jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Zhi | Jianping Sun | Yu Yang | Yuxin Chai | Yuyang Liu
Source: ACS Applied Energy Materials (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Rapid Screening Strategy of 2D Materials for Li-Ion Battery (LIB) Electrode Based on Deep Neural Networks (DNN) The development of electrode materials is crucial for achieving an optimal performance in secondary ion batteries. Previous research has accumulated a substantial amount of data on electrode materials, creating varied data sets that include information on ion species, voltage, and other relevant characteristics. In this study, we processed the latest data and employed a deep neural network (DNN) machine learning (ML) platform to construct a regression model. The model relies on easily accessible input information, such as the initial structure, and utilizes high-quality data to validate its reliability. The two-dimensional material data set containing only the material structure is taken as the target set to predict the average discharge voltage (Uav), according to which more than 2500 potential electrode materials are selected. From this pool, we rigorously selected a subset of anode materials for detailed density functional theory (DFT) calculations. These materials exhibit promising elemental compositions and have not been previously investigated as electrode materials. The results of DFT calculations confirmed the reliability of the ML model’s predictions, demonstrating that the combination of ML and DFT calculations can effectively screen data sets lacking expensive DFT-calculated data. This strategy can significantly reduce computational costs by predicting specific performance metrics and conducting preliminary screenings. Electrode ; Battery (electricity) ; Artificial neural network ; Ion ; Deep neural networks ; Materials science ; Computer science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Zijun Qi | Rui Li | Kun Ma | Haoyuan Chen | Yunfei Song | Qijun Wang | Xiang Sun | Lijie Li | Sheng Liu | Wei Shen | Dekun Yang | Gai Wu
Source: International Communications in Heat and Mass Transfer (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

In-depth insights into crystal orientation and temperature dependence of interfacial thermal transport in diamond/SiC heterostructures by machine learning molecular dynamics Materials science ; Diamond ; Heterojunction ; Thermal ; Molecular dynamics ; Crystal (programming language) ; Condensed matter physics ; Thermal conductivity ; Orientation (vector space) ; Chemical physics ; Optoelectronics ; Thermodynamics ; Composite material ; Computer science ; Physics ; Geometry Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science
Authors: Wenjie Du | Shuai Zhang | Zhaohui Cai | Zhiyuan Liu | Jianmin Wang | Xiang Wang | Yang Wang
Source: Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular Merged Hypergraph Neural Network for Explainable Solvation Gibbs Free Energy Prediction Solvation free energies play a fundamental role in various fields of chemistry and biology. Accurately determining the solvation Gibbs free energy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Δ</mml:mi> <mml:msub><mml:mi>G</mml:mi> <mml:mtext>solv</mml:mtext></mml:msub> </mml:math> ) of a molecule in a given solvent requires a deep understanding of the intrinsic relationships between solute and solvent molecules. While deep learning methods have been developed for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Δ</mml:mi> <mml:msub><mml:mi>G</mml:mi> <mml:mtext>solv</mml:mtext></mml:msub> </mml:math> prediction, few explicitly model intermolecular interactions between solute and solvent molecules. The molecular modeling graph neural network more closely aligns with real-world chemical processes by explicitly capturing atomic-level interactions, such as hydrogen bonding. It achieves this by initially establishing indiscriminate connections between intermolecular atoms, which are subsequently refined using an attention-based aggregation mechanism tailored to specific solute-solvent pairs. However, its sharply increasing computational complexity limits its scalability and broader applicability. Here, we introduce an improved framework, molecular merged hypergraph neural network (MMHNN), which leverages a predefined subgraph set and replaces subgraphs with supernodes to construct a hypergraph representation. This design effectively mitigates model complexity while preserving key molecular interactions. Furthermore, to handle noninteractive or repulsive atomic interactions, MMHNN incorporates an interpretation mechanism for nodes and edges within the merged graph, leveraging the graph information bottleneck theory to enhance model explainability. Extensive experimental validation demonstrates the efficiency of MMHNN and its improved interpretability in capturing solute-solvent interactions. Hypergraph ; Solvation ; Artificial neural network ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Discrete mathematics ; Molecule ; Statistics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Sepideh Kavousi | Mohsen Asle Zaeem
Source: Acta Materialia (article, 2025-03-22)
Field: Materials Science — Materials Chemistry

Integration of multiscale simulations and machine learning for predicting dendritic microstructures in solidification of alloys This study presents an integration of machine learning (ML) with a multiscale computational framework to predict primary dendrite arm spacing (PDAS) during alloy solidification. Analytical models, such as Hunt (HT) and Kurz-Fisher (KF), provide the basis for developing parametric and non-parametric ML models that capture the influence of processing conditions and material properties on PDAS. The training and testing dataset is generated from high-throughput phase-field simulations across various alloy systems, incorporating material properties calculated via molecular dynamics. While non-parametric models, such as decision trees, random forests, and gradient boosting decision trees, perform well in training, they encounter overfitting challenges due to the limited size of the computational dataset. In contrast, parametric models, including linear, ridge, and lasso regression, successfully capture key PDAS features, producing predictions that align closely with experimental data. Overall, parametric ML-based models show a stronger dependence on pulling velocity, temperature gradient, and material properties compared to the HT and KF models, offering a more accurate tool for predicting PDAS and optimizing alloy solidification processes. Materials science ; Microstructure ; Metallurgy ; Directional solidification Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Stefaan S. P. Hessmann | Kristof T. Schütt | Niklas W. A. Gebauer | Michael Gastegger | Tamio Oguchi | Tomoki Yamashita
Source: npj Computational Materials (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Accelerating crystal structure search through active learning with neural networks for rapid relaxations Abstract Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates toward their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si 16 , Na 8 Cl 8 , Ga 8 As 8 and Al 4 O 6 but also excels in finding the most stable minimum for the unseen, more complex systems Si 46 and Al 16 O 24 . Moreover, we demonstrate at the example of Si 16 that our method can find multiple relevant local minima while only adding minor computational effort. Artificial neural network ; Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Fei Shuang | Yucheng Ji | Zixiong Wei | Chaofang Dong | Wei Gao | Luca Laurenti | Poulumi Dey
Source: Acta Materialia (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is crucial for enhancing hydrogen transport and storage technologies. However, the vast compositional space and complex chemical environments of MPEAs pose significant challenges. We develop highly accurate machine learning potentials and neural network-driven kinetic Monte Carlo simulations to investigate H diffusion in body-centered cubic (BCC) MoNbTaW MPEAs. H diffusion exhibits super-Arrhenius behavior in MPEAs, controlled by the low percentile of the H solution energy spectrum. Robust analytical models are derived via machine learning symbolic regression to predict H diffusivity across general BCC MPEAs. Additionally, chemical short-range order (SRO) generally does not impact H diffusion in MoNbTaW MPEAs, except it enhances diffusion when H-favoring elements are present in low concentrations. These insights not only deepen our understanding of H diffusion dynamics in MPEAs but also guide the strategic development of advanced MPEAs for hydrogen-related applications by manipulating element type, composition, and SRO. Materials science ; Arrhenius equation ; Diffusion ; Decoding methods ; Hydrogen ; Principal (computer security) ; Element (criminal law) ; Dynamics (music) ; Artificial intelligence ; Statistical physics ; Thermodynamics ; Computer science ; Physical chemistry ; Algorithm ; Activation energy ; Physics ; Acoustics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhuo Zheng | Keyan Liu | Xiyuan Zhu
Source: Journal of Technology Innovation and Engineering (article, 2025-09-19)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning approaches for predicting MOF material properties. We employed five different machine learning models: Random Forest, XGBoost, LightGBM, Support Vector Machine, and Neural Network, to analyze and predict MOF characteristics using a dataset from the Kaggle platform. The models were evaluated using multiple performance metrics, including RMSE, R², MAE, and cross-validation scores. Results demonstrated that the Random Forest model achieved superior performance with an R² value of 0.891 and RMSE of 0.152, significantly outperforming other models. LightGBM showed remarkable computational efficiency, completing training in 25.7 seconds while maintaining high accuracy. Our comparative analysis revealed that ensemble learning methods generally exhibited better performance than traditional single models in MOF property prediction. This research provides valuable insights into the application of machine learning in materials science and establishes a robust framework for future MOF material design and property prediction. Random forest ; Ensemble learning ; Property (philosophy) ; Support vector machine ; Artificial neural network ; Statistical learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhoulin Liu | Jianchun Sha | Guang‐Ling Song | Ziliang Wang | Yinghe Zhang
Source: Chemical Engineering Journal (article, 2025-05-24)
Field: Materials Science — Biomaterials

Understanding magnesium dissolution through Machine learning molecular dynamics Dissolution ; Magnesium ; Molecular dynamics ; Dynamics (music) ; Computer science ; Materials science ; Chemistry ; Biological system ; Chemical engineering ; Metallurgy ; Engineering ; Physics ; Computational chemistry ; Biology Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal Extraction and Bioleaching
Authors: Yitong Niu | Ying Ying Tye | Tannimalay Hemashini | Cheu Peng Leh
Source: Biomass and Bioenergy (article, 2025-05-12)
Field: Chemistry — Analytical Chemistry

Feasibility of use limited data to establish a relationship between chemical composition and the enzymatic glucose yield using machine learning Yield (engineering) ; Composition (language) ; Enzyme ; Chemical composition ; Chemistry ; Machine learning ; Computer science ; Artificial intelligence ; Biochemistry ; Materials science ; Organic chemistry ; Philosophy Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Microbial Metabolic Engineering and Bioproduction
Authors: Heesu Hwang | Hyeseong Jeong | J. Y. Cho | Youkeun K. Oh | Do-Kyun Kim | Dong Wook Shin | Jong‐Ho Lee | Hyoungchul Kim | Jin‐Ha Hwang
Source: Small (article, 2025-01-19)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Microstructural Quantification of Multiphase Cathode Composites in All‐Solid‐State Batteries: Correlation with Battery Performance Microstructure optimization and high-performance material development are crucial for improving the electrochemical performance of all-solid-state batteries (ASSBs). Researchers frequently record numerous micro-scale or nano-scale electron micrographs for unbiased post-mortem analysis, performance evaluation, and improvement of ASSBs. However, these micrographs are often underutilized and typically analyzed qualitatively without ensuring an accurate representation of the experimental objectives. This study explores machine learning (ML)-based quantitative analysis techniques using electron microscopy images, combined with a stereology-driven linear-intercept concept method and semantic segmentation, to extract quantitative microstructural parameters for optimizing ASSB performance. The applicability of ML-assisted image analytics is demonstrated by employing composite cathodes in ASSBs to achieve unbiased automation and deep semantic segmentation during microstructural characterization. Furthermore, the ramifications of this ML-assisted method are discussed, along with its advantages and disadvantages in battery research. Materials science ; Battery (electricity) ; Composite number ; Segmentation ; Characterization (materials science) ; Scanning electron microscope ; Computer science ; Automation ; Microstructure ; Artificial intelligence ; Cathode ; Scale (ratio) ; Nanotechnology ; Machine learning ; Process engineering ; Composite material ; Mechanical engineering ; Power (physics) ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Zihao Gao | Changsheng Zhu | Canglong Wang | Yafeng Shu | Shuo Liu | Jiashi Miao | Lei Yang
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Advanced deep learning framework for multi-scale prediction of mechanical properties from microstructural features in polycrystalline materials Crystallite ; Scale (ratio) ; Materials science ; Artificial intelligence ; Composite material ; Computer science ; Metallurgy ; Geography ; Cartography Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ali Ebadi | Manpreet Kaur | Qian Liu
Source: Computational Materials Science (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Hyperparameter optimization and neural architecture search algorithms for graph Neural Networks in cheminformatics • Comprehensive review of cheminformatics datasets for molecular property prediction. • Survey of optimization techniques for Graph Neural Networks in cheminformatics. • Comparison of optimization methods, highlighting strengths and limitations. • Identify gaps and future directions in Graph Neural Networks for cheminformatics. Cheminformatics, an interdisciplinary field bridging chemistry and information science, leverages computational tools to analyze and interpret chemical data, playing a critical role in drug discovery, material science, and environmental chemistry. Traditional methods, reliant on rule-based algorithms and expert-curated datasets, face challenges in scalability and adaptability. Recently, machine learning and deep learning have revolutionized cheminformatics by offering data-driven approaches that uncover complex patterns in vast chemical datasets, advancing molecular property prediction, chemical reaction modeling, and de novo molecular design. Among the most promising techniques are Graph Neural Networks (GNNs), which have emerged as a powerful tool for modeling molecules in a manner that mirrors their underlying chemical structures. Despite their success, the performance of GNNs is highly sensitive to architectural choices and hyperparameters, making optimal configuration selection a non-trivial task. Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) are crucial for improving GNN performance, but the complexity and computational cost of these processes have traditionally hindered progress. This review examines various strategies for automating NAS and HPO in GNNs, highlighting their potential to enhance model performance, scalability, and efficiency in key cheminformatics applications. As the field evolves, automated optimization techniques are expected to play a pivotal role in advancing GNN-based solutions in cheminformatics. Cheminformatics ; Hyperparameter ; Computer science ; Artificial neural network ; Graph ; Machine learning ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Shirun Zhao | Shinya Mine | Gang Wang | Weiyang Zhang | Abdellah Ait El Fakir | Bin Yang | Zengwei Qin | Nazmul Hasan MD Dostagir | Koichi Matsushita | Ichigaku Takigawa | Ken‐ichi Shimizu | Takashi Toyao
Source: Journal of the American Chemical Society (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Development of Highly Active Catalysts for Low-Temperature CO <sub>2</sub> Hydrogenation to Methanol Using a Machine Learning Approach The conversion of CO<sub>2</sub> into methanol through hydrogenation represents a promising approach for the utilization of CO<sub>2</sub> and sustainable chemical production. However, current industrial methods rely on copper-based catalysts, which exhibit low CO<sub>2</sub> conversion and limited methanol yields and require high temperatures and pressures. In this study, we utilized a machine-learning (ML) approach to develop low-temperature CO<sub>2</sub> hydrogenation catalysts. By employing iterative ML model predictions and experimental validation in batch reactors, we screened 580 distinct catalysts and identified 33 catalysts that outperformed the previously reported highly active catalyst (Pt(3)/Mo(20)/TiO<sub>2</sub>). The best catalyst, Pt(5)/Mo(8)-Re(1)-W(0.7)/TiO<sub>2</sub>, exhibited a methanol production rate of 1.46 mmol g<sup>-1</sup> h<sup>-1</sup> in a batch reactor and a high production rate of 1.8 mmol g<sup>-1</sup> h<sup>-1</sup> in a flow reactor at 150 °C under 4 MPa (H<sub>2</sub>/CO<sub>2</sub> = 3). <i>In situ/operando</i> spectroscopic analysis was conducted to elucidate the function of each catalyst component in the methanol synthesis. Detailed analysis revealed that in the best catalyst, Pt primarily facilitated H<sub>2</sub> dissociation, partially reduced Mo oxides were crucial in generating oxygenated species, and the presence of acidic W promoted methanol desorption from the catalyst surface. Re promoted the formate conversion to methanol, thus accelerating the overall methanol formation. Chemistry ; Methanol ; Catalysis ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Carbon dioxide utilization in catalysis
Authors: Shitong Nie | Lin Tao | Honglei Yu | Davoud Dastan | Wensen Wang | L. Hong | Lixiang Li | Baigang An | Yaqiong Su
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Theoretical and machine learning exploration of electronic factors governing Ni-centered CO<sub>2</sub> reduction catalysts Carbon-based electrocatalysts are promising candidates for CO<sub>2</sub> reduction due to their intrinsic redox properties. However, achieving an in-depth understanding and rational design of the nickel site coordination environment and active site density remains a significant challenge. In this study, a single-atom catalyst supported on graphene was designed to reduce CO<sub>2</sub> to CO or HCOOH, based on density functional theory. The catalytic activity of the Ni<sub><i>n</i></sub>-C<sub><i>x</i></sub>N<sub><i>y</i></sub>-<i>d</i> monolayer was systematically evaluated through charge density, density of states, and molecular dynamics analyses, verifying the conductivity and stability. Furthermore, an analysis of the Gibbs free energy pathway and electronic structure revealed that Ni<sub>2</sub>-C<sub>3</sub>N<sub>1</sub>-1 exhibits excellent catalytic performance for CO production in the CO<sub>2</sub> reduction reaction, while Ni<sub>2</sub>-C<sub>2</sub>N<sub>2</sub>-1 demonstrates superior performance for HCOOH, with relatively low limiting potentials of -0.27 V and -0.09 V, respectively. Molecular orbital theory analysis underscores the critical role of bonding states in explaining the adsorption energy of intermediate products. Moderate adsorption energy is shown to effectively suppress hydrogen evolution reactions, thereby enhancing both reaction activity and product selectivity. Leveraging the best-performing machine learning XGBoost model, the feature importance between HCOOH product and the Ni single-atom catalyst structure was predicted to be 0.568, allowing for the identification of optimal tuning strategies to achieve superior catalytic performance. This study provides novel theoretical insights and technological strategies for advancing sustainable CO<sub>2</sub> reduction. Catalysis ; Density functional theory ; Gibbs free energy ; Graphene ; Adsorption ; Molecular dynamics ; Reduction (mathematics) ; Scalability Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Zhongwei Chen | Chunlei He | Kai Wang | Rong Feng | Xiang Long | Z.J. Zuo | Changliang Wang | Xinyuan Yang | Yong Guo | Juncheng Jiang | Yuan Yu
Source: Chemical Engineering Journal (article, 2025-05-19)
Field: Materials Science — Polymers and Plastics

Machine learning-driven molecular generation for accelerated screening of high-performance flame retardants in epoxy resin composites Epoxy ; Composite material ; Materials science ; Fire retardant Flame retardant materials and properties ; Toxic Organic Pollutants Impact ; Photopolymerization techniques and applications