jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sher Ahmad | Daniël Emmery | Fausto Gallucci | J. van der Schaaf
Source: International Journal of Hydrogen Energy (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Neural network based molecular dynamics simulations for oxide ion transport in solid oxide electrolysis cell materials Solid oxide electrolysis cells (SOECs) based hydrogen production is regarded as one of the most efficient methods for sustainable energy conversion. Brownmillerite-type oxides, such as Ca 2 Fe 2 O 5 , have recently gained significant interest due to their inherent oxygen vacancy channels that facilitate oxide ion transport through the structure. In this research work, neural network-based interatomic potentials (ML-IAPs) were employed in molecular dynamics (MD) simulations to study the oxide ion transport in Co-doped brownmillerites. The simulation results and experimental data aligned within 98 % accuracy. Parametric analysis revealed that temperature, Co doping, and oxygen vacancies enhances oxide ionic conductivity in these materials. Co doping leads to a 2–3 fold increase in diffusion coefficient compared to undoped Ca 2 Fe 2 O 5 structure. From the trajectory analysis, oxygen pathways, and anisotropic nature of ionic diffusion in brownmillerites is observed. The findings of this research provide a strong data-driven framework for accelerating material selection strategies, paving the way for next-generation high-performance electrolysis technologies. • Neural network-based interatomic potentials (ML-IAPs) applied to oxide ion transport simulations. • Co doping in Ca 2 Fe 2 O 5 increases the diffusion coefficient by 2–3 compared to undoped structures. • Molecular dynamics results match experimental data with 98 % accuracy. • Trajectory analysis reveals anisotropic diffusion pathways in brownmillerites. • Demonstrates a computational framework for high-performance SOEC material discovery for hydrogen production. Oxide ; Electrolysis ; Diffusion ; Molecular dynamics ; Ionic conductivity ; Ionic bonding ; Oxygen transport ; Hydrogen Advancements in Solid Oxide Fuel Cells ; Nuclear Materials and Properties ; Fuel Cells and Related Materials
Authors: Anita Halder | Durga Paudyal | Stefano Sanvito | Martin Takáč | Huseyin Ucar
Source: Acta Materialia (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening We present a machine-learning-driven framework for discovering high-performance rare-earth-free hard magnetic materials integrating machine learning, a universal graph deep-learning interatomic potential, and density functional theory validation. Key contributions include the identification of FeCo-based ternary alloys with remarkable magnetic properties, such as uniaxial anisotropy constant, K 1 , Curie temperature, T C , and saturation magnetization, M S . Notable examples include Fe 6 CoB 2 and FeCo 5 B, which exhibit K 1 values of 1.76 MJ/m 3 and 1.00 MJ/m 3 , respectively, with M S above 1.3 T, and T C exceeding 600 K. These properties align with the needs of high-temperature and high-performance applications. The universal graph deep-learning interatomic potential M3GNet accelerates the structural relaxation process, enabling the efficient screening of 48,000 candidate structures, while density functional theory validates the top performers with energy product ( B H ) max reaching more than 600 kJ/m 3 . Our study highlights a scalable, efficient pipeline for advancing the discovery of permanent magnets, reducing reliance on rare-earth elements. • A machine learning pipeline predicts magnetic properties and structural stability of new hard magnets. • Accelerated discovery of FeCo-based ternary alloys with high magnetocrystalline anisotropy and Curie temperatures. Materials science ; Throughput ; Computation ; High-throughput screening ; Nanotechnology ; Computer science ; Algorithm ; Biology ; Bioinformatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Rasoul Salami | Tianlong Liu | Xue Han | Ying Zheng
Source: Advanced Powder Materials (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Machine learning application in thermal CO2 hydrogenation: catalyst design, process optimization, and mechanism insights The growing demand for carbon neutrality has heightened the focus on CO 2 hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels. Advanced machine learning (ML) approaches integrate materials science with artificial intelligence, enabling scientists to identify hidden patterns in datasets, make informed decisions, and reduce the need for labor-intensive, repetitive experimentation. This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO 2 . Following an introduction to ML tools and workflows, various ML algorithms employed in CO 2 hydrogenation are systematically categorized and reviewed. Next, the application of ML in catalyst discovery is discussed, highlighting its role in identifying optimal compositions and structures. Then, ML-driven strategies for process optimization, particularly in enhancing CO 2 conversion and product selectivity, are examined. Studies modeling descriptors, spanning catalyst properties and reaction conditions, to predict catalytic performance are analyzed. Consequently, ML-based mechanistic studies are reviewed to elucidate reaction pathways, identify key intermediates, and optimize catalyst performance. Finally, key challenges and future perspectives in leveraging ML for advancing CO 2 hydrogenation research are presented. Machine learning (ML) is transforming the landscape of thermal CO 2 hydrogenation by enabling data-driven catalyst discovery, process optimization, and mechanistic insight. This review highlights the integration of ML with thermocatalytic CO 2 conversion, covering algorithm selection, feature engineering, catalyst screening, and prediction of reaction outcomes. By bridging materials science and artificial intelligence, ML accelerates the development of high-performance catalysts and reveals complex structure–activity relationships, offering a powerful approach to achieve efficient and selective CO 2 utilization. Mechanism (biology) ; Process (computing) ; Catalysis ; Process engineering ; Computer science ; Biochemical engineering ; Chemical engineering ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysts for Methane Reforming
Authors: Kaifeng Zhang | Xudong Wang | Yanjing Su
Source: Materials Today Communications (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis Electrocatalyst ; Photocatalysis ; Materials science ; Nanotechnology ; Chemical engineering ; Catalysis ; Physical chemistry ; Electrochemistry ; Biology ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: David A. Egger | Manuel Grumet | Tomáš Bučko
Source: The Journal of Chemical Physics (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

Machine learning accelerates Raman computations from molecular dynamics for materials science Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often performed using the canonical harmonic approximation, which cannot capture certain thermal changes in the Raman response. Anharmonic vibrational effects were recently found to play crucial roles in several materials, which motivates theoretical treatments of the Raman effect beyond harmonic phonons. While Raman spectroscopy from molecular dynamics (MD-Raman) is a well-established approach that includes anharmonic vibrations and further relevant thermal effects, MD-Raman computations were long considered to be computationally too expensive for practical materials computations. In this perspective article, we highlight that recent advances in the context of machine learning have now dramatically accelerated the involved computational tasks without sacrificing accuracy or predictive power. These recent developments highlight the increasing importance of MD-Raman and related methods as versatile tools for theoretical prediction and characterization of molecules and materials. Anharmonicity ; Raman spectroscopy ; Context (archaeology) ; Computation ; Characterization (materials science) ; Thermal ; Molecular dynamics ; Perspective (graphical) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Crystallography and molecular interactions
Authors: Xingwang Zhao | Zhaomeng Liu | Lu‐Kang Zhao | Xuan‐Wen Gao | Tianzhen Ren | Wen Luo
Source: MetalMat (article, 2025-06-28)
Field: Materials Science — Materials Chemistry

Machine Learning Helps Data Mining to Build Descriptor Databases for Lithium‐Ion Batteries ABSTRACT The rapid development of computer science has made machine learning (ML)‐driven design a research hotspot in high‐performance lithium‐ion batteries. Descriptors play a critical role in ML processes, as accurate descriptors significantly improve prediction accuracy (achieving over 92% validation accuracy in density functional theory [DFT]‐calibrated models). Although open‐source databases offer rich material data, their operational complexity hinders effective utilization. This review highlights ML algorithms that streamline data extraction from these repositories, slashing experimental iterations by 75%–80%. Further analyze future challenges in descriptor acquisition for lithium‐ion batteries. This review is to provide insights into dataset construction and ML‐compatible descriptor generation, accelerating electrode material discovery from conventional 5–7 years to &lt; 18 months in recent cases. Lithium (medication) ; Computer science ; Database ; Ion ; Data mining ; Chemistry ; Psychology Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Mohamed Ezz | Ayman Mohamed Mostafa | Alaa Alaerjan | Hisham Allahem | Bader Aldughayfiq | Hassan M.A. Hassan | Rasha M.K. Mohamed
Source: Catalysts (article, 2025-04-13)
Field: Materials Science — Materials Chemistry

Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields This study focuses on optimizing C2 yields in the oxidative coupling of methane (OCM), a pivotal process for sustainable chemical production. By harnessing advanced machine learning (ML) techniques, this research aimed to predict C2 yields and identify the factors that drive catalytic performance. The Extra Trees Regressor emerged as the most effective model after a comprehensive evaluation across multiple datasets and methodologies. Key to the method was the use of an innovative Aggregated Catalyst Physicochemical Descriptor (ACPD) and stratified cross-validation, which effectively addressed feature complexity and target skewness. Hyperparameter optimization using Modified Sequential Model-Based Optimization (SMBO) further enhanced the model’s performance, achieving optimized R2 values of 61.7%, 75.9%, and 92.0% for datasets A, B, and C, respectively, with corresponding reductions in the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Additionally, SHAP (SHapley Additive exPlanations) analysis provided a detailed understanding of the model’s decision-making process, revealing the relative importance of individual features and their contributions to the predictive outcomes. This research not only achieved state-of-the-art predictive accuracy, but also deepened our understanding of the underlying chemical dynamics, offering practical guidance for catalyst design and operational optimization. These findings mark a significant advancement in catalysis, paving the way for future innovations in sustainable chemical manufacturing. Catalysis ; Oxidative coupling of methane ; Coupling (piping) ; Chemistry ; Chemical engineering ; Dynamics (music) ; Methane ; Biochemical engineering ; Computer science ; Process engineering ; Materials science ; Environmental science ; Engineering ; Organic chemistry ; Physics ; Composite material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Zhuo Zheng | Keyan Liu | Xiyuan Zhu
Source: Journal of Technology Innovation and Engineering (article, 2025-09-11)
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 ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Hyeongtag Jeon | Hyeonsu Kim | J. Kim | Young Jin Yoon | Myung Jun Kim | Jae Jeong Kim
Source: Electrochimica Acta (article, 2025-05-22)
Field: Chemistry — Electrochemistry

Extrapolability of machine learning for determining additive concentrations in copper electrolyte Copper ; Electrolyte ; Chemistry ; Materials science ; Metallurgy ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Electrodeposition and Electroless Coatings ; Ionic liquids properties and applications
Authors: Jacob Toney | Aaron Garrison | Weiliang Luo | R. Michel | Sukrit Mukhopadhyay | Heather J. Kulik
Source: Current Opinion in Chemical Engineering (article, 2025-10-05)
Field: Materials Science — Materials Chemistry

Exploring beyond experiment: generating high-quality datasets of transition metal complexes with quantum chemistry and machine learning Leverage (statistics) ; Benchmark (surveying) ; Artificial neural network ; Chemical space ; Electronic structure ; Experimental data ; Ab initio ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Pradeepta Kumar Sarangi | Nidhi Goel | Ashok Kumar Sahoo | Lekha Rani
Source: Macromolecular Symposia (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning in Polymer Science: Emerging Trends and Future Directions Abstract Artificial intelligence (AI) and machine learning (ML) have advanced tremendously in the previous 5 years regarding polymer science. Polymers are materials with enormous versatility that are now widely used. Polymers have found extensive applications in several fields such as energy storage, construction, medical, aerospace, and other industries. This study is presently in the era of the 4.0 industry, a transformative period that is profoundly reshaping both business and society in an unprecedented manner specifically in developing countries. Data‐driven strategies for process analysis and control are crucial in expediting the creation of polymer production processes while maintaining product quality and avoiding a rise in production cost. More and more scientists are utilizing polymer informatics and data science to create new materials and understand the connections between their molecular structure and characteristics. The field of polymer informatics is relatively new. Even though there are a lot of helpful databases and tools accessible, not many are used frequently. The application of AI is starting to have an influence on several aspects of human existence, including fields such as science and technology. Polymer informatics is a field that utilizes AI and ML techniques to enhance the process of developing, designing, and discovering polymers. Based on these ideas, it examines the burgeoning fields of ML‐assisted polymer informatics in this research. It also looks at these new developments in the polymeric informatics ecosystem and talks about upcoming potential and problems for applications. Polymer ; Materials science ; Engineering ethics ; Nanotechnology ; Engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Zhihong Hu | Jiayi Liu | Xuefei Li | Zongyu Huang | Xiang Qi | Wenjuan Du
Source: Chemistry (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Optical Characterization and Growth Modulation of Two-Dimensional Materials This review focuses on research on machine learning-enabled two-dimensional (2D) materials, exploring the progress and prospects of this interdisciplinary field. At a fundamental level, machine learning algorithms incorporate imaging systems to build highly accurate viewing frameworks for material analysis. Two-dimensional materials have a rich set of optical properties, including light absorption and emission, anisotropy, photoluminescence, and nonlinear optical effects, which machine learning can accurately understand through image characterization, spectral fusion, and quantitative analysis. Meanwhile, the preparation process and post-processing are key aspects in the growth regulation of 2D materials, and machine learning helps optimize the experiments by analyzing the growth kinetics for fine control. Related research has spawned many academic achievements, gradually penetrating electronics, energy, and other industrial applications. The innovation of imaging technology and the deepening of multidisciplinary integration are expected to unlock more emerging applications and expand the application boundaries of 2D materials. Characterization (materials science) ; Materials science ; Modulation (music) ; Optoelectronics ; Computer science ; Nanotechnology ; Physics ; Acoustics 2D Materials and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Krittapong Deshsorn | Panwad Chavalekvirat | Somrudee Deepaisarn | Ho-Chiao Chuang | Pawin Iamprasertkun
Source: ACS Materials Au (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications Machine learning transforms the landscape of 2D materials design, particularly in accelerating discovery, optimization, and screening processes. This review has delved into the historical and ongoing integration of machine learning in 2D materials for electrochemical energy applications, using the Knowledge Discovery in Databases (KDD) approach to guide the research through data mining from the Scopus database using analysis of citations, keywords, and trends. The topics will first focus on a "macro" scope, where hundreds of literature reports are computer analyzed for key insights, such as year analysis, publication origin, and word co-occurrence using heat maps and network graphs. Afterward, the focus will be narrowed down into a more specific "micro" scope obtained from the "macro" overview, which is intended to dive deep into machine learning usage. From the gathered insights, this work highlights how machine learning, density functional theory (DFT), and traditional experimentation are jointly advancing the field of materials science. Overall, the resulting review offers a comprehensive analysis, touching on essential applications such as batteries, fuel cells, supercapacitors, and synthesis processes while showcasing machine learning techniques that enhance the identification of critical material properties. Data science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Javad Tamnanloo | Abdol Hadi Mokarizadeh | Farzad Toiserkani | Hansini Abeysinghe | Abraham Joy | Mesfin Tsige
Source: Macromolecules (article, 2025-04-13)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Surface Tension Prediction of Polymers: A Comparative Analysis with Descriptor-Based Models Surface tension is a critical property that influences polymer behavior at interfaces and affects applications ranging from coatings to biomedical devices. Traditional experimental methods for measuring polymer surface tension are time-consuming, costly, and sensitive to environmental conditions. Computational approaches such as molecular dynamics (MD) simulations are valuable but computationally intensive, especially for polymers with long chains. This study investigates the use of machine learning (ML) techniques to predict polymer surface tension using different levels of molecular representation, focusing on multilinear regression (MLR), random forest (RF), and graph neural networks (GNNs). A data set of 317 homopolymers collected from the PolyInfo database is used to train and evaluate these models. Descriptors are derived at various levels of complexity, ranging from manually calculated features to graph-based representations. The GNN approach captures the intrinsic connectivity of polymer structures, while the MLR and RF models rely on manually crafted descriptors. The performance of these models is compared with experimental data, with the GNN model demonstrating superior accuracy due to its ability to directly learn from molecular graphs. Our results show that GNNs can better capture complex nonlinear relationships in polymer structures than traditional descriptor-based methods, suggesting their significant potential for accelerating polymer design and development. The study also includes validation of model predictions against molecular dynamics simulations, highlighting the potential of GNNs to accurately model polymer interfacial properties. Surface tension ; Polymer ; Artificial neural network ; Graph ; Computer science ; Materials science ; Artificial intelligence ; Polymer science ; Biological system ; Theoretical computer science ; Composite material ; Thermodynamics ; Physics ; Biology Machine Learning in Materials Science ; Adhesion, Friction, and Surface Interactions ; Advanced Sensor and Energy Harvesting Materials