jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Omid Tarkhaneh | Sharene D. Bungay | Robert C. Mawhinney | Raymond A. Poirier
Source: Journal of Chemical Theory and Computation (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

ReMLP-NET: A Neural Network Interaction Potential for Molecular Energy Prediction Deep learning methods have seen increased applications and are now regularly used in chemistry, biology, and related areas. In chemical applications, they are used as an alternative to quantum mechanics (QM) methods to predict molecular energy with a more reasonable computational time. Here, we utilize a machine learning algorithm to predict the molecular total energy of structures containing elements H, C, N, O, F, S, and Cl. Optimized structures and total energies from the Retrievium repository, which includes molecules from the GDB13 and DUD-E sets, were used to train and assess a single Retrievium multilayer perceptron neural network (ReMLP-NET) using an atomic environment vector as the feature set. Symmetry function hyperparameters were selected using a Genetic Algorithm. The proposed method showed improved performance with MAE and root mean squared error values of 1.29 and 1.81 kcal/mol, respectively, compared to 1.53 and 2.16 kcal/mol for ReANI-2x. Hyperparameter ; Artificial neural network ; Energy (signal processing) ; Feature (linguistics) ; Mean squared error ; Perceptron ; Multilayer perceptron ; Function (biology) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gbolagade Olajide | Khagendra Baral | Sophia Ezendu | Ademola Soyemi | Tibor Szilvási
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis Interatomic potential ; Catalysis ; Computer science ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Omid Tarkhaneh (10207980) | Sharene D. Bungay (22227368) | Robert C. Mawhinney (2145853) | Raymond A. Poirier (1617958)
Source: Figshare (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

ReMLP-NET: A Neural Network Interaction Potentialfor Molecular Energy Prediction Deep learning methods have seen increased applications and are now regularly used in chemistry, biology, and related areas. In chemical applications, they are used as an alternative to quantum mechanics (QM) methods to predict molecular energy with a more reasonable computational time. Here, we utilize a machine learning algorithm to predict the molecular total energy of structures containing elements H, C, N, O, F, S, and Cl. Optimized structures and total energies from the Retrievium repository, which includes molecules from the GDB13 and DUD-E sets, were used to train and assess a single Retrievium multilayer perceptron neural network (ReMLP-NET) using an atomic environment vector as the feature set. Symmetry function hyperparameters were selected using a Genetic Algorithm. The proposed method showed improved performance with MAE and root mean squared error values of 1.29 and 1.81 kcal/mol, respectively, compared to 1.53 and 2.16 kcal/mol for ReANI-2x. Artificial neural network ; Hyperparameter ; Energy (signal processing) ; Feature (linguistics) ; Mean squared error ; Perceptron ; Function (biology) ; Multilayer perceptron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graph theory and applications
Authors: Natasya Putri Amalia | Adina, Zakia | Setiawan, Hendri | Yolanda, Naira Agustin | Farhana, Nabila Nazyu
Source: Open Science Framework (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

INVESTIGATION OF FORMULAS AND SURFACE MODELLING OF NITROGEN USING COMPUTATIONAL CHEMISTRY AND MACHINE LEARNING This study aims to explore the molecular formulas of nitrogen and its surface modeling through modern computational approaches, in order to understand electronic properties, interaction energies, and potential applications in sustainable materials. The main focus is to establish a connection between computational chemistry data and machine learning–based predictions, resulting in accurate and efficient models capable of handling the complexity of nitrogen molecular systems. The methodology involves Density Functional Theory (DFT) calculations to analyze the electronic structure and bonding energies of various nitrogen configurations, followed by surface modeling simulations to investigate stability and molecular interactions. Computational data are then employed as the training basis for machine learning algorithms, including multiple linear regression and artificial neural networks. Validation is carried out by comparing surface energy values and structural parameters with literature data and model predictions. The results demonstrate that integrating computational chemistry with machine learning significantly accelerates the analysis of nitrogen interactions. The predictive models achieve high accuracy in estimating surface energies with a relative deviation of less than 5%. Furthermore, the modeling successfully reveals the most stable orientation of nitrogen molecules, providing valuable insights for catalytic material design and future clean energy applications. Stability (learning theory) ; Computational model ; Surface (topology) ; Nitrogen ; Basis (linear algebra) ; Computational learning theory ; Linear regression ; Artificial neural network ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Zhiqiang Wan | Sisi Lao | Weifeng Zhang | Qiuju Zhang | Zhong‐Qun Tian
Source: Chinese Chemical Letters (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Convolutional neural network enabled prediction of surface features in CuAgAu alloys Convolutional neural network ; Ternary operation ; Alloy ; Artificial neural network ; Surface (topology) ; Electronic structure ; Representation (politics) ; Binary number ; Work (physics) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Welding Techniques and Residual Stresses
Authors: Khalid Ferji
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

PolySet: Restoring the Statistical Ensemble Nature of Polymers for Machine Learning Machine-learning (ML) models in polymer science typically treat a polymer as a single, perfectly defined molecular graph, even though real materials consist of stochastic ensembles of chains with distributed lengths. This mismatch between physical reality and digital representation limits the ability of current models to capture polymer behaviour. Here we introduce PolySet, a framework that represents a polymer as a finite, weighted ensemble of chains sampled from an assumed molar-mass distribution. This ensemble-based encoding is independent of chemical detail, compatible with any molecular representation and illustrated here in the homopolymer case using a minimal language model. We show that PolySet retains higher-order distributional moments (such as Mz, Mz+1), enabling ML models to learn tail-sensitive properties with greatly improved stability and accuracy. By explicitly acknowledging the statistical nature of polymer matter, PolySet establishes a physically grounded foundation for future polymer machine learning, naturally extensible to copolymers, block architectures, and other complex topologies. Representation (politics) ; Stability (learning theory) ; Polymer ; Block (permutation group theory) ; Encoding (memory) ; Statistical learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Kuangbiao Liao | Chonghuan Zhang | Ming Zeng | Qianghua Lin
Source: Research Square (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Reusability report: A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning Reusability ; Benchmark (surveying) ; Deep learning ; Representation (politics) ; Encoder ; External Data Representation ; Performance prediction ; Stability (learning theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Benjamin C. Koenig | Sili Deng
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. However, standard CRNNs cannot represent pressure-dependent rate behavior, which is critical in many combustion and chemical systems and typically requires empirical formulations such as Troe or PLOG. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs) that generalize CRNNs by modeling each kinetic parameter as a learnable function of system pressure using Kolmogorov-Arnold activations. This structure maintains full interpretability and physical consistency while enabling assumption-free inference of pressure effects directly from data. A proof-of-concept study on the CH3 recombination reaction demonstrates that KA-CRNNs accurately reproduce pressure-dependent kinetics across a range of temperatures and pressures, outperforming conventional interpolative models. The framework establishes a foundation for data-driven discovery of extended kinetic behaviors in complex reacting systems, advancing interpretable and physics-consistent approaches for chemical model inference. Interpretability ; Artificial neural network ; Chemical kinetics ; Arrhenius equation ; Range (aeronautics) ; Kinetic energy ; Chemical reaction ; Consistency (knowledge bases) ; Combustion Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Siyuan Zeng | Kuanping Gong | Yongquan Jiang | Yan Yang
Source: Artificial Intelligence Chemistry (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

A method for predicting molecular point group based on graph neural networks Molecular symmetry is fundamental to understanding molecular properties, designing functional materials, and optimizing chemical structures. Traditional symmetry determination methods, based on mathematical and rule-based approaches, are often limited by high computational cost and low efficiency. At present, deep learning methods predicting molecular 3D conformations from 2D structures also neglect molecular symmetry and point group considerations. To address these challenges, we propose a novel task: predicting the point group of a molecule's most stable 3D conformation using only its 2D topological graph, thereby enabling symmetry-aware conformation prediction. We adopt Graph Neural Networks (GNNs) to learn from molecular graph structures, and evaluate several GNN variants on this task. Among them, the Graph Isomorphism Network (GIN) achieves the highest accuracy by effectively capturing both local connectivity and global structural information. Experiments on the QM9 dataset show that our method achieves 92.7% accuracy and an F1-score of 0.924 on the test set, significantly surpassing both traditional approaches and other GNN-based methods. This work demonstrates the potential of deep learning in automated, efficient, and accurate molecular symmetry prediction, providing a valuable tool for future research in computational chemistry and material science. Molecular graph ; Graph isomorphism ; Graph ; Molecular symmetry ; Artificial neural network ; Symmetry (geometry) ; Deep learning ; Group (periodic table) ; Point (geometry) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Manajit Das | Ajnabiul Hoque | Mayank Baranwal | Raghavan B. Sunoj
Source: ArXiv.org (preprint, 2025-09-19)
Field: Materials Science — Materials Chemistry

DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction Prediction of complete step-by-step chemical reaction mechanisms (CRMs) remains a major challenge. Whereas the traditional approaches in CRM tasks rely on expert-driven experiments or costly quantum chemical computations, contemporary deep learning (DL) alternatives ignore key intermediates and mechanistic steps and often suffer from hallucinations. We present DeepMech, an interpretable graph-based DL framework employing atom- and bond-level attention, guided by generalized templates of mechanistic operations (TMOps), to generate CRMs. Trained on our curated ReactMech dataset (~30K CRMs with 100K atom-mapped and mass-balanced elementary steps), DeepMech achieves 98.98+/-0.12% accuracy in predicting elementary steps and 95.94+/-0.21% in complete CRM tasks, besides maintaining high fidelity even in out-of-distribution scenarios as well as in predicting side and/or byproducts. Extension to multistep CRMs relevant to prebiotic chemistry, demonstrates the ability of DeepMech in effectively reconstructing 2 pathways from simple primordial substrates to complex biomolecules such as serine and aldopentose. Attention analysis identifies reactive atoms/bonds in line with chemical intuition, rendering our model interpretable and suitable for reaction design. Fidelity ; Rendering (computer graphics) ; Quantum chemical ; Interpretability ; Template ; Mechanism (biology) ; Deep learning ; High fidelity Machine Learning in Materials Science
Authors: D.S. Novak | A.O. Moshenskyi | M.L. Sukalo | V.I. Pylypenko | S.M. Lisovets
Source: Scientific notes of Taurida National V I Vernadsky University Series Technical Sciences (article, 2025-01-01)
Field: Materials Science — General Materials Science

MODELS AND METHODS FOR POLYMER COMPOSITE MATERIALS MICROSCOPY PROCESSING USING MODERN MACHINE LEARNING TECHNOLOGIES The article presents a comprehensive investigation of automated processing methods for microscopic images of polymer composite materials utilizing modern machine learning technologies and deep neural networks.The research addresses fundamental problems inherent in traditional approaches to composite microstructure analysis, which are characterized by substantial labor intensity and pronounced subjectivity of results.A novel convolutional neural network architecture for the segmentation of structural elements in composite materials is proposed, combining attention mechanisms with multi-scale image analysis capabilities.The developed polymer matrix defect classification model employs an ensemble approach that integrates Random Forest methods, Gradient Boosting algorithms, and deep neural networks to achieve robust performance.A comprehensive system for automatic determination of filler distribution within composite structures has been created, utilizing semantic segmentation algorithms including U-Net and DeepLab architectures.Extensive experimental studies were conducted on a dataset comprising microscopic images of various composite materials, obtained through optical microscopy techniques.The experimental results demonstrate that the proposed approach achieves segmentation accuracy at 94%, representing a 12% improvement over existing methodologies.Specialized software with an intuitive graphical user interface has been developed for automated microstructure analysis applications.Implementation of the proposed system enables a reduction of microscopic image analysis time by approximately tenfold compared to manual processing procedures, while simultaneously ensuring objectivity and reproducibility of analytical results.The obtained results possess significant practical importance for quality control of composite materials throughout their production processes and for investigating fundamental relationships between microstructure characteristics and macroscopic properties of composites. Microscopy ; Composite number ; Polymer ; Machine vision Material Properties and Applications ; Polymer Science and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Killian Sheriff | Daniel Xiao | Yifan Cao | Lewis R. Owen | Rodrigo Freitas
Source: ArXiv.org (preprint, 2025-06-14)
Field: Materials Science — Materials Chemistry

Machine learning potentials for modeling alloys across compositions Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered (i.e., stoichiometric compounds) to completely disordered (i.e., solid solutions). Accurately capturing this range of chemical arrangements remains a major challenge, limiting the predictive accuracy of machine learning potentials (MLPs) in materials modeling. Here, we combine information theory and machine learning to optimize the sampling of chemical motifs and design MLPs that effectively capture the behavior of metallic alloys across their entire compositional and structural landscape. The effectiveness of this approach is demonstrated by predicting the compositional dependence of various material properties - including stacking-fault energies, short-range order, heat capacities, and phase diagrams - for the AuPt and CuAu binary alloys, the ternary CrCoNi, and the TiTaVW high-entropy alloy. Extensive comparison against experimental data demonstrates the robustness of this approach in enabling materials modeling with high physical fidelity. Ternary operation ; Robustness (evolution) ; Binary number ; Limiting ; Experimental data ; Phase diagram ; Stoichiometry ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: C. S. | Goutam Sahana | Ayesha Firdose | A K Shreesha | Samuel Samuel
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of Lithium NCM Cathode Material Using Machine-Learning Techniques Abstract- The optimisation of Lithium Nickel−Cobalt−Manganese (NCM) cathode materials is essential for improving the electrochemical performance of lithium-ion batteries. However, experimental evaluation of doped NCM compositions is slow, resource-intensive, and unable to efficiently explore the vast combinational space of dopants and synthesis parameters. This work presents a machine-learning-based predictive framework designed to estimate the discharge capacity of doped NCM cathode and cost-effectiveness. However, enhancing the discharge capacity and structural stability of NCM materials remains a complex scientific challenge. Their electrochemical behaviour is influenced by multiple interdependent factors, including dopant type, concentration, crystal structure, calcination conditions, and lattice distortion. Exploring such a multidimensional design space through traditional experimental methods is slow, expensive, and often yields limited insight due to the time-consuming nature of synthesis materials using material composition, structural characteristics, and synthesis-related data. Seven supervised regression algorithms—Linear Regression, Ridge Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting, Support Vector Regression, and K-Nearest Neighbor Regression—were compared to identify the most reliable model. Ridge Regression demonstrated the best balance of accuracy, stability, and generalisation, making it the final predictive model. The results show that machine learning can significantly reduce the dependency on costly laboratory experiments and accelerate the discovery of high- performance cathode compositions. This study contributes a data-driven methodology for supporting materials research and improving lithium-ion battery development. Key Words: lithium-ion batteries, NCM cathode materials, discharge capacity prediction, machine learning, Ridge Regression, dopant optimisation, regression modelling, materials informatics, battery performance analysis, data- driven material design. Dopant ; Cathode ; Support vector machine ; Decision tree ; Electrochemistry ; Calcination ; Battery (electricity) ; Doping ; Lithium (medication) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Yuheng Li | F. Guo | Shui‐Yang Lien | Abd. Rashid bin Mohd Yusoff | Zhihong Zheng | Jingyun Zhang | Peng Gao
Source: Physical Chemistry Chemical Physics (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Harnessing the leading edge: machine learning ventures in chemistry and materials science The widespread application of machine learning (ML) is profoundly transforming traditional research methods in materials science and chemistry, bringing new opportunities while also posing significant challenges and risks. Improper use of ML methods can lead to biased and misleading research outcomes. This review outlines the application processes of ML in the fields of materials science and chemistry, providing an in-depth analysis of potential issues at each stage with case studies, including data management, model construction, evaluation, and shared risks in data reporting. We emphasize the necessity of standardized use of ML and highlight the current crises faced in ML applications in scientific research. This review also summarizes a series of strategies to ensure the reliability and scientific validity of research results. It aims to offer practical guidance to researchers, helping them leverage the advantages of ML while applying these tools in a scientifically sound and compliant manner, avoiding common pitfalls, and promoting more rigorous research practices in materials science and chemistry. Enhanced Data Rates for GSM Evolution ; Nanotechnology ; Chemistry ; Engineering ; Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science
Authors: Steinbeck, Christoph
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Advancing Machine Learning in Chemistry Through Open and Large-Scale Data Initiatives Research in Chemical Informatics, in particular in Deep Learning, has long been hampered by a lack of free and open datasets, let alone large ones. In my statement, I will highlight examples for promising machine learning in chemistry and describe efforts to build a large corpora of chemistry data in the context of NFDI4Chem and elsewhere. Context (archaeology) ; Deep learning ; Chemistry education ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Atsuo Yamada | Norio Takenaka | Taiga Iwata | T. Bernhard
Source: Research Square (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Machine learning force field molecular dynamics simulation of SEI formation on lithium metal Lithium (medication) ; Interphase ; Molecular dynamics ; Electrolyte ; Lithium metal ; Force field (fiction) ; Field (mathematics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials