jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: José L. C. Fajín | Amit Kumar Halder | M. Natália D. S. Cordeiro
Source: The Journal of Physical Chemistry C (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Prediction of Activation Energies for Catalyst Design in the Water Gas Shift Reaction The water gas shift (WGS) reaction is a critical process in various industrial applications, with its rate often governed by the activation energy of water dissociation on metal-based catalysts. In this work, we develop a machine learning model to predict the activation energy for water dissociation across diverse catalytic surfaces, including monometallic, bimetallic, and trimetallic surfaces, metallic nanotubes, and platinum nanoparticles. The model links activation energy to easily computable descriptors, such as adsorption energies or distances of the involved reaction species, derived from density functional theory (DFT) calculations. Our multilayer perceptron-based regression model demonstrates exceptional predictive accuracy. A Shapley additive explanation (SHAP) analysis discloses that all input variables contribute positively to the activation energy, with the adsorption energy of reaction products identified as the most influential feature. Remarkably, our model enables the prediction of activation energy for water dissociation and WGS on metal-based catalysts not included in the training set by simply DFT computing adsorption energies and distances, significantly reducing computational effort. This approach thus provides a powerful tool for the rapid screening and optimization of catalysts for the WGS reaction, paving the way for more efficient industrial processes. Water-gas shift reaction ; Catalysis ; Activation energy ; Chemical engineering ; Computer science ; Chemistry ; Physical chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Sivaraos | Rakesh Kumar Phanden | Kyoungoh Lee | Ermira Junita Abdullah | Kalyanaraman Kumaran | Abdullah Al-Obaidi | Ramaswamy Devarajan
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Artificial neural network for performance modelling of shape memory alloy Abstract In recent years, significant strides in technological advancement have revolutionized our lifestyles, driving a surge in demand for multifunctional and intelligent materials. Among these materials, Shape Memory Alloy (SMA) stands out for its unique ability to memorize and revert to its original shape through phase transformation. Despite its remarkable properties, SMAs exhibit a minor limitation in accurately retaining their original shape or length. Furthermore, there is a notable dearth of information regarding the modelling of SMA behaviour with high precision. This study endeavors to address these challenges by integrating experimental data with neural network modelling to comprehensively examine SMA behaviour for mechanical applications. Leveraging an experimental dataset, this research employs feedforward backpropagation neural network (BPNN) modelling to forecast the strain recovery of SMA Nitinol alloy. The model aims to predict the recovery strain of SMA by utilizing three input parameters: temperature conditional, number of coils, and initial length. Remarkably, the achieved error rates of 0.29%, 0.80%, and 9.20% for various strain measurements significantly undercut the commonly accepted error threshold of 10% for nonlinear data predictions in SMA behaviour. The final results underscore the high prediction accuracy of the Artificial Neural Network (ANN), offering promising prospects for SMA applications involving temperature-strain interactions and enhancing engineering design. SMA* ; Shape-memory alloy ; Artificial neural network ; Memorization ; Backpropagation ; Computer science ; Feed forward ; Feedforward neural network ; Artificial intelligence ; Nonlinear system ; Machine learning ; Mechanical engineering ; Control engineering ; Engineering ; Algorithm ; Mathematics Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Jiang You | Cheng Wang | Hong Ju | S. X. Hu | Yongzhen Wang | Hui‐Yuan Wang
Source: Journal of Material Science and Technology (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Evaluating kinetic properties of Mg-based alloy melts via deep learning potential driven molecular dynamics simulations • We developed deep learning potentials for simulating binary Mg-Al, Mg-Si, Mg-Ca, and Mg-Zn alloy melts, and the kinetic properties were accessed by deep potential molecular dynamics simulations (DPMD). • Our theoretically evaluated viscosity of Mg-Al melts exhibits excellent agreement with experimental measurements over a wide temperature range. • The solute elements Ca and Zn exhibit sluggish liquid kinetics in the studied melts, while supporting the promising glass-forming ability of the Mg-Zn-Ca alloy system . • DPMD simulations are a feasible protocol to establish comprehensive and reliable database for the liquid kinetics of Mg-alloy melts. The kinetic properties of Mg alloy melts are crucial for determining the forming quality of castings, as they directly affect crystal nucleation and dendritic growth. However, accurately assessing the kinetic properties of molten Mg alloys remains challenging due to the difficulties in experimentally characterizing the high-temperature melts. Herein, we propose that molecular dynamics (MD) simulations driven by deep learning based interatomic potentials (DPs), referred to as DPMD, are a promising strategy to tackle this challenge. We develop MgAl-DP, MgSi-DP, MgCa-DP, and MgZn-DP to assess the kinetic properties of Mg-Al, Mg-Si, Mg-Ca, and Mg-Zn alloy melts. The reliability of our DPs is rigorously evaluated by comparing the DPMD results with those from ab initio MD (AIMD) simulations, as well as available experimental results. Our theoretically evaluated viscosity of Mg-Al melts shows excellent agreement with experimental results over a wide temperature range. Additionally, we found that the solute elements Ca and Zn exhibit sluggish kinetics in the studied melts, which supporting the promising glass-forming ability of the Mg-Zn-Ca alloy system. The computational efficiency of DPMD simulations is several orders of magnitude higher than that of AIMD simulations, while maintaining ab initio -level accuracy. This makes DPMD a highly feasible protocol for building a comprehensive and reliable database of kinetic properties of Mg alloy melts. Molecular dynamics ; Kinetic energy ; Alloy ; Dynamics (music) ; Materials science ; Statistical physics ; Chemical physics ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Metallurgy ; Classical mechanics Machine Learning in Materials Science ; Hydrogen Storage and Materials ; X-ray Diffraction in Crystallography
Authors: Zhe Shang | Susu Zhao | Qian Dang | Fengmei Wang | Xiaoming Sun | Hui Li
Source: ACS Catalysis (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

A General Machine-Learning Framework for High-Throughput Screening for Stable and Efficient RuO<sub>2</sub>-Based Acidic Oxygen Evolution Reaction Catalysts Doping guest elements is an effective way to increase the activity and stability of RuO2 catalysts in acidic oxygen evolution reaction (OER). However, due to the vastness of the doping space, it is challenging for either high-cost experiments or density functional theory (DFT) calculations to screen out the doped structures with the optimized catalytic performance. Herein, we reported a machine-learning (ML) framework that aims to realize high-throughput screening for both stability and activity of doped-RuO2 acidic OER catalysts from monodoping to triple-doping at a low level of computational cost. Compared to the d-band theory and some other previous models, our ML model was constructed based on more general input features and realized higher prediction accuracy with mean absolute errors (MAEs) of 0.074, 0.142, and 0.082 eV for *OH, *O, and *OOH adsorption energies, respectively. Through the ML models, three doping structures Ru41Zn7O96, Ru41Fe3Zn4O96, and Ru39Co1Cu4Zn4O96 were found to possess extraordinarily high stability and comparable or higher activity than previously reported OER catalysts. This work provided an efficient study paradigm in fields of material screening and a useful guide for experimental synthesis. Catalysis ; Throughput ; Oxygen evolution ; Oxygen ; Computer science ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Electrochemistry ; Physical chemistry ; Organic chemistry ; Wireless ; Operating system ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Yuze Liu | L. Wang | Weigang Zhu | Xi Yu
Source: Journal of Materials Informatics (article, 2025-06-19)
Field: Materials Science — Materials Chemistry

Farthest point sampling in property designated chemical feature space as an effective strategy for enhancing the machine learning model performance for small scale chemical dataset Machine learning (ML) model development in chemistry and materials science often grapples with the challenge of small and imbalanced labeled datasets, a common limitation in experimental studies. These dataset imbalances can precipitate overfitting and diminish model generalization. Our study explores the efficacy of the farthest point sampling (FPS) strategy within targeted chemical feature spaces, demonstrating its capacity to generate well-distributed training sets and consequently enhance model performance. We rigorously evaluate this strategy across various ML models, including artificial neural networks, support vector machines, and random forests, using datasets with target physicochemical properties such as standard boiling points and enthalpy of vaporization. Our findings reveal that FPS-based models consistently surpass randomly sampled models, exhibiting superior predictive accuracy and robustness, alongside a marked reduction in overfitting. This improvement is particularly pronounced in smaller training set, attributable to increased diversity within the training data’s chemical feature space. Consequently, FPS emerges as an effective and adaptable approach for achieving high-performance ML models at reduced cost by limited and biased experimental datasets typical in chemistry and materials science. Overfitting ; Machine learning ; Random forest ; Computer science ; Artificial intelligence ; Robustness (evolution) ; Generalization ; Feature (linguistics) ; Feature vector ; Chemical space ; Sampling (signal processing) ; Support vector machine ; Training set ; Property (philosophy) ; Artificial neural network ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Michele Tomasini | Lucia Caporaso | Martí Gimferrer | Albert Poater
Source: Coordination Chemistry Reviews (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

On the use of chemical bonding descriptors in machine learning This review explores recent advances in machine learning in chemistry, emphasizing mechanistic understanding, performance optimization, and emerging design strategies. Key developments include novel synthesis routes, computational screening, hybrid experimental–theoretical approaches, and in-situ characterization. The review highlights how these innovations improve efficiency, selectivity, and scalability while uncovering fundamental structure-activity relationships. Special attention is given to integrating predictive modeling and high-throughput experimentation, which accelerates discovery cycles and enables rational design. Comparative discussions of different methodologies reveal synergies between traditional approaches and data-driven tools. Despite remarkable progress, translating laboratory results into practical applications remains a central challenge. The review concludes by outlining open questions, methodological gaps, and future research directions aimed at developing robust, cost-effective, and environmentally sustainable solutions Chemical bond ; Support vector machine ; Pattern recognition (psychology) ; Feature (linguistics) ; Computational learning theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Wenjie Gao | Yongsheng Xu | Xianglin Chang | Xing Xu | Ning Li | Beibei Yan | Guanyi Chen | Xiaoguang Duan
Source: Environmental Science & Technology (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Global Optimization of Single-Atom Catalyst-Mediated Advanced Oxidation Processes Single-atom catalysts (SACs) are state-of-the-art for advanced oxidation processes (AOPs) for purifying water contaminants. While previous studies have explored individual influencing factors, such as the central metal species and coordination environment, reaction conditions, or contaminant molecular properties, the combined effects of these variables on AOP kinetics and thermodynamics remain poorly understood. Here, we propose a machine learning model based on a global optimization strategy that leverages a random forest model to predict pollutant degradation performance with high accuracy. The d electron number of the central metal and the average electronegativity of the coordination environment are identified as key descriptors in determining AOP performances. Theoretical calculations, including charge density distribution, adsorption energy, projected density of states, and crystal orbital Hamilton population metrics, reveal strong linear relationships between these descriptors and peroxymonosulfate activation energy. Global optimization analysis reveals that the optimal catalyst configuration requires metals possessing 5-7 d electrons, combined with coordination environments with average electronegativity values below 3.04. In addition, contaminant characteristics significantly affect degradation performances. Specifically, faster pollutant degradation is realized for organics with energy gaps below 3.92 eV and dipole moments greater than 7 D. This study offers a machine learning-guided pathway for intelligent design of SACs for effective AOP-based purification systems. Electronegativity ; Adsorption ; Degradation (telecommunications) ; Catalysis ; Population ; Pollutant ; Metal ; Global optimization Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Runjie Bao | Zhao Wang | Qiwen Guo | Xiaoyu Wu | Qingchun Yang
Source: ACS Catalysis (article, 2025-07-12)
Field: Materials Science — Materials Chemistry

Bio-Digital Catalyst Design: Generative Deep Learning for Multi-Objective Optimization and Chemical Insights in CO<sub>2</sub> Methanation To address the limitations of traditional trial-and-error experimental methods and deep learning algorithms in the design of CO2 methanation catalysts, this study proposes a dual-driven deep learning (BODAL) strategy. This approach aims to enhance prediction accuracy by leveraging bioinspired optimization algorithms for adaptive hyperparameter configuration and employing generative techniques for data augmentation. By systematically comparing six bioinspired optimization algorithms, the particle swarm optimization algorithm is identified as the most effectively automatic hyperparameter configuration tool and combined with the FT-Transformer (FTT) deep learning model to achieve high-precision prediction of the CO2 conversion ratio and CH4 yield. Compared with numerous generative data augmentation strategies, the generation of synthetic data by the Tabular Variational AutoEncoder approach is a more effective approach. It significantly alleviates the limitations of small sample data by iteratively enhancing the preferred FTT model, achieving a test set R2 of 0.9591. The global and local interpretability analysis using SHAP and partial dependence plots reveals the contributions of 23 input variables to the model’s predictive performance, as well as the regulatory mechanisms of the most significant discrete and continuous variables on the catalytic performance of the CO2 methanation reaction. The optimized FTT model is further integrated with the multiobjective particle swarm optimization algorithm, successfully optimizing the content of the active component and the reaction temperature for the widely used Ni/Al2O3 catalyst. Additionally, it facilitated the prediction of six novel, highly efficient Ni-based catalysts. Notably, Ni–Y/ZrO2–La2O3 achieves a CH4 yield of 90.30% with an active component content of approximately 5.35%, while Ni–Gd/Al2O3–Pr2O3 demonstrated a high CH4 yield (59.89%) at a low temperature of 210 °C, surpassing the experimentally reported value of 52.44%. Methanation ; Catalysis ; Chemistry ; Chemical engineering ; Biochemical engineering ; Process engineering ; Computer science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Hydrodesulfurization Studies
Authors: Yuxuan Zeng | Wei Cao | Peng Tan | Yue Hou | Ling Miao | Ziyu Wang | Jing Shi
Source: Applied Materials Today (article, 2025-02-16)
Field: Materials Science — Materials Chemistry

A machine learning-based framework for predicting the power factor of thermoelectric materials Power factor ; Factor (programming language) ; Thermoelectric effect ; Power (physics) ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Shuo Tao | Li Zhu
Source: The Journal of Physical Chemistry Letters (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments. The model demonstrates superior performance across various prediction tasks of materials' properties, achieving particularly notable results in properties sensitive to many-body interactions. For band gap prediction, EOSnet achieves a mean absolute error of 0.163 eV, surpassing previous state-of-the-art models. The model also excels in predicting mechanical properties and classifying materials, with 97.7% accuracy in metal/nonmetal classification. These results demonstrate that embedding GOM fingerprints into node features enhances the ability of GNNs to capture complex atomic interactions, making EOSnet a powerful tool for materials' discovery and property prediction. Computer science ; Embedding ; Artificial neural network ; Gaussian ; Graph ; Representation (politics) ; Node (physics) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zhijun Zhang | Yingying Yuan | Yayang Tian | Yuan Zhao | Tao Chen | Jie Gao | Linfeng Chen
Source: Sensors and Actuators B Chemical (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Machine learning-assisted carbon quantum dots-molecularly imprinted polymer array for synchronous screening of multiple sulfonamides Molecularly imprinted polymer ; Carbon quantum dots ; Quantum dot ; Molecular imprinting ; Nanotechnology ; Polymer ; Materials science ; Chemistry ; Organic chemistry ; Selectivity Carbon and Quantum Dots Applications ; Analytical chemistry methods development ; Electrochemical sensors and biosensors
Authors: Quan Li | Siqi Zhan | Zhanjie Liu | Caibo Dong | Hengheng Zhao | Tongkui Yue | Qingsong Zhao | Liqun Zhang | Ying Li | Jun Liu
Source: Journal of Chemical Information and Modeling (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Developing Hybrid Machine Learning Frameworks for Polymer Property Prediction Based on Composition and Sequence Features Artificial intelligence (AI) plays a significant role in advancing polymer science and engineering. Considering the critical role of the glass transition temperature (<i>T</i><sub>g</sub>) in determining the physical properties of polymers, this study systematically investigates the influence of their composition and sequence structure on <i>T</i><sub>g</sub> using machine learning (ML) models. To clarify the complex relationship between polymer composition and <i>T</i><sub>g</sub>, the k-nearest neighbor mega-trend diffusion (kNNMTD) method was employed for data augmentation, and various ML models were constructed for <i>T</i><sub>g</sub> prediction. Among them, the Random Forest model demonstrated the best performance for the generated data, achieving an <i>R</i><sup>2</sup> of 0.85 and an RMSE of 0.38. To explore the effect of polymer sequence structure on <i>T</i><sub>g</sub>, we further introduced natural language processing (NLP) techniques to represent polymer sequences. The data was augmented using the Wasserstein generative adversarial network (GAN) with gradient penalty (WGAN-GP) model, and <i>T</i><sub>g</sub> predictions were made using a convolutional neural network-long short-term memory (CNN-LSTM) model. This integrated framework achieved excellent predictive performance, with an <i>R</i><sup>2</sup> of 0.95 and an RMSE of 0.23, and demonstrated strong generalization across different data sets. In summary, this study introduces an innovative application of kNNMTD for augmenting polymer composition data combined with NLP techniques for representing polymer sequences. The proposed ML framework offers a valuable contribution to the advancement of polymer material design and optimization. Sequence (biology) ; Composition (language) ; Property (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jialu Chen | Min Wang | Chao Jiang | Xintong Dou | Xiang Yin | Yunliang Yue
Source: Solar Energy (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Machine learning: from perovskite materials to devices Perovskite (structure) ; Materials science ; Computer science ; Engineering physics ; Chemical engineering ; Physics ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing