jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kais Iben Nassar | Tiago L. P. Galvão | José D. Gouveia | José R. B. Gomes
Source: Advanced Intelligent Discovery (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes Machine learning (ML) has been employed to predict activation energies for key reactions in the water‐gas shift process on pure MXene surfaces, namely, the dissociations of water (H 2 O) and hydroxyl (OH), and the formation reactions of hydrogen (H 2 ) and carbon dioxide (CO 2 ). The dataset includes 92 MXenes, including single‐transition‐metal (M 2 N or M 2 C) and dual‐transition‐metal (M’ 2 M’’C 2 ) compositions, with energies derived from density functional theory calculations from this work or the literature. Several ML models are evaluated, including Random Forest Regressor (RFR), Gradient Boosting Regressor, Artificial Neural Networks, Support Vector Machines, Decision Tree Regressor, and K‐Nearest Neighbors Regressor, identifying RFR as the most accurate. Correlation plots comparing actual versus predicted activation energies further validate the robustness and accuracy of our models. Feature importance analysis reveals that two key factors, the reaction energy and the logarithmic partition coefficient (LogP) of the reactant, are crucial in predicting activation energies. While the former feature aligns well with the Bronsted‐Evans‐Polanyi principle relating to the kinetics and thermodynamics of a given reaction, the latter encompasses different physicochemical properties in only one parameter: polarity, hydrogen bonding, molecular size, molecular shape, and charge. This study underscores the power of ML in catalysis, offering a predictive framework to guide catalyst design. MXenes ; Chemistry ; Artificial neural network ; Brønsted–Lowry acid–base theory ; Water-gas shift reaction ; Thermodynamics ; Density functional theory ; Artificial intelligence ; Catalysis ; Machine learning ; Computational chemistry ; Computer science ; Physics ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yukihiro Okuno
Source: The Journal of Physical Chemistry B (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Structures and Ion Transport Properties of Hydrate-Melt Electrolytes: A Machine-Learning Potential Molecular Dynamics Study High-concentration aqueous electrolytes (hydrate-melts) have attracted significant attention for lithium-ion batteries due to their nonflammability and low toxicity. In these electrolytes, the static and dynamic structures of the solvent play a crucial role in determining various properties, such as the ionic conductivity, of the system. To clarify the solvent structure and ion diffusion mechanism, we conducted molecular dynamics simulations using a machine learning potential for Li and Na hydrate-melts. By analyzing the dynamical interaction between ions and their coordinating molecules, we found the ligand exchange of H<sub>2</sub>O molecules coordinated to cations occurs frequently. As a result, it is considered that the kinetic energy of H<sub>2</sub>O is transferred to cations and drives the diffusion of cations in the hydrate-melts. This ion transport mechanism is different from the conventionally understood vehicle-type or hopping-type ion transport mechanism. The comparison of Na hydrate-melts and Li hydrate-melts shows the higher diffusion of Na relative to Li. It was suggested that there exists an optimal value for the strength of interaction between cations and H<sub>2</sub>O molecules, which influences ion diffusion, and that the interaction for Na is close to this optimal value compared to that of the Li. Molecular dynamics ; Electrolyte ; Ion transporter ; Hydrate ; Ion ; Materials science ; Chemical engineering ; Chemical physics ; Nanotechnology ; Chemistry ; Engineering ; Computational chemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced NMR Techniques and Applications
Authors: Clint John Cortes Otic | Ikuya Kinefuchi
Source: Journal of Power Sources (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Machine learning-driven 3D reconstruction of PEMFC catalyst layers from FIB-SEM imaging The microstructure of the polymer electrolyte membrane fuel cell (PEMFC) cathode catalyst layer (CCL) critically influences key transport phenomena, directly impacting fuel cell performance. Accurate three-dimensional (3D) reconstruction of the CCL is essential for understanding structure-property relationships and optimizing material design. This study presents a machine learning-driven framework for 3D reconstruction of PEMFC catalyst layers from focused ion beam-scanning electron microscopy (FIB-SEM) images, addressing common challenges such as slice-wise segmentation artifacts and imaging resolution trade-offs. The framework integrates a two-stage segmentation process—beginning with a 2D DeepLabv3+-based network (CatalVis2D) followed by a 3D U-Net-based model (CatalVis3D)—to enhance segmentation accuracy and spatial continuity. To further refine structural fidelity, a regression network (CatalReVis3D) is developed for super-resolution reconstruction, enabling high-resolution 3D structures to be inferred from low-resolution inputs. The approach is validated through segmentation metrics and gas transport simulations. The trained networks also exhibit strong generalization capability across unseen datasets and a different catalyst layer sample composition. This machine learning-driven framework offers a scalable and accurate tool for PEMFC microstructural analysis, contributing to data-driven catalyst layer optimization and advancing electrochemical material research. • Machine-learning for accurate 3D reconstruction of catalyst layer from FIB-SEM images. • Predicting missing catalyst layer structure when using longer FIB-SEM pitch. • Publicly available dataset for catalyst layer segmentation and generation. Proton exchange membrane fuel cell ; Catalysis ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Engineering ; Fuel cells ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Like Zhang | Huangyou Zhang | Bingyang Ji | Leqing Liu | Xianlan Liu | Ding Chen
Source: Materials (review, 2025-04-13)
Field: Materials Science — Materials Chemistry

Application of Machine Learning in Amorphous Alloys In the past few decades, traditional methods for developing amorphous alloys, such as empirical trial-and-error approaches and density functional theory (DFT)-based calculations, have enabled researchers to explore numerous amorphous alloy systems and investigate their properties. However, these methods are increasingly unable to meet the demands of modern research due to their long development cycles and low efficiency. In contrast, machine learning (ML) has gained widespread adoption in the design, analysis, and property prediction of amorphous alloys due to its advantages of low experimental cost, powerful performance, and short development cycles. This review focuses on four key applications of ML in amorphous alloys: (1) prediction of amorphous alloy phases, (2) prediction of amorphous composite phases, (3) prediction of glass-forming ability (GFA), and (4) prediction of material properties. Finally, we outline future directions for ML in materials science, including the development of more sophisticated models, integration with high-throughput experimentation, and the creation of standardized data-sharing platforms. These insights provide potential research directions and frameworks for subsequent studies in this field. Amorphous solid ; Amorphous metal ; Materials science ; Computer science ; Field (mathematics) ; Alloy ; Nanotechnology ; Machine learning ; Artificial intelligence ; Metallurgy ; Mathematics ; Chemistry Phase-change materials and chalcogenides ; Metallic Glasses and Amorphous Alloys ; Machine Learning in Materials Science
Authors: Yu Yuan | Baoli Wu | Rui Wei | Nanqi Ren | Shijie You
Source: Nature Communications (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning Reactive transport in porous media is the key to heterogeneous catalysis, which is the central process in both natural and engineered systems. Elucidating nexus between porous architecture and reactive transport is of importance, but remains a challenge. Conventional text-based approach relies on quantitative structural features (QSFs; porosity, tortuosity, and connectivity), which fails to identify key reaction regions and predict local reaction rate for anisotropic architecture due to isotropic assumption. To address these issues, this study reports a data-driven deep learning computer vision (DLCV) method for visualizing nexus between porous architecture and reactive transport in heterogeneous catalysis. Here, we show that the 3D local reaction rate can be inferred from 2D lateral images of anisotropic porous catalysts using Conditional Generative Adversarial Network and feature representation transfer learning (cGAN-FRT). Efficiency and generalizability are validated by rapid and accurate prediction of reaction rate for heterogeneous electrocatalysis. Based on feature importance generated by cGAN-FRT, pore throat, curved flow channel, and their combined structures are identified to be the dominant factors that affect nonlinear variation of porous reactive transport, which can be interpreted by physical field synergy. This study realizes visualizing nexus between anisotropic porous architecture and local reactive transport powered by artificial intelligence. Tortuosity ; Nexus (standard) ; Porous medium ; Nanoporous ; Computer science ; Porosity ; Deep learning ; Materials science ; Transfer of learning ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Qingchuan Yang | Wenjun Zhang | Lian-Fu Wei
Source: Quantum Information Processing (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

A quantum residual attention neural network for high-precision material property prediction Residual ; Quantum computer ; Property (philosophy) ; Artificial neural network ; Computer science ; Quantum ; Artificial intelligence ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Neural Networks and Applications
Authors: Shin Kiyohara | Yu Kumagai
Source: The Journal of Physical Chemistry Letters (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs Machine learning (ML) approaches have become ubiquitous in the search for new materials in recent years. Bayesian optimization (BO) based on Gaussian processes (GPs) has become a widely recognized approach in material exploration. However, feature engineering has critical impacts on the efficiency of GP-based BO, because GPs cannot automatically generate descriptors. To address this limitation, this study applies deep kernel learning (DKL), which combines a neural network with a GP, to BO. The efficiency of the DKL model was comparable to or significantly better than that of a standard GP in a data set of 922 oxide data sets, covering band gaps, ionic dielectric constants, and effective masses of electrons, as well as in experimental data sets, the band gaps of 610 hybrid organic-inorganic perovskite alloys. When searching for the alloy with the highest Curie temperature among 4560 alloys, the standard GP outperformed the DKL model because a strongly correlated descriptor of the Curie temperature could be directly utilized. Additionally, DKL supports transfer learning, which further enhances its efficiency. Thus, we believe that BO based on DKL paves the way for exploring diverse material spaces more effectively than GPs. Bayesian optimization ; Gaussian process ; Gaussian ; Artificial intelligence ; Bayesian probability ; Computer science ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Manufacturing Process and Optimization
Authors: Yongchun Dang | Zechen Li | Yongchao Yu | Xingzhi Bai | Lijun Wang | Xuelei Wang | Peng Liu | Chen Sun | Xilian Zhou | Zhenpo Wang | Yongjie Zhao | Xiangming He | Lei Li
Source: Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Selecting High-Energy Phosphate Cathode Materials The limited energy density inherent in cathode materials remains a marked barrier to the widespread adoption of sodium-ion batteries. Despite considerable research efforts, the precise influence of atomic and crystalline configurations on energy density is not yet fully understood, creating a knowledge gap that hinders the rational design of advanced cathode materials. In this study, we propose a machine learning approach to systematically identify promising cathode materials with high energy densities. Our model highlights the critical roles of entropy and equivalent electronegativity, among other properties such as molecular mass, electron affinity, and average ionic radius. Based on these insights, we successfully synthesized Na<sub>3</sub>Mn<sub>0.5</sub>V<sub>0.5</sub>Ti<sub>0.5</sub>Zr<sub>0.5</sub>(PO<sub>4</sub>)<sub>3</sub> (NMVTZP) electrodes via a sol-gel method. The resulting electrodes exhibit an impressive reversible specific capacity of 148.27 mAh g<sup>-1</sup> at a 0.1-C rate, outperforming several previously reported cathode materials. Additionally, the NMVTZP electrodes demonstrate an average operating voltage of 3.14 V, an energy density of 465 Wh kg<sup>-1</sup>, and exceptional rate performance, retaining 90.20 mAh g<sup>-1</sup> at a 5-C rate. We anticipate that our machine learning approach will accelerate the development of high-performance materials and greatly contribute to the advancement of sodium-ion battery technology. Cathode ; Phosphate ; Materials science ; Engineering physics ; Computer science ; Environmental science ; Chemistry ; Engineering ; Electrical engineering ; Biochemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Nicolai Ree | Jan M. Wollschläger | Andreas H. Göller | Jan H. Jensen
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Atom-based machine learning for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and chemical stability Nucleophilicity and electrophilicity are important properties for evaluating the reactivity and selectivity of chemical reactions. It allows the ranking of nucleophiles and electrophiles on reactivity scales, enabling a better understanding and prediction of reaction outcomes. Building upon our recent work (N. Ree, A. H. Göller and J. H. Jensen, Automated quantum chemistry for estimating nucleophilicity and electrophilicity with applications to retrosynthesis and covalent inhibitors, <i>Digit. Discov.</i>, 2024, <b>3</b>, 347-354), we introduce an atom-based machine learning (ML) approach for predicting methyl cation affinities (MCAs) and methyl anion affinities (MAAs) to estimate nucleophilicity and electrophilicity, respectively. The ML models are trained and validated on QM-derived data from around 50 000 neutral drug-like molecules, achieving Pearson correlation coefficients of 0.97 for MCA and 0.95 for MAA on the held-out test sets. In addition, we demonstrate the ML approach on two different applications: first, as a general tool for filtering retrosynthetic routes based on chemical selectivity predictions, and second, as a tool for assessing the chemical stability of esters and carbamates towards hydrolysis reactions. The code is freely available on GitHub under the MIT open source license and as a web application at https://www.esnuel.org. Nucleophile ; Electrophile ; Chemistry ; Reactivity (psychology) ; Affinities ; Retrosynthetic analysis ; Computational chemistry ; Atom (system on chip) ; Machine learning ; Combinatorial chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Asymmetric Hydrogenation and Catalysis
Authors: D. Chen | Guo Wei | Guoqing Wu | Qi Chen | Guangxin Chen | Y. H. Zheng | Fangbiao Wang
Source: Computational Condensed Matter (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Material property prediction of perovskite oxides based on machine learning Perovskite (structure) ; Property (philosophy) ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Crystallography Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Machine Learning and ELM
Authors: Yue Tian | Junghwan Kim | Yi Liu
Source: Energy (article, 2025-05-21)
Field: Chemistry — Analytical Chemistry

Interpretable machine learning model for bio-oil property prediction from hydrothermal liquefaction of biomass via graph neural networks-based molecular structure Artificial neural network ; Liquefaction ; Hydrothermal circulation ; Biomass (ecology) ; Property (philosophy) ; Graph ; Hydrothermal liquefaction ; Artificial intelligence ; Petroleum engineering ; Computer science ; Biochemical engineering ; Environmental science ; Machine learning ; Engineering ; Geology ; Chemical engineering ; Waste management ; Theoretical computer science ; Biofuel ; Geotechnical engineering Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes ; Hydrocarbon exploration and reservoir analysis
Authors: Hongfei Li | Yazhou Chen | Linsen Zhou | Zun Xie | Wei Cao | Zhaoming Qu
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach Polymer/graphene (Py/GN) composites under the influence of external electric fields often exhibit unique nonlinear conducting behaviors. However, the underlying mechanism of this field effect at the molecular level is still obscure until now. Herein, the evolution of electrical properties of Py/GN composites induced by electric fields has been explored by combining high-throughput first-principles calculations with machine learning models. The results show that the polymer valence band maximum (PVBM) and polymer conduction band minimum (PCBM) of Py/GN composites under different electric fields can be accurately predicted by the XGBoost regression algorithm. The band arrangement of polymers in Py/GN composites can be easily altered with applied electric fields, where charges accumulate around the graphene layer and depleted around the polymer layer. Moreover, the electrons at the pyrrole/GN interface may overcome the Schottky barrier height, leading to a transition from a Schottky contact to an ohmic contact under a critical field (<i>E</i> <sub>b</sub>), which can be effectively predicted with an <i>R</i> <sup>2</sup> value of 0.854. Then, two types of novel Py/GN composites with lower or higher <i>E</i> <sub>b</sub> values were screened by reverse engineering the ML model, offering valuable guidance for the application of Py/GN composites in different electric field conditions. This work can provide new insights into the nonlinear electrical response of Py/GN composites under electric fields, which has significant implications for practical applications. Graphene ; Materials science ; Composite material ; Nonlinear system ; Electrical conductor ; Polymer ; Thermal conduction ; Conductive polymer ; Nanotechnology ; Physics ; Quantum mechanics Conducting polymers and applications ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Mohanad A. Deif | Hani Attar | Mohammad Aljaidi | Ayoub Alsarhan | Dimah Al-Fraihat | Ahmed Solyman
Source: Intelligent Systems with Applications (article, 2025-06-21)
Field: Materials Science — Materials Chemistry

Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization • Implants developed with biodegradable materials offer advantages such as eliminating the need for secondary surgery, improving mechanical and biological properties, and enhancing biocompatibility. • The study introduces a machine learning approach combining Bayesian optimization (BO) and eXtreme Gradient Boosting (XGBoost) algorithms for designing a biodegradable Zinc (Zn) alloy and predicting the percentage of elements in the alloy for bone implants. • A dataset of 1182 Zn alloy samples from Google Scholar and the mat web database was utilized for the study. • The proposed method demonstrated exceptional predictive capacity, achieving maximum R2 values of 0.85, 0.87, and 0.81 for forecasting mechanical parameters including Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS). • The model generated a Zn biodegradable alloy with desirable mechanical characteristics, meeting bone implant criteria, including UTS of 363.55 MPA, YS of 318.93 MPA, and Ductility of 14%. • The BO-XGBoost model is highlighted as a tool that can expedite the production of suitable alloys for various medical applications, leading to potential savings in time, money, and effort in the implant development process. Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. The dataset employed in this study comprised 1182 samples of Zn alloys obtained from supplementary articles from Google Scholar and the mat web database. For forecasting the mechanical parameters Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS), the suggested method got maximum R 2 values of 0.85, 0.87, and 0.81 demonstrating its exceptional predictive capacity. In addition, the model created a Zn biodegradable alloy with UTS of 363.55 Mpa, YS of 318.93 Mpa, and Ductility of 14%, which are regarded as good mechanical characteristics meet bone implant criteria. The BO-XGBoost model can expedite the production of the proper alloy for several medical applications, saving time, money, and effort. Bayesian optimization ; Alloy ; Zinc ; Materials science ; Bayesian probability ; Metallurgy ; Computer science ; Artificial intelligence Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science