jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yihan Ye | Bing Bai | Yilun Ding | Xinzhe Li | Feng Jiao | Jianping Xiao | Xiulian Pan
Source: Angewandte Chemie (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Accelerated Oxide‐Zeolite Catalyst Design for Syngas Conversion by Reaction Phase Diagram Analysis and Machine Learning Abstract Oxide‐zeolite (OXZEO) catalyst design concept provides an alternative approach for the direct syngas‐to‐olefins (STO) with superior selectivity. Enhancing the activity of oxide components remains a critical and long‐pursued target in this field. However, rational design strategies for optimizing oxides and improving the catalyst performance in such complex reaction networks are still lacking. We employed energetic descriptors such as the adsorption energies of CO* and O* ( G ad CO* and G ad O*) through reaction phase diagram (RPD) analysis to predict the catalyst performance. The prediction was initially validated by the catalytic activity trends measured by experiments. Machine learning (ML) was further utilized to accelerate the screening of new catalysts. Ultimately, Bi‐doped and Sb‐doped ZnCrO x were theoretically predicted as optimized oxide candidates for the OXZEO reaction, which was experimentally verified to be more active than the currently best ZnCrO x counterpart. This work demonstrated enhanced OXZEO catalysts for STO as well as a research paradigm integrating theory and experiment to optimize bifunctional catalysts for complex reaction networks. Syngas ; Zeolite ; Catalysis ; Oxide ; Chemical engineering ; Phase diagram ; Phase (matter) ; Chemistry ; Materials science ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Machine Learning in Materials Science
Authors: Jinjin Dong | Wenjun Yang | Haolin Liu | J. Wu | Zongguo Wang
Source: ACS Applied Materials & Interfaces (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

A Generalizable Machine Learning Framework for Identifying Sustainable Multi-Ion Garnet Electrolytes Lithium-ion (Li-ion) solid-state batteries (SSBs) are highly regarded for their exceptional energy density and prolonged operational lifespan. However, concerns regarding their sustainability have arisen due to the uneven global distribution of Li resources and Li's relatively low abundance in the Earth's crust. Consequently, significant interest has shifted toward developing alternative SSBs, such as sodium (Na), magnesium (Mg) and Aluminum (Al)-ion batteries. A key challenge in this pursuit is efficiently identifying viable solid-state electrolytes (SEs) from the vast chemical space, particularly for Na and Mg ions. This study introduces a generalized framework based on machine learning for effectively screening high-performance garnet-type SEs. Utilizing specifically designed chemical descriptors, ML models predict the thermal stability and electrical conductivity of garnet-type SEs, achieving predictive accuracies of 94% and 89%, respectively. The chemical factors influencing stability and conductivity are identified and validated through interpretability analysis. Leveraging these models, 1764 garnet-type SEs exhibiting high thermal stability and wide band gaps were screened from a database of 43,732 compounds. Furthermore, 44 garnet-type SEs with favorable environmental and economic advantages were selected, and verified through first-principles calculations using density functional theory. Given their cost-effectiveness and high performance, these SEs hold great potential for application in Na, Mg, and Al ion SSBs. This study provides crucial insights into developing SSB materials, advances sustainable energy storage, and offers key perspectives for exploring material systems within specific space groups. Materials science ; Ion ; Machine learning ; Electrolyte ; Artificial intelligence ; Nanotechnology ; Computer science ; Physical chemistry Solid-state spectroscopy and crystallography ; Perovskite Materials and Applications ; Transition Metal Oxide Nanomaterials
Authors: Sanghoon Lee | Hyun Woo Kim
Source: Advanced Theory and Simulations (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Revealing the Impact of Aggregations in the Graph‐Based Molecular Machine Learning: Electrostatic Interaction Versus Pooling Methods Abstract Molecular structures that can be readily represented by graphs comprising constituent atoms (nodes) and their chemical bonds (edges) can also be used as input data for well‐known machine learning (ML) models that process this data, such as graph neural networks (GNNs). GNNs have shown a reasonable performance in the predicting properties of chemical systems. In typical applications of GNNs to chemistry‐related fields, the main objective is to create an optimal molecular representation by aggregating atomic features and pooling features in the graph. In this study, two different approaches are investigated that can possibly generate better molecular representations. First, intermolecular edges are created to predict the photochemical properties of chromophore molecules in the solution. These intermolecular edges are constructed using atomic partial charges, inspired from the fact that electrostatic interaction is the main component of solute‐solvent interaction. In the second approach, the effect of the aggregation and pooling functions is investigated. The results show that intermolecular electrostatic edges based on ground state charges prevent the GNN model from generating more effective molecular representations. On the contrary, the model demonstrated better performance when the averaging and adding operations are employed in a hybrid manner for the aggregation and pooling functions. Pooling ; Artificial intelligence ; Computer science ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Lin Luo | Qimin Liu | Junhao Sun | Yaosong Huang
Source: Chemical Engineering Science (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Exploring surface reaction mechanism using a surface reaction neural network framework Mechanism (biology) ; Surface (topology) ; Artificial neural network ; Reaction mechanism ; Chemistry ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Catalysis ; Mathematics ; Biology ; Organic chemistry ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Adhesion, Friction, and Surface Interactions
Authors: Liang Zhang | Yuxuan Wan | Yasushi Shibuta | Xiaoxu Huang
Source: Progress in Natural Science Materials International (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Progress in machine learning interatomic potential and its applications in materials science Interatomic potential ; Field (mathematics) ; Construct (python library) ; Molecular dynamics ; Computational model ; Experimental data ; Computational simulation Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Electron Microscopy Techniques and Applications
Authors: Chang Gao | M. Zhu | Chenglong Shi | Hongzhi Chen | Rubin Zhu | Hao Xu | Xufeng Dong | Zhanjun Wu
Source: Computational Materials Science (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Machine learning-aided reparameterization of a united atom model for chemically intricate polymer networks subjected to large tensile deformation Deformation (meteorology) ; Ultimate tensile strength ; Polymer ; Materials science ; Atom (system on chip) ; Composite material ; Computer science ; Parallel computing Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Jiaqi He | Rong Fei
Source: Journal of Physics Conference Series (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Prediction of molecularly imprinted polymer binding affinity based on graph neural networks Abstract Molecularly imprinted polymers (MIPs) have found widespread applications in fields such as molecular recognition, sensing, and drug delivery, due to their high selectivity and specificity. However, designing efficient MIPs and quickly determining their selectivity in complex environments is a time-consuming and resource-intensive task. In this study, five graph neural networks (GCN, GAT, Weave, MPNN, and AttentiveFP) were trained and evaluated on publicly available experimental datasets to assess their performance in predicting the properties of MIPs. Additionally, the impact of different task architectures on model performance was analyzed. The results demonstrate that AttentiveFP exhibits the best predictive capability, with R 2 and RMSE values of 0.67 and 6.27, respectively, under the single-task architecture. Furthermore, compared to the single-task architecture, the multi-task architecture can enhance model performance, with R 2 improving by up to 50%. Molecularly imprinted polymer ; Artificial neural network ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry ; Selectivity Analytical chemistry methods development ; Analytical Chemistry and Chromatography
Authors: Shiyang Ji | Chao Zhang | Zihan Zou | Bin Jiang | Jun Jiang | Yi Luo | Weijin Hu
Source: ArXiv.org (preprint, 2025-10-05)
Field: Chemistry — Physical and Theoretical Chemistry

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction Accurate and efficient simulation of infrared (IR) and Raman spectra is essential for molecular identification and structural analysis. Traditional quantum chemistry methods based on the harmonic approximation neglect anharmonicity and nuclear quantum effects, while ab initio molecular dynamics (AIMD) remains computationally expensive. Here, we integrate our deep equivariant tensor attention network (DetaNet) with a velocity-Verlet integrator to enable fast and accurate machine learning molecular dynamics (MLMD) simulations for spectral prediction. Trained on the QMe14S dataset containing energies, forces, dipole moments, and polarizabilities for 186,102 small organic molecules, DetaNet yields a universal and transferable force field with high-order tensor prediction capability. Using time-correlation functions derived from MLMD and ring-polymer molecular dynamics (RPMD) trajectories, we computed IR and Raman spectra that accurately reproduce anharmonic and nuclear quantum effects. Benchmark tests on isolated molecules, including polycyclic aromatic hydrocarbons, demonstrate that the DetaNet-based MD approach achieves near-experimental spectral accuracy with speedups up to three orders of magnitude over AIMD. Furthermore, the framework extends seamlessly to molecular and inorganic crystals, molecular aggregates, and biological macromolecules such as polypeptides with minimal fine-tuning. In all systems, DetaNet maintains high accuracy while significantly reducing computational cost. Overall, this work establishes a universal machine learning force field and tensor-aware MLMD framework that enable fast, accurate, and broadly applicable dynamic simulations and IR/Raman spectral predictions across diverse molecular and material systems. Anharmonicity ; Molecular dynamics ; Force field (fiction) ; Quantum ; Dipole ; Ab initio ; Tensor (intrinsic definition) ; Field (mathematics) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Microfluidic and Capillary Electrophoresis Applications
Authors: Linhan Yu | Shijie Jiang | Chenlong Hu | Yumin Wang | Liuyang Xu | Hu He | Yu Wang | Xuesen Du
Source: ACS Sustainable Chemistry & Engineering (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Machine Learning for Hydrogen Storage Properties of TiFe Alloys Based on Literature-Curated Data Set: Prediction and Limitation This study establishes an interpretable machine learning framework to elucidate the composition–property relationships of TiFe-based hydrogen storage alloys. Beyond elemental compositions, two physical descriptors (Size 1/Size 2) were first introduced. XGBoost performed superiorly in predicting hydrogen absorption and desorption plateau pressures. SHAP analysis revealed that Fe content and temperature are the principal variables affecting platform pressure, a result that is consistent with empirical observations. However, hydrogen storage capacity prediction showed limited accuracy In addition to the features utilized in the present study, there is a lack of supplementary descriptive features that can robustly characterize hydrogen storage capacity. Potentially relevant attributes may encompass phase composition, synthesis methodology, and chemical information on the material. The lack of incorporation of such data constrains the predictive performance and utility of machine learning data sets of TiFe alloys. This work provides actionable insights for alloy design while exposing inherent limitations of the Literature-curated Data set by coupling machine learning with experimental data. The convergence of interpretable AI and materials science demonstrates transformative potential to overcome longstanding challenges in hydrogen storage material development, particularly through hybrid data-physics modeling strategies. Hydrogen storage ; Principal component analysis ; Transformative learning ; Work (physics) ; Hydrogen ; Data set ; Set (abstract data type) ; Convergence (economics) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Dongbo Zhao | Yilin Zhao | Chunying Rong | He Zhang | Chang Liu | Shubin Liu
Source: Acta Physico-Chimica Sinica (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

From Knowledge Tree to Knowledge Forest: Harnessing Chemical Understanding with Machine Learning Transformative learning ; Tree (set theory) ; Dilemma ; Plural ; Learning theory ; Conceptual framework Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yingying Liu | Liang Gao | Zican Yang | Huatang Zhang | Jizhong Chen | Jiayuan Xu | Kai Yin
Source: Journal of Chemical Education (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Using Response Surface Methodology To Enhance Limited Data Sets for Machine Learning: A Polyvinyl Butyral Synthesis-Based Chemistry Undergraduate Teaching Case This paper presents a novel Course-Based Undergraduate Research Experience (CURE) that addresses a critical challenge in chemical education: teaching students to generate appropriate data for machine learning (ML) applications rather than relying on precurated data sets. Through polyvinyl butyral (PVB) synthesis experiments, 22 third-year chemistry students learned to integrate Response Surface Methodology (RSM) with Support Vector Regression (SVR) algorithms to overcome the inherent data scarcity challenge in undergraduate laboratories. The course guided students through a progressive four-module framework: (1) designing experiments using Central Composite Design principles, (2) conducting PVB synthesis with varied parameters and characterizing products, (3) developing RSM models to establish mathematical relationships between synthesis parameters and material properties, and (4) using these models to generate augmented data sets for ML training. Exit survey results indicate that students self-report significant improvements in their technical competencies, particularly in experimental design (45%, n = 10), machine learning fundamentals (61%, n = 13), and data processing skills (55%, n = 12). Student-developed hybrid RSM-ML models achieved strong predictive performance (R2 values range from 0.78 to 0.95 for testing data sets), significantly outperforming either methodology used independently. This integrated approach prepares students for modern research environments by developing both experimental and computational competencies. The framework is adaptable to other chemical systems. Response surface methodology ; Polyvinyl butyral ; Experimental data ; Design of experiments ; Range (aeronautics) ; Support vector machine ; Surface (topology) ; Data point Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Benjamin Poswell | Amanda S. Barnard
Source: ACS Nano (review, 2025-03-07)
Field: Materials Science — Materials Chemistry

Materials “Economatics”: Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning This Perspective discusses the application of advanced machine learning techniques to explore the latent relationships between the electrochemical performance and the environmental and socioeconomic impacts of modern nanomaterials fundamental to a carbon-neutral and sustainable future. Through the use of state-of-the-art algorithms, the aim is to make transparent the confluence of opaque factors that have resulted in the applications of nanomaterial research and development, for example, batteries, largely overlooking ecological and social consequences. We demonstrate how interpretable machine learning could uncover hidden patterns that inform more rational, holistic, and thus sustainable decision-making. By presenting a case study to explore relationships within a publicly available battery compound data set, we propose a framework that expands on existing methods, such as life cycle analysis and criticality assessments. This framework broadens the scope of nanomaterial understanding by incorporating increasingly holistic factors, while also enhancing scalability and explanatory capacity. Ultimately, using this approach, practitioners will be able to identify and analyze the fundamental barriers that are hindering the renewable energy transition, thus contributing to the future of sustainable nanomaterial research and development. Materials science ; Nanotechnology ; Business Machine Learning in Materials Science ; Recycling and Waste Management Techniques ; Extraction and Separation Processes
Authors: Haonan Wang | Qiulin Wang | Jiayi Zhao | Chenyang Liu | Jiaxin Feng | Jing Jin | Dunyu Liu
Source: Molecular Catalysis (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Machine learning-driven rapid screening and performance prediction of NH3-SCR catalyst • Constructed a comprehensive catalytic database with 751 datasets, including 30 input features and 2 output features (NO x conversion and N 2 selectivity). • CatBoost achieves optimal performance (R² = 0.999, MAE = 0.006, RMSE = 0.008) in predicting NH₃-SCR catalyst activity. • SHAP analysis reveals reaction temperature and Mn ratio as critical for NO ₓ conversion and N₂ selectivity. • Machine Learning-driven framework reduces trial-and-error costs, accelerating high-performance catalyst discovery. Traditional research and development of catalysts mainly rely on experimental trial-and-error methods, which struggle to meet the demand for efficient catalysts in the energy and environmental fields. Applying machine learning to catalyst design can achieve rapid screening of catalysts, thus overcoming the limitations of the traditional trial-and-error methods and solving the problems of low efficiency and high cost affecting catalyst research and development. This paper reports the development of a catalytic database containing 751 sets of data, with 30 input features divided into three categories, such as catalyst composition, preparation conditions, and catalytic reaction conditions, along with NO x conversion and N 2 selectivity serving as output features. A machine learning approach was used to assist in the formulation screening and performance prediction of selective catalytic reduction (SCR) catalysts. The Categorical boosting (CatBoost) model was found to exhibit the best performance among the 10 models explored, with R 2 , Mean absolute error (MAE), and Root mean square error (RMSE) values of 0.999, 0.006, and 0.008, respectively. The importance of each input feature for model prediction was obtained using the SHapley Additive exPlanations (SHAP) method, which showed that the reaction temperature and Mn elemental molar ratio had the greatest effect on NO x conversion and N 2 selectivity. This study provides guidance for the screening, reaction condition optimization, and performance prediction of SCR catalysts. The successful integration of machine learning with experimental methods represents an effective strategy that can accelerate the discovery of catalysts. Catalysis ; Chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Machine Learning and ELM
Authors: Jyothish Joy | Alex Kraus | Spencer Ricks | Daniel H. Ess
Source: Organometallics (article, 2025-07-05)
Field: Chemistry — Organic Chemistry

Pd–Methyl Bond Energy─Property Correlations, Noncorrelations, Machine Learning Models, and Application to Polymerization Catalysis Metal–carbon bonds are a key intermediate in a variety of homogeneous organometallic transformations and often determine the critical thermodynamics and kinetics of catalytic processes. Surprisingly, the influence of different ligands on metal–carbon bond strengths has been largely overlooked. Here we evaluated nearly 700 experimental Pd–methyl complexes by calculating their bond dissociation energies using density functional theory (DFT) and compared these bond strengths to several fundamental molecular properties, and this revealed several surprising correlations and noncorrelations. Most surprising was that several fundamental properties, such as the bond length, bond force constant, and bond electron density, have no correlation with bond strength, despite these correlations often holding for main-group compounds. We were indeed able to identify key ligand-dependent chemical features/descriptors that provided a highly accurate machine learning model and provided insight into the general factors that control the Pd–carbon bond strength, such as radical delocalization and nucleophilicity. Insights gained from the Pd–Me bond energy analysis were then applied to CO migratory insertion steps that are part of copolymerization reactions. Chemistry ; Catalysis ; Polymerization ; Bond energy ; Property (philosophy) ; Energy (signal processing) ; Computational chemistry ; Polymer chemistry ; Photochemistry ; Organic chemistry ; Molecule ; Polymer ; Quantum mechanics Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis