jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yue Zhu | Fengyu Gao | Lei Yi | Honghong Yi | Qingjun Yu | Shunzheng Zhao | Yuansong Zhou | Ya Wang | Xiaolong Tang
Source: Applied Energy (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules In the field of chemical and environmental engineering, research on the adsorption and catalytic processes of gas molecules has long been constrained by the efficiency bottleneck of traditional trial and error methods and empirical analysis. With the rapid development of artificial intelligence technology, machine learning (ML), leveraging its powerful data processing and pattern recognition capabilities, has provided a revolutionary tool for material design and optimization. This paper systematically reviews the latest progress of ML in the research of gas-molecule adsorption and catalytic materials, focusing on three core aspects: formulation screening, material property correlation, and descriptor construction. By integrating experimental conditions and material characteristics, ML models can efficiently predict adsorption/catalytic performance and uncover key descriptors to guide the design of new materials. In formulation screening, ML can identify key factors from complex multivariable systems and optimize the composition, synthesis methods, and reaction conditions of catalysts and adsorbents. Regarding material property correlation, it can deeply analyze the internal relationships between the physical-chemical and reaction properties of materials. In the field of descriptor construction, innovative designs of electronic, elemental, and structural descriptors offer a new perspective for understanding material properties and catalytic reactions. Although ML shows great potential in this field, it also faces many challenges, such as data quality and scarcity, model interpretability and generalization ability, and descriptor universality. This paper not only reviews key application cases of ML in gas pollutant treatment but also proposes the urgent need for interdisciplinary collaboration and the construction of standardized databases. Despite being in the early stage of research, the ML-driven Materials Genome Initiative has demonstrated the potential to revolutionize traditional research and development models, opening up a new path for solving environmental and energy problems. • Machine learning can screen catalyst and adsorbent formulations. • Material properties of catalysts and adsorbents can be correlated by machine learning. • Design descriptors accurately capture material properties. • Databases and descriptors are key to machine learning. • Improving the interpretability of models is key to future research. Adsorption ; Perspective (graphical) ; Catalysis ; Nanotechnology ; Molecule ; Chemistry ; Engineering ; Materials science ; Biochemical engineering ; Computer science ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Christian Venturella | Jiachen Li | Christopher Hillenbrand | Ximena Leyva Peralta | Jessica Liu | Tianyu Zhu
Source: Nature Computational Science (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Unified deep learning framework for many-body quantum chemistry via Green’s functions Quantum ; Computer science ; Chemistry ; Nanotechnology ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Md. Habibur Rahman | Arun Mannodi‐Kanakkithodi
Source: Journal of Physics Materials (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Defect modeling in semiconductors: the role of first principles simulations and machine learning Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability. Semiconductor ; Computer science ; Engineering physics ; Materials science ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Shiwei Su | Tsukuru Masuda | Madoka Takai
Source: Langmuir (article, 2025-03-13)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure-property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for various applications, such as protein immobilization supports, biosensors, and antibiofouling surfaces. However, protein adsorption is a complex phenomenon that is difficult to predict quantitatively owing to the involvement of multiple factors. Therefore, this study aims to establish a machine learning model for protein adsorption on densely packed polymer brushes with various chemical structures, as these surfaces are well-suited for analyzing structure-property correlations between the polymer's chemical structure and adsorption amount during initial protein adsorption. Two proteins, bovine serum albumin (BSA) and lysozyme, are adopted as target proteins, with the expectation that differences in their charge profiles will be reflected in the resulting machine learning model. The descriptors of the polymer brush surfaces include their grafted structures (thickness) and chemical properties, which are described by the contact angle and ζ potential. This allows physicochemical knowledge to be incorporated into the machine learning model. Random forest exhibits the best performance in all situations, accurately predicting the amounts of adsorbed BSA and lysozyme. In addition, the prediction of the contact angle and ζ potential by machine learning also enables a quantitative and explainable prediction of protein adsorption based on theoretical molecular descriptors, ensuring that no characteristics are overlooked. Moreover, the model is used to analyze the contributions of electrostatic and hydrophobic interactions to protein adsorption. In conclusion, a machine learning model is developed to predict protein adsorption on polymer brush surfaces, incorporating descriptors such as the grafted structure, contact angle, and ζ potential. It provides quantitative predictions and analyzes the roles of electrostatic and hydrophobic interactions, advancing the design of functional polymer surfaces for applications in biosensors and antifouling technologies. Brush ; Adsorption ; Polymer ; Polymer brush ; Chemistry ; Chromatography ; Chemical engineering ; Materials science ; Nanotechnology ; Organic chemistry ; Composite material ; Engineering Polymer Surface Interaction Studies ; Nanofabrication and Lithography Techniques ; Protein purification and stability
Authors: Jinkai Zhang | Yaopeng Li | Ming Chen | Jiaping Fu | Liang Zeng | Xi Tan | Tian Sun | Guang Feng
Source: Journal of Energy Chemistry (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Atomistic simulation of batteries via machine learning force fields: From bulk to interface Interface (matter) ; Materials science ; Force field (fiction) ; Nanotechnology ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Advanced Battery Technologies Research ; X-ray Diffraction in Crystallography
Authors: Hanxuan Wang | Raman Kumar | Ashutosh Pattanaik | Rajender Kumar | Ali Salam Al-jaberi | Mayada Ahmed Abass
Source: Frontiers in Materials (review, 2025-08-08)
Field: Materials Science — Biomaterials

Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimization approaches Magnesium (Mg) alloys show promise for lightweight structural and biomedical applications, but they face challenges such as poor corrosion resistance and complex deformation behavior. This systematic review explores how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) address these limitations. These techniques enable the fast and accurate prediction and optimization of material properties, thereby reducing experimental effort and accelerating the design of high-performance Mg alloys. A multi-database validation approach using Scopus and Web of Science ensured methodological robustness when searching for AI, ML, and DL in Mg alloys. A comparative analysis of author keywords, index keywords, sources, authors, and countries confirmed strong thematic consistency between databases, thereby enhancing the credibility of the cluster-based bibliometric analysis. The PRISMA framework was used to ensure the structured literature search, eligibility assessment, and documentation of the selection process. 185 peer-reviewed articles (2015–2025) were analyzed and organized into seven refined thematic clusters: ‘mechanical behavior modeling using neural networks’, ‘AI-driven alloy design and compositional optimization’, ‘atomic-scale modeling and physics-guided learning’, ‘AI applications in welding and thermomechanical processing’, ‘biomaterials and microstructural optimization’, ‘corrosion modeling and degradation prediction’, ‘data-driven design and integrated optimization frameworks’. The review highlights the extensive application of models, including Artificial Neural Networks, Convolutional Neural Networks, and hybrid frameworks that combine ML with optimization algorithms or physical simulations. These approaches enhance predictions on mechanical properties, microstructural changes, corrosion behavior, and processing results of Mg alloys. The study also discusses cross-cutting themes such as simulation speed-up metrics, model interpretability across domains, and limitations in dataset coverage. Findings indicate AI-based methods can expedite alloy design and performance optimization; however, challenges remain in data accessibility, model interpretability, and experimental validation. The study concludes that integrating physics-informed ML models, using multimodal data, and employing inverse design will be crucial for advancing the intelligent development of high-performance Mg alloys for sustainable engineering applications. Artificial intelligence ; Computer science ; Machine learning ; Artificial neural network ; Interpretability ; Convolutional neural network ; Deep learning Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Corrosion Behavior and Inhibition
Authors: Zonglin Yi | Yitong Zhou | Hao Liu | Li Li | Yan Zhao | Jiayuan Li | Yixuan Mao | Fangyuan Su | Cheng‐Meng Chen
Source: npj Computational Materials (article, 2025-05-16)
Field: Chemistry — Electrochemistry

Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models Accurate prediction of practical reduction electrode potentials (Ered) of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction. However, the emergence of new electrolyte solvents and additives leaves most of the reaction mechanisms unveiled. Here, we provide a machine-learning-assisted workflow of thermodynamically quantified Ered prediction for electrolyte solvents. A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps. Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms. Validation of the best-scoring model is conducted by experimental validation of 6 additional solvents. Multiple thermodynamics features are found impactful on Ered through different chemical bonding with reaction intermediates. This workflow enables accurate Ered prediction for electrolyte solvents without identified reduction mechanisms, and is widely applicable in the electrochemical energy storage area. Electrolyte ; Reduction (mathematics) ; Electrode ; Reversible hydrogen electrode ; Computer science ; Standard hydrogen electrode ; Artificial intelligence ; Chemistry ; Reference electrode ; Mathematics ; Physical chemistry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Kais Iben Nassar | Tiago L. P. Galvão | José D. Gouveia | José R. B. Gomes
Source: The Journal of Physical Chemistry C (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Predicting Adsorption Energies on MXene Surfaces Using Machine Learning to Enhance Catalyst Design for the Water–Gas Shift Reaction Efficient prediction of gas-phase adsorption energies on MXene surfaces is critical for advancing materials science applications of these materials. This study integrates data from density functional theory calculations, both in-house and from the literature, to train, validate, and test machine learning models for predicting adsorption energies of various species involved in the water–gas shift reaction (WGSR) mechanism (H2O, CO2, H2, CO, O2, OH, O, H) on MXenes, considering different compositions and surface terminations (O, H, S, F, Cl, among others). Our database comprises 600 data points with diverse structural, electronic, and adsorption properties. We identify key properties influencing adsorption behavior through data preprocessing and feature selection. Five supervised machine learning models were employed: random forest regression (RFR), XGBoost regression (XGB), artificial neural network (ANN), decision tree regression (DTR), and gradient boosting regression (GBR). Each model underwent cross-validation using 80% of the dataset and testing on a 20% hold-out sample. Results demonstrate that RFR and XGB effectively predict adsorption energies of important species in the WGSR, showing a good correlation between actual and predicted values. Feature importance analysis highlights the significance of crucial adsorbate characteristics, such as the number of radical electrons, the molecular weight, and the total number of valence electrons, as well as key MXene features, such as the Bader charges of the transition metal element (M) and of surface terminations (T), and the standard deviation of block, which refers to the measure of the amount of variation or dispersion of properties among elements within a specific block of the periodic table. This study enhances our understanding of MXene-based materials and provides a promising predictive approach for the adsorption behavior, with implications for catalyst design, such as in the WGSR. Water-gas shift reaction ; Adsorption ; Catalysis ; Chemical engineering ; Materials science ; Chemistry ; Physical chemistry ; Engineering ; Organic chemistry MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Alex K. Chew | Mohammad Atif Faiz Afzal | Zachary Kaplan | Eric M. Collins | Suraj Gattani | Mayank Misra | Anand Chandrasekaran | Karl Leswing | Mathew D. Halls
Source: npj Computational Materials (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures Mixtures of chemical ingredients, such as formulations, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation. Using high-throughput classical molecular dynamics simulations, we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property: formulation descriptor aggregation (FDA), formulation graph (FG), and Set2Set-based method (FDS2S). Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties. Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing. The models show robust transferability to experimental datasets, accurately predicting properties across energy, pharmaceutical, and petroleum applications. Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties. Throughput ; Computer science ; High-throughput screening ; Artificial intelligence ; Machine learning ; Computer architecture ; Chemistry ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: H. Y. Mark Liao | Yen-Hsiang Lin | Chi‐How Peng | Yi‐Pei Li
Source: ACS Engineering Au (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Directed Message Passing Neural Networks for Accurate Prediction of Polymer–Solvent Interaction Parameters Accurate prediction of polymer–solvent interactions is essential for applications such as polymer processing, drug delivery, and membrane separations. The Flory–Huggins interaction parameter (χ parameter) serves as a key descriptor for polymer–solvent compatibility; however, its experimental determination is often costly and time-consuming. In this study, we develop a machine learning framework based on directed message passing neural networks (D-MPNNs) to predict χ parameters directly from molecular structures, temperature, and volume fraction. Our approach systematically evaluates different feature representations, pooling methods, and empirical equation integrations to optimize prediction accuracy. Among the tested models, D-MPNN-TC, which incorporates both temperature and volume fraction, demonstrates strong predictive performance (MAE = 0.092, RMSE = 0.162, and R2 = 0.926), outperforming descriptor-based models that rely on precomputed molecular fingerprints and handcrafted chemical features. Additionally, integrating the Flory–Huggins equation into the classification framework enables highly accurate miscibility predictions, with F1 scores of 0.915. Further analysis using t-SNE visualization reveals that D-MPNNs effectively capture key structural features, such as aromaticity and cyclic structures, that influence polymer–solvent interactions. Our findings highlight the advantages of graph-based molecular representations over traditional fingerprinting methods and underscore the importance of volume fraction information in predicting polymer–solvent compatibility. This study provides a scalable and interpretable framework for leveraging machine learning in polymer science, facilitating data-driven solvent selection and polymer design. Artificial neural network ; Computer science ; Message passing ; Artificial intelligence ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Abrar U. Hassan | Cihat Güleryüz | Islam H. El Azab | Ashraf Y. Elnaggar | M.H.H. Mahmoud
Source: Optical Materials (article, 2025-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest Affect (linguistics) ; Basis (linear algebra) ; Nonlinear optical ; Nonlinear system ; Psychology ; Optics ; Materials science ; Cognitive psychology ; Computer science ; Social psychology ; Physics ; Mathematics ; Communication ; Quantum mechanics Nonlinear Optical Materials Research ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mechanical and Optical Resonators
Authors: Kenji Konashi | Nobuhiko Kato | Kazuki Mori | Ken Kurosaki
Source: Journal of Nuclear Materials (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Neural network potential for molecular dynamics calculation of UO2 This study employed a machine learning approach to develop a neural network potential for molecular dynamics simulations of uranium dioxide (UO 2 ). The results of first-principles calculations were used as training data. The calculation results of the physical properties of UO 2 showed that this potential is widely applicable to the evaluation of physical properties. It is particularly effective for calculating thermophysical properties near the melting point, where experiments are difficult due to extremely high temperatures. The calculations of diffusion constant suggest that the melting of the oxygen sublattice in UO2 occurs at temperatures beyond 2600 K. Molecular dynamics ; Dynamics (music) ; Artificial neural network ; Statistical physics ; Biological system ; Computer science ; Computational biology ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Biology Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering