jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ms. Nikita Selokar | Shalini Gupta | prof. Sushil Bhise
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Intelligent Prediction of Nanomaterial Properties Using Crystal Graph-Based Deep Learning Models The burgeoning field of nanotechnology necessitates efficient and accurate methods for predictingthe properties of novel nanomaterials. Traditional experimental characterization is often time-consuming andresource-intensive. Computational approaches, particularly those leveraging machine learning, offer a promisingalternative. This paper proposes a novel framework for the intelligent prediction of nanomaterial properties usingcrystal graph-based deep learning models. By representing the atomic structure of nanomaterials as graphs, whereatoms are nodes and bonds are edges, we can effectively capture the complex interatomic relationships that dictatematerial properties. Deep learning architectures, specifically Graph Neural Networks (GNNs), are employed tolearn intricate patterns from these crystal graphs and establish robust correlations with various material properties,including but not limited to band gap, mechanical strength, and thermal conductivity. This approach overcomeslimitations of traditional feature engineering by automatically extracting relevant structural information. Theproposed methodology offers a powerful tool for accelerating nanomaterial discovery and design, enabling highthroughput screening and optimization of materials with desired functionalities. Deep learning ; Artificial neural network ; Nanomaterials ; Field (mathematics) ; Feature engineering ; Feature (linguistics) ; Deep neural networks Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ling Tang | Weiyi Xia | Gayatri Viswanathan | Ernesto Soto | Kirill Kovnir | Cai‐Zhuang Wang
Source: ArXiv.org (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 2007, Behler and Perrinello first proposed and demonstrated an artificial neural network machine learning (ANN-ML) scheme, opening a new paradigm for developing accurate and efficient interatomic potentials for reliable MD simulation studies of the thermodynamics and kinetics of materials. In this paper, we show that an accurate and transferable ANN-ML interatomic potential can be developed for MD simulations of La-Si-P system. The crucial role of training data in the ML potential development is discussed. The developed ANN-ML potential accurately describes not only the energy versus volume curves for all the known elemental, binary, and ternary crystalline structures in La-Si-P system, but also the structures of La-Si-P liquids with various compositions. Using the developed ANN-ML potential, the melting temperatures of several crystalline phases in La-Si-P system are predicted by the coexistence of solid-liquid phases from MD simulations. While the ANN-ML model systematically underestimates the melting temperatures of these phases, the overall trend agrees with experiment. The developed ANN-ML potential is also applied to study the nucleation and growth of LaP as a function of different relative concentrations of Si and P in the La-Si-P liquid, and the obtained results are consistent with experimental observations. Interatomic potential ; Molecular dynamics ; Artificial neural network ; Nucleation ; Ternary operation ; Function (biology) ; Potential energy Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Huei-Ching Liao (21979487) | Yen-Hsiang Lin (21573413) | Chi-How Peng (1513420) | Yi-Pei Li (1745551)
Source: Figshare (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

DirectedMessage Passing Neural Networks for AccuratePrediction of Polymer–Solvent Interaction Parameters Accurate prediction of polymer–solvent interactions is essential for applications such as polymer processing, drug delivery, and membrane separations. The Flory–Huggins interaction parameter (χ parameter) serves as a key descriptor for polymer–solvent compatibility; however, its experimental determination is often costly and time-consuming. In this study, we develop a machine learning framework based on directed message passing neural networks (D-MPNNs) to predict χ parameters directly from molecular structures, temperature, and volume fraction. Our approach systematically evaluates different feature representations, pooling methods, and empirical equation integrations to optimize prediction accuracy. Among the tested models, D-MPNN-TC, which incorporates both temperature and volume fraction, demonstrates strong predictive performance (MAE = 0.092, RMSE = 0.162, and <i>R</i><sup>2</sup> = 0.926), outperforming descriptor-based models that rely on precomputed molecular fingerprints and handcrafted chemical features. Additionally, integrating the Flory–Huggins equation into the classification framework enables highly accurate miscibility predictions, with F1 scores of 0.915. Further analysis using t-SNE visualization reveals that D-MPNNs effectively capture key structural features, such as aromaticity and cyclic structures, that influence polymer–solvent interactions. Our findings highlight the advantages of graph-based molecular representations over traditional fingerprinting methods and underscore the importance of volume fraction information in predicting polymer–solvent compatibility. This study provides a scalable and interpretable framework for leveraging machine learning in polymer science, facilitating data-driven solvent selection and polymer design. Pooling ; Scalability ; Key (lock) ; Artificial neural network ; Feature selection ; Visualization ; Volume (thermodynamics) ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Zhao-Chen Xi (21634333) | Xin Wang (91924) | Chang-Hao Wang (19860887) | Wei Wang (17594) | Jian Bao (549957) | Diming Xu (7460081) | Guo-Qiang He (19860890) | Tao Zhou (117050) | Guo-Hua Chen (5392337) | Song Xia (3808810) | Di Zhou (126052)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

AcceleratingLow‑<i>k</i> DielectricMaterial Discovery: From Graph Machine Learning to Synthesis The exploration of dielectric materials with a specific permittivity remains a significant challenge. Dielectric materials with low permittivity are widely used in semiconductor interlayers and communication substrates for reducing parasitic capacitance and latency transmission. However, discovering novel dielectric materials often relies on a trial-and-error strategy, which is inefficient and time-consuming. Herein, a graph neural network (Res-GCN) is proposed to directly predict permittivity from the connections of atoms, after which a material searching pipeline is developed based on pattern recognition approaches. The model improves relative accuracy by ∼267% over the classical Clausius–Mossotti model, ∼38% over deep neural networks, and ∼17% over crystal convolution neural networks and achieves a root-mean-squared error of ∼1.788 over different crystal symmetries. The high-throughput screening is conducted over 6000 material entries within minutes, identifying promising low-permittivity candidates, and two novel dielectric ceramics with expected properties were discovered and synthesized through only eight targeted experiments. Such machine learning approaches provide an efficient and scalable framework for accelerating the discovery of dielectric materials, significantly shortening the research cycle, and enhancing the synergy between prediction and experiment. Dielectric ; Artificial neural network ; Permittivity ; Scalability ; Capacitance ; Graph ; Convolutional neural network ; Deep learning Machine Learning in Materials Science ; Dielectric properties of ceramics ; Inorganic Chemistry and Materials
Authors: T. Chung | S. J. Poon
Source: Materials (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Machine Learning Phase Classification of Thermoelectric Materials In this study, we employ a Support Vector Machine (SVM) model to efficiently classify the phases of thermoelectric (TE) alloys. While ab initio calculations and experiments have explored the phases of functional TE materials, the large variety of alloys makes these explorations time-consuming and expensive. Therefore, there is a critical need for time-efficient methods to accelerate the discovery and development of new TE materials. Recently, machine learning (ML) classification models have been applied to predict material phases, including those of multi-principal element alloys. Using an SVM to classify phases of TE alloys, our results demonstrate that the model achieves prediction accuracies ranging from 77% to 92%. Additionally, cross-validation across various TE phases is performed to demonstrate the model's robustness in phase differentiation. This work offers a time-efficient computational approach to distinguish TE material phases, offering valuable insights that can aid in the evaluation and design of high-performance thermoelectric materials. Robustness (evolution) ; Support vector machine ; Thermoelectric effect ; Thermoelectric materials ; Ab initio ; Work (physics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yong Wang | Xudong Ma | Alexander Robson | Robert D. Short | James W. Bradley
Source: Plasma Processes and Polymers (article, 2025-05-11)
Field: Materials Science — Materials Chemistry

A Hybrid Machine Learning Approach to Predict and Evaluate Surface Chemistries of Films Deposited via APPJ ABSTRACT We developed a hybrid machine learning model, integrating Artificial Neural Network (ANN), Random Forest (RF) and AdaBoost (AB), to predict and evaluate the plasma polymerization process of TEMPO monomer, specifically for Nitric Oxide films. This model is specifically designed to adeptly navigate the intricate landscape of the plasma polymerization process. Through genetic algorithm optimization, we have fine‐tuned our hybrid model's algorithm weights, achieving results that closely match experimental data. TEMPO‐Helium flow ratio is identified as the most critical parameter for the surface N percentage, with a relative importance of 41%. Frequency has the greatest influence on the N‐O percentage, with a relative importance of 30%. The intertwined influence of different polymerization parameters on the film's surface chemistry has been detailed. Materials science ; Surface (topology) ; Chemical engineering ; Nanotechnology ; Computer science ; Engineering ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Alexander Mavromaras | Kyle Starkey | Xavier Rozanska | E. Wimmer
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning for Multi-Scale Modeling in Corrosion-Resistant Alloy Engineering Abstract Recent advances in chemical system simulations are driven by multi-scale modeling, machine learning, and significant improvements in theoretical frameworks and computational power. AI plays a pivotal role in materials discovery and optimization by enabling systematic screening and quantitative predictions. Machine learning methods leverage high-quality, materials-specific data, which are often sparse, time-consuming, and expensive to obtain. These methods benefit significantly from physics-driven descriptors obtained through atomistic simulations. The critical importance of microstructure phenomena for corrosion-resistant alloy engineering has led to the development of advanced techniques for building accurate models and conducting extensive simulations. This presentation will discuss recent developments in multi-scale modeling and phase-field simulation techniques within the MedeA materials modeling environment and showcase applications relevant to corrosion research. The MedeA simulation environment fosters molecular-level understanding and generates key materials property data with exceptionally fine time and space resolution. MedeA integrates advanced electronic structure methods with classical molecular dynamics and Monte Carlo methods, builders, analysis tools, and workflows for automated property prediction, the informatics method Polymer Expert, and finite element methods for capturing microstructure evolution. Workflow ; Leverage (statistics) ; Materials informatics ; Key (lock) ; Automation ; Property (philosophy) ; Monte Carlo method Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mehrdad Jalali | Lachlan Caulfield | Eric Sauter | Alexei Nefedov | Chengwu Yang | Christof Wöll
Source: Advanced Intelligent Discovery (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces This study presents a novel convolutional neural network (CNN) architecture that represents a significant advancement in the unsupervised analysis of data from infrared (IR) spectroscopy, both in IRRAS (infrared reflection absorption spectroscopy) and in DRIFTS (diffuse reflection infrared Fourier transform spectroscopy). After measuring reference data for single‐crystal samples using IRRAS, DRIFTS allows the characterization of surfaces exposed by cerium oxide powder particles through the stretch frequency of adsorbed probe molecules. To enable real‐time monitoring of catalyst modification during exposure to reactive gases under reaction conditions, a rapid, unsupervised analysis of the DRIFTS data is required. It is demonstrated that this goal can be achieved by using a CNN with an optimized architecture. This model is proficient in determining the intensities of the adsorbed CO bands, which depend on the crystallographic orientation and oxidation state of the exposed facets. The CNN design incorporates parallel 1D convolutional layers with varied kernel sizes. These layers work in tandem to capture spectral features. To address the challenge of overfitting, advanced regularization techniques within the CNN are integrated, enhancing the model's performance on new, unseen data. In particular, this approach to generating synthetic data has been instrumental in improving the performance of the CNN. The employment of the Adam optimizer and the mean squared error loss function aligns the model for efficient learning, ensuring accurate and reliable predictions. By introducing this CNN architecture, a robust, precise, and adaptable tool for rapid, unsupervised spectroscopic analysis is provided, demonstrating the potential of deep learning combined with synthetic data generation for advanced spectroscopy applications. Characterization (materials science) ; Catalysis ; Spectroscopy ; Materials science ; Infrared spectroscopy ; Artificial neural network ; Chemical engineering ; Nanotechnology ; Chemistry ; Computer science ; Organic chemistry ; Engineering ; Physics ; Artificial intelligence Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Water Quality Monitoring and Analysis
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Automated Knowledge Synthesis and Validation for Advanced Materials Discovery through Heterogeneous Graph Neural Networks (HKS-HGNN) **Abstract:** This research introduces Automated Knowledge Synthesis and Validation (HKS-VG) for Advanced Materials Discovery, a novel framework leveraging Heterogeneous Graph Neural Networks (HGSNN) to accelerate the identification and validation of promising material candidates. Unlike traditional materials discovery methods reliant on human intuition and limited experimental data, HKS-VG autonomously synthesizes knowledge from diverse data sources – scientific literature, patents, and experimental databases – and iteratively validates predictions through in-silico simulations. This approach achieves a 10x acceleration in candidate screening and a 20% increase in the accuracy of predicting material properties compared to existing machine learning models, paving the way for rapid advancement in materials science. Furthermore, it's designed for modular scalability allowing integration of new knowledge sources and property prediction models. Scalability ; Modular design ; Intuition ; Knowledge graph ; Artificial neural network ; Graph ; Identification (biology) ; Knowledge extraction Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: M.M. Darwhekar | Y. Pushpalatha Devi | Theta Murty | K.V. Mani Krishna | R.N. Singh
Source: Journal of Nuclear Materials (article, 2025-10-05)
Field: Materials Science — Materials Chemistry

Automated hydride segmentation and quantification in zirconium alloys using a convolutional neural network-assisted machine learning model Convolutional neural network ; Segmentation ; Artificial neural network ; Thresholding ; Modular design ; Pattern recognition (psychology) ; Zirconium ; Workflow Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Li Yan
Anthony Choong (22855328) | Zhong Chen (32985) | Man-Fai Ng (1400281)
Source: Figshare (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Discovery of EffectiveHalide Solid Electrolytes for Solid-State Rechargeable Batteriesvia Machine Learning andDFT Calculations Halide solid electrolytes (SEs) are a strong candidate for next-generation lithium-based solid-state batteries for their potential to possess a balance of key properties including ionic conductivity, mechanical properties, and electrochemical stability window (ESW) and can be synthesized using environmentally friendly processes. However, there is a lack of halides simultaneously fulfilling all the mentioned key properties, and searching for the right candidates via experiments is proven challenging. In this work, we develop a computational approach combining machine learning (ML) and DFT calculations, to discover promising halide SEs that satisfy several bulk properties via multiproperty predictions. Various ML and deep learning (DL) models are compared to predict ionic conductivity, bulk and shear moduli, and ESW. The CatBoost, Light Gradient Boosting (LGBM), and Skorch Neural Network (NN) models are found to yield high prediction accuracies for the mentioned properties, with minimum average classification accuracies and average <i>R</i><sup>2</sup> scores exceeding 80% and 0.70, respectively. DFT verifications are performed on Rb<sub>2</sub>LiBiCl<sub>6</sub>, LiHF<sub>2</sub>, and Rb<sub>2</sub>LiAlF<sub>6</sub>, with the results suggesting Rb<sub>2</sub>LiAlF<sub>6</sub> as a promising candidate for high voltage battery applications. Overall, we demonstrate that the current ML + DFT approach is useful in screening potential halide solid-state electrolytes that can satisfy several key SE properties. Halide ; Electrolyte ; Boosting (machine learning) ; Ionic bonding ; Key (lock) ; Artificial neural network ; Electrochemistry ; Voltage Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Do Hung Manh | Sang Uck Lee
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Prediction of Catalytic Activity of Alloy Spherical Nanoparticles for the Hydrogen Evolution Reaction Alloy ; Catalysis ; Nanoparticle ; Hydrogen ; Materials science ; Chemical engineering ; Computer science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jonathan A. Semelak | Ignacio Pickering | Kate Huddleston | Justo Olmos | Juan Santiago Grassano | Camila M. Clemente | Salvador I. Drusin | Marcelo A. Martí | Mariano C. González Lebrero | Adrián E. Roitberg | Darío A. Estrı́n
Source: ChemRxiv (preprint, 2025-02-20)
Field: Materials Science — Materials Chemistry

Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics We introduce an innovative machine learning (ML)-based framework for multiscale molecular modeling, in which the ML subsystem is treated as an electrostatic entity interacting with its molecular mechanics (MM) environment through classical electrostatics. The integration of ML accuracy with multiscale modeling is accomplished by leveraging the capabilities of the ANI neural networks to predict geometry-dependent atomic partial charges at the Minimal Basis Iterative Stockholder (MBIS) level, going beyond static mechanical embedding. This ML/MM approach can closely approximate state-of-the-art multi-scale quantum-classical (QM/MM) methods while significantly lowering computational requirements, thereby facilitating more efficient and precise simulations in computational chemistry. The method requires no additional training beyond the initial model setup and is integrated into Amber, one of the most widely used software suites for molecular modeling, ensuring accessibility to the broader community. We validate its performance across a variety of challenging applications, including solvation structure, vibrational spectra, torsion free energy profiles, and protein-ligand interactions, achieving excellent agreement with QM/MM benchmarks. This framework not only advances the frontiers of multiscale modeling but also showcases the potential of machine learning to achieve quantum-level accuracy with exceptional efficiency for complex chemical systems. Electrostatics ; Computer science ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques