type) ; Training set ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Metallurgy ; Computational chemistry ; Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Akihide Hayashi | So Takamoto | Ju Li | Yuta Tsuboi | Daisuke Okanohara
Source: Journal of Chemical Theory and Computation (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Generative Model for Constructing Reaction Path from Initial to Final States Mapping the chemical reaction pathways and their corresponding activation barriers is a significant challenge in molecular simulation. Given the inherent complexities of 3D atomic geometries, even generating an initial guess of these paths can be difficult for humans. This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways based on the initial state and learning from a database of low-energy transition paths. The proposed method is initiated by inputting the coordinates of the initial state, followed by progressive alterations to its structure. This iterative process culminates in the generation of the guess reaction path and the coordinates of the final state. The method does not require one-the-fly computation of the actual potential energy surface and is therefore fast-acting. The application of this geometry-based method extends to complex reaction pathways illustrated by organic reactions. Training was executed on the Transition1x data set of organic reaction pathways. The results revealed the generation of reactions that bore substantial similarities with the test set of chemical reaction paths. The method's flexibility allows for reactions to be generated either to conform to predetermined conditions or in a randomized manner. Computer science ; Path (computing) ; Flexibility (engineering) ; Set (abstract data type) ; Potential energy surface ; Process (computing) ; State (computer science) ; Artificial neural network ; Computation ; Energy profile ; Algorithm ; Artificial intelligence ; Energy (signal processing) ; Biological system ; Chemistry ; Mathematics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Hang Sun | Wengang Wu | Zheng‐Da Hu | Wenjia Yu | Jingjing Wu | Jicheng Wang | Yuting Yang | Mengze Li | Sergei Khakhomov
Source: Laser & Photonics Review (article, 2025-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐Capacity Corner State Encoding and Dual‐Plane Switching Encryption Based on Topological Photonic Crystals Abstract This study introduces a tunable high‐capacity corner state encoding photonic crystal structure designed for Beyond‐5G (B5G) communication. By arranging photonic crystals with distinct topological properties into arrays, localized confinement of light at specific frequencies at the corners of the structure is achieved. The hybrid deep learning model integrating convolutional neural networks and long short‐term memory networks are employed to precisely predict photonic crystal array parameters and corner state encoding performance within the B5G band. A nested single‐frame photonic crystal array is constructed and manipulate the relative phases of four excitation sources to selectively strengthen or weaken localized states at each corner. Simulation and experiment results validate the effectiveness of the proposed high‐capacity corner state encoding method. Furthermore, a dual‐plane corner state encryption system significantly enhancing the efficiency is developed, security and practical applicability of B5G communication. Encoding (memory) ; Topology (electrical circuits) ; Encryption ; Dual (grammatical number) ; Photonics ; Photonic crystal ; State (computer science) ; Materials science ; Optoelectronics ; Computer science ; Mathematics ; Computer network ; Algorithm ; Combinatorics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Florian Lindner | Nina Strasser | Martin Schultze | Sandro Wieser | Christian Slugovc | Kareem Elsayad | Kristie J. Koski | Egbert Zojer | Caterina Czibula
Source: The Journal of Physical Chemistry Letters (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Combining Brillouin Light Scattering Spectroscopy and Machine-Learned Interatomic Potentials to Probe Mechanical Properties of Metal-Organic Frameworks The mechanical properties of metal-organic frameworks (MOFs) are of high fundamental and practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has rarely been used for highly complex materials like MOFs. In the present contribution, we apply this technique to study a newly synthesized MOF-type material, referred to as GUT2. The experiments are combined with state-of-the-art simulations of elastic properties and phonon bands, which are based on machine-learning force fields and dispersion-corrected density functional theory. This provides a comprehensive understanding of the experimental signals, which can be correlated to the longitudinal and transverse sound velocities of the material. Notably, the combination of the insights from simulations and experiments allows the determination of approximate values for the components of the elastic tensor of the studied material even when dealing with comparably small single crystals, which limit the range of accessible experimental data. Brillouin zone ; Brillouin scattering ; Spectroscopy ; Materials science ; Brillouin Spectroscopy ; Light scattering ; Metal-organic framework ; Scattering ; Chemical physics ; Nanotechnology ; Molecular physics ; Optics ; Chemistry ; Physical chemistry ; Physics ; Laser Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Huiyu Liu | Muyao Yu | Zikai Xie | Xiangyu Zhang | Y. Sun | Y. Ling | Zezhao Xu | Zidi Wang | Na Yu | Yue‐Biao Zhang | Ping Huai | Qisheng Wang | Xuming He | Linjiang Chen | Weihong Zhu | Shan Jiang
Source: Journal of the American Chemical Society (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening Polar organic cocrystals hold significant promise for various advanced technological applications. However, their relatively low occurrence emphasizes the difficulties in achieving the desired polar packing arrangements, making their discovery complex and challenging. Here, we introduce a data-driven method that combines machine learning (ML) with high-throughput (HT) automation to speed up the discovery of polar organic cocrystals. Using ML techniques, we identified key factors that influence polar cocrystal formation, allowing for targeted selection of molecular candidates. We examined 13 cocrystal combinations with chloranilic acid (CA), screening 20 solvent systems for each, which enabled a highly efficient search across a broad chemical space. HT automation further enhanced the synthesis and characterization by enabling rapid screening and precise structural validation, while thoroughly exploring the chemical landscape. Experimental results confirmed 13 pairs of CA cocrystals, with 6 crystallizing in polar space groups, resulting in a polar discovery rate of 46%-nearly three times higher than the average in the Cambridge Structural Database (CSD) (∼13.2%). This integrated approach offers a new strategy in polar organic cocrystal research. The findings demonstrate the potential of this method to advance functional molecular materials and pave the way for next-generation applications using polar organic cocrystals. Cocrystal ; Polar ; Automation ; Drug discovery ; Chemical space ; Organic molecules ; Key (lock) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Jun‐Shan Si | Nan Wu | M. Wen | Dongping Chen | Yonglyu He | Jianwei Zhang | Ke Duan
Source: Chinese Journal of Polymer Science (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Reactive Machine Learning Force Field for Crosslinked Epoxy Epoxy ; Field (mathematics) ; Materials science ; Composite material ; Force field (fiction) ; Computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Semiconductor materials and devices
Authors: Ryuhei Sato | Lewis J. Conway | Di Zhang | Chris J. Pickard | Kazuto Akagi | Kartik Sau | Hao Li | Shin‐ichi Orimo
Source: Proceedings of the National Academy of Sciences (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Surface melting–driven hydrogen absorption for high-pressure polyhydride synthesis The synthesis of new polyhydrides with high superconducting <i>T</i><sub>c</sub> is challenging owing to the high pressures and temperatures required. In this study, we used machine-learning potential molecular dynamics simulations to investigate the initial stage of polyhydride formation in calcium hydrides. Upon contact with high-pressure H<sub>2</sub>, the surface of CaH<sub>2</sub> melts, leading to CaH<sub>4</sub> formation. This surface melting proceeds via CaH<sub>4</sub> liquid phase as an intermediate state. High pressure reduces not only the hydrogenation (CaH<sub>2</sub>(s) + H<sub>2</sub>(l) ↔ CaH<sub>4</sub>(s)) enthalpy but also the enthalpy for liquid polyhydride formation (CaH<sub>2</sub>(s) + H<sub>2</sub>(l) ↔ CaH<sub>4</sub>(l)). Consequently, this surface melting process becomes more favorable than the fusion of the polyhydride bulk. Thus, high pressure not only shifts the equilibrium toward the polyhydride product but also lowers the activation energy, thereby promoting the hydrogenation reaction. From these thermodynamic insights, we propose structure-search criteria for polyhydride synthesis that are both computationally effective and experimentally relevant. These criteria are based on bulk properties, such as polyhydride (product) melting temperature and pressure-dependent hydrogenation enthalpy, readily determined through supplementary calculations during structure prediction workflows. Enthalpy ; High pressure ; Thermodynamics ; Melting point ; Fusion ; Materials science ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Thomas Gasevic | Marcel Müller | Jonathan Schöps | Stephanie Lanius | Jan Hermann | Stefan Grimme | Andreas Hansen
Source: Journal of Chemical Information and Modeling (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Chemical Space Exploration with Artificial “Mindless” Molecules We introduce MindlessGen, a Python-based generator for creating chemically diverse, "mindless" molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the <i>MB2061</i> benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for H<sub>2</sub>-promoted decomposition reactions. This set provides a challenging benchmark for testing, validating, and training density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond conventional chemical space. For DFAs, we initially hypothesized that highly parametrized functionals might perform poorly on this set. However, no consistent relationship between the fitting strategy and accuracy was observed. A clear Jacob's ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol<sup>-1</sup> and r<sup>2</sup>SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol<sup>-1</sup>). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine-learning interatomic potentials. Python (programming language) ; Chemical space ; Benchmark (surveying) ; Computer science ; Set (abstract data type) ; Molecule ; Generator (circuit theory) ; Space (punctuation) ; Test set ; Statistical physics ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Chemistry ; Thermodynamics ; Quantum mechanics ; Power (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions