jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Hyper-Predictive Catalysis: Utilizing Graph Neural Networks for Accelerated Screening of Supported Metal Oxide Catalysts in Selective Oxygen Evolution Reactions. **Abstract:** Selective oxygen evolution reaction (SOER) catalysts are pivotal for sustainable energy technologies, yet traditional discovery methods remain slow and resource-intensive. This paper introduces a novel framework leveraging Graph Neural Networks (GNNs) and variational autoencoders (VAEs) to predict catalytic activity for supported metal oxide (SMO) catalysts with unparalleled speed and accuracy. We demonstrate a 10x acceleration in catalyst screening and identification of novel SMO compositions exhibiting enhanced SOER performance compared to existing materials. Our method synergistically combines high-throughput computational screening, GNN-based structure-property relationship modeling, and a Bayesian optimization loop for accelerated material discovery, offering a transformative approach within the 선택적 산소 발생 반응 촉매 설계 field and enabling rapid development of next-generation SOER catalysts. Catalysis ; Oxide ; Artificial neural network ; Sustainable energy ; Identification (biology) ; Oxygen evolution ; Graph ; Field (mathematics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Tianqi Xu
Source: Theoretical and Natural Science (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Research Progress on the Integration of Molecular Dynamics and Machine Learning in 3xxx Series Aluminum Alloys With the increasing demand for high-performance materials in transportation, construction, and electronics industries, the need for efficient and precise alloy design has become more critical. This study focuses on the integration of molecular dynamics (MD) simulations and machine learning (ML) techniques in the research and optimization of 3xxx series aluminum alloys. The primary objective is to explore how the combination of these two methods can enhance the understanding of micro-mechanisms and accelerate alloy design processes. The research adopts a comprehensive literature review approach, analyzing existing experimental studies, simulation results, and machine learning applications related to 3xxx series alloys. Key tools include molecular dynamics simulation for atomic-scale behavior analysis and machine learning models for property prediction and optimization. The study finds that integrating MD and ML significantly improves the efficiency of data analysis, performance prediction, and microstructure control in 3xxx series alloys. It concludes that the MD-ML hybrid approach offers a promising path to address traditional limitations in alloy research and paves the way for the intelligent design of advanced aluminum materials. Computer science ; Series (stratigraphy) ; Alloy ; Industrial engineering ; Molecular dynamics ; Artificial intelligence ; Machine learning ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Chemistry Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Shunya Sudo (22538213) | Madoka Takai (1526308) | Tsukuru Masuda (1487413)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of SurfaceFree Energy of Polymer Surfacesby Machine Learning Modeling of Chemical Structure–PropertyRelationships The wettability of liquids on polymer surfaces is an important issue in basic science and applications, and the ability of polymers to repel both water and oil with low surface free energy is considered useful for developing antifouling low-adhesion surfaces. Understanding the quantitative structure–property relationship for the wettability of polymer materials is essential for designing such polymer surfaces. Machine learning modeling can quantitatively analyze factors strongly correlated to the target properties of materials. In this study, a machine learning model for contact angle values for water and diiodomethane and surface free energy is presented to clarify the quantitative structure–property relationship for the wettability. This strategy not only predicts contact angles of the two types of liquids but also makes surface free energy predictable as a thermodynamic parameter from the molecular structure of polymers, where the surface free energy can be calculated using a theoretical model, the Owens–Wendt equation. A series of polymer brush surfaces with diverse chemical structures, including polymers with alkyl, silicone-like, and fluorinated side chains, were synthesized by surface-initiated atom transfer radical polymerization, and their wettability and surface free energies were evaluated. A data set was constructed using values for 72 types of polymer brush surfaces and bulk polymers collected from both our experiments and literature sources. The decision tree-based models were suitable to predict the water contact angle, diiodomethane contact angle, and surface free energy. The trained decision tree-based models were interpreted using Shapley additive explanation analysis and utilized to simulate the effect of the number of CH<sub>2</sub> and CF<sub>2</sub> groups in the side chains of hypothetical polymers. This machine learning model is expected to aid in designing water- and oil-repellent polymers and help with directly predicting the surface free energy from the chemical structure. Diiodomethane ; Wetting ; Polymer ; Contact angle ; Surface energy ; Gibbs free energy ; Surface (topology) Surface Modification and Superhydrophobicity ; Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Shuwen Zhang | Ya Gao | Han Wang | John Z. H. Zhang | Tong Zhu
Source: ChemRxiv (preprint, 2025-02-17)
Field: Materials Science — Materials Chemistry

A Deep Learning-Augmented Density FunctionalFramework for Reaction Modeling with ChemicalAccuracy Accurate prediction of reaction energetics remains a fundamental challenge in computational chemistry, as conventional density functional theory (DFT) often fails to reconcile high accuracy with computational effi ciency. Here, we introduce Deep post-Hartree-Fock (DeePHF), a machine learning framework that synergistically integrates neural networks with quantum mechanical descriptors to achieve CCSD(T)-level precision while retaining the efficiency of DFT. By establishing a direct mapping between the eigenvalues of local density matrices and high-level correlation energies, DeePHF circumvents the traditional accuracy-scalability trade-off . Trained on a limited dataset of small-molecule reactions, our method demonstrates unprecedented performance across multiple benchmark datasets, exhibiting exceptional transferability. In fact, its accuracy even surpasses that of advanced double-hybrid functionals, all while maintaining O(N^3) scaling. DeePHF offers a promising pathway to bridge the gap between high-level quantum chemistry methods and the practical demands for scalable, accurate models in computational chemistry, and with further refi nement, it is poised to make signifi cant contributions to the advancement of chemical reaction modeling. Computer science ; Deep learning ; Artificial intelligence Machine Learning in Materials Science
Authors: Alex K. Chew | Mohammad Atif Faiz Afzal | Zach Kaplan | Eric M. Collins | Suraj Gattani | Mayank Misra | Anand Chandrasekaran | Karl Leswing | Mathew D. Halls
Source: ChemRxiv (preprint, 2025-01-07)
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 ; Computer architecture ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Y.S. Wudil
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Exploring the Chemical Space for Zinc-Ion Battery Cathode Discovery via Machine Learning and High-Throughput Screening In this work, we present a machine-learning framework for discovering zinc-ion battery cathode materials by screening a dataset of 6,858 zinc-containing compounds. Leveraging the extensive Materials Project (MP) database, we employed a two-step machine learning (ML) approach that integrates transfer learning to address missing electrochemical properties. First, a random forest regressor was used to impute missing features in zinc compounds, utilizing the full battery explorer dataset for enhanced predictions. We then developed two hybrid models: the Sparrow Search Algorithm-Light Gradient Boosting Machine (SSA-LGBM) and the Harris Hawk Optimization-Deep Neural Network (HHO-DNN). The dataset comprises 107 feature vectors, which were reduced through principal component analysis (PCA) to enhance model efficiency. These features encompass structural, chemical, and electronic descriptors. Both models were trained on 4,351 known battery compounds from MP to predict key electrochemical properties, including average voltage and gravimetric capacity. Our initial screening identified 62 potential cathode candidates, which were further refined based on voltage, specific capacity, electronic conductivity, stability, safety, cost, and elemental abundance. Ultimately, 18 promising cathode materials were identified. Model validation was performed by testing predictions against known cathode materials, demonstrating the effectiveness of our approach. Random forest ; Gradient boosting ; Battery (electricity) ; Boosting (machine learning) ; Artificial neural network ; Chemical space ; Cathode ; Missing data ; Principal component analysis Machine Learning in Materials Science ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Sk Mujaffar Hossain | Namitha Anna Koshi | Seung‐Cheol Lee | G. P. Das | Satadeep Bhattacharjee
Source: ArXiv.org (preprint, 2025-03-17)
Field: Materials Science — Materials Chemistry

Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode materials. In this work, we present a deep neural network (DNN) model, built using PyTorch, to estimate the average voltage of cathode materials across Li-ion, Na-ion, and other alkali-metal-ion batteries. The model is trained on an extensive dataset from the Materials Project, incorporating a wide range of specific structural, physical, chemical, electronic, thermodynamic, and battery descriptors, ensuring a comprehensive representation of material properties. Our model exhibits strong predictive performance, as corroborated by first-principles density functional theory (DFT) calculations. The close alignment between the DNN predictions and the DFT outcomes highlights the robustness and accuracy of our machine learning framework to effectively select and identify viable battery materials. Using this validated model, we successfully proposed novel Na-ion battery compositions, with their predicted behavior confirmed by rigorous computational assessment. By seamlessly integrating data-driven prediction with first-principles validation, this study presents an effective framework that significantly accelerates the discovery and optimization of advanced battery materials, contributing to the development of more reliable and efficient energy storage technologies. Battery (electricity) ; Robustness (evolution) ; Voltage ; Artificial neural network ; Range (aeronautics) ; Representation (politics) ; Energy storage ; Deep learning Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Brianna R. Farris | Kevin C. Leonard
Source: JACS Au (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Accelerating Catalysis Understanding via Large Language Model Data Extraction and Shallow Machine Learning Techniques Catalysis is inherently complex. The lack of precise knowledge available to experimental researchers about the microenvironment, catalytic sites, mechanisms, and changes that occur under reaction conditions has hindered the effectiveness of deep-learning artificial intelligence algorithms to predict catalyst behavior under reaction conditions. Given the type and quality of data available in the scientific literature, there are still open questions on how machine learning can be used by experimentalists working in the field of catalysis to accelerate catalyst design. Here, we present a framework that leverages large language models to extract textual data from known and trusted sources to automatically generate large, but relatively low-fidelity, experimental catalysis data sets across many research groups. We also show that instead of using deep-learning models, which require high-quality data, shallow learning models with posthoc interpretability can extract valuable information about experimental catalytic systems from these low-fidelity data sets. The innovation of this work lies not in the model development but in the prompt engineering, data encoding, and question architectures employed to extract meaningful information. We applied this framework to two different model reactions: the electrocatalytic reduction of carbon dioxide and the electrocatalytic oxygen reduction reaction. We showcase that this framework has the ability to uncover known and established facts within the catalysis community, such as the catalytic properties of Cu, as well as novel insights, including the critical role of voltages above a certain threshold in producing multicarbon products from CO<sub>2</sub>. We anticipate that this proposed framework will serve as an entryway for experimental catalytic researchers to utilize machine learning to rapidly process literature, generate novel hypotheses, and design experiments to accelerate catalyst development. Interpretability ; Catalysis ; Field (mathematics) ; Quality (philosophy) ; Experimental data ; Work (physics) Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Yanuar - Abimanyu | Rangga Ega Santoso | Revaldy Bisma Saylendra | Sri Astutik | Habibah Khusna Baihaqi
Source: Journal of Aceh Physics Society (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Development of Semiconductor Electrical Conductivity Prediction System through Integration of Machine Learning and Solid State Physics Theory for Electronic Material Design Machine learning has rapidly emerged as an effective system in various fields, including materials science, due to its ability to efficiently uncover complex patterns from big data. This research aims to develop a machine learning-based semiconductor electrical conductivity prediction system integrated with solid state physics theory. In the context of electronic material technology development, the ability to predict the electrical properties of materials before the synthesis process is crucial for improving efficiency and accuracy in material design. This study employs a qualitative descriptive method to create a machine learning-based system and qualitatively describe the research results. The dataset is obtained from various literature and previous experiments, with physical parameters such as crystal structure, band gap, and density. The machine learning model used is the random forest algorithm. Evaluation results show that the system can classify materials based on their electrical conductivity properties with high accuracy. Materials such as GaAs, Ge, and Si are identified as potential candidates for electronic device design. The integration of solid-state physics theory into this system enhances the physical and statistical validity of predictions. This research demonstrates that a machine learning approach based on physical principles can be an effective solution for future semiconductor material engineering. Semiconductor ; Solid-state ; Electronics ; Electrical resistivity and conductivity ; Engineering physics ; State (computer science) ; Materials science ; Mechanical engineering ; Computer science ; Engineering ; Electrical engineering ; Optoelectronics Machine Learning in Materials Science
Authors: Michael S. Chen | Alan Robledo | C. Schäfer | Katherine Y. Han | Cecilia Clementi | Mark E. Tuckerman
Source: The Journal of Chemical Physics (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange-correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. In order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four. Molecular dynamics ; Multiscale modeling ; Benchmark (surveying) ; Electrolyte ; Path integral formulation ; Proton ; Charge (physics) ; Density functional theory Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Quantum, superfluid, helium dynamics
Authors: Gbolagade Olajide | Khagendra Baral | Sophia Ezendu | Ademola Soyemi | Tibor Szilvási
Source: ChemRxiv (preprint, 2025-02-24)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis Heterogeneous catalysts are crucial in modern societies as they promote sustainability by enabling lower-energy pathways for various chemical reactions. While Density Functional Theory (DFT) computations can provide critical insights into how heterogeneous catalysts operate at the atomic level, they are limited by computational costs and unfavorable scaling with system size. Recently, machine learning interatomic potentials (MLIPs) have emerged as a promising alternative to DFT, offering near-DFT accuracy at significantly reduced cost. In this perspective, we discuss the application of MLIPs in heterogeneous catalyst modeling as a surrogate for DFT. We detail how MLIPs have been applied in thermal catalysis to probe active sites, enable studying complex metallic and nanoporous catalysts, and investigate the reconstruction of catalytic surfaces. We review the use of MLIPs in electrocatalysis and photocatalysis, emphasizing their capabilities in studying transition metal oxide surfaces and solid-liquid interfaces. We also discuss the current limitations of MLIPs, particularly their challenges with transferability and description of non-local interactions. Finally, we conclude by identifying promising and underexplored domains in which MLIPs can further advance our understanding of heterogeneous catalysts. Interatomic potential ; Computer science ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science
Authors: Juan Li | Haizhou Xue | Zhijun Zhang | Zhen Chang | Fuling Tang | Jiangnan Ren | Xuefeng Lu | Jiawen Li
Source: Chinese Rare Earths (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Deep-learning Potential Molecular Dynamics Simulations of the Mechanical Properties of Al-Sc Alloys The deep potential (DP) model based on high-throughput first-principles calculation is expected to solve the problem of potential function scarcity in the molecular dynamics simulation of rare earth aluminium alloys. Firstly, the Al-Sc binary potential function was trained by deep-learning method, and the accuracy of the DP potential function was tested by comparing the interatomic force, energy, energy-volume curve and elastic modulus of DP and DFT. The results show that the interatomic force, energy, energy-volume curve and elastic modulus of precipitated phase predicted by DP are very close to the first-principles calculation results, which proves that the DP potential function developed in this paper has high accuracy. On this basis, the DP potential function was used to study the uniaxial tensile deformation of aluminium single-crystal alloys with different Sc contents. It is found that Al-0.1%Sc alloy has the highest tensile strength, which will provide a reference for the composition optimization of high-performance rare earth Al-Sc alloys. Interatomic potential ; Molecular dynamics ; Aluminium ; Deformation (meteorology) ; Elastic modulus ; Function (biology) ; Embedded atom model ; Bulk modulus ; Ultimate tensile strength Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties