jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yannick Couzinié | Shunsuke Daimon | Hirofumi Nishi | Natsuki Ito | Yusuke Harazono | Yu-ichiro Matsushita
Source: Physical review. A/Physical review, A (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Towards improved quantum machine learning for molecular force fields This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based on previous research and point out shortcomings in the parametrization of the atomic environments. These shortcomings limit its expressivity as an interatomic potential and precludes transferability between molecules. We propose a revised QNN architecture that addresses these shortcomings. While both QNNs show promise in force prediction, with the revised architecture showing improved accuracy, they struggle with energy prediction. Further, both QNNs architectures fail to demonstrate a meaningful scaling law of decreasing errors with increasing training data. These findings highlight the challenges of scaling QNNs for complex molecular systems and emphasize the need for improved encoding strategies, regularization techniques, and hybrid quantum-classical approaches. Scaling ; Point (geometry) ; Quantum ; Architecture ; Encoding (memory) ; Force field (fiction) ; Regularization (linguistics) ; Limit (mathematics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Ryosuke Nakagawa | Izumi Takahara | Kiyou Shibata | Teruyasu Mizoguchi
Source: Chemistry Letters (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Prediction of the overlap population diagram of organic molecules based on a graph neural network Abstract Chemical bonds and electronic structures govern properties of materials and molecules. Overlap population (OP) diagrams, a key tool for understanding the chemical bonding, typically relies on computationally expensive quantum simulations. We developed a graph neural network model to directly predict OP diagrams from molecular structures, using a chemical bonding database of 2,370,000 bonds in 130,000 molecules. The model achieved high accuracy across diverse bond types and environments, reproducing qualitative trends even for unknown structures. Chemistry ; Quantum chemical ; Molecule ; Artificial neural network ; Population ; Graph ; Biological system ; Diagram ; Chemical bond ; Chemical physics ; Computational chemistry ; Theoretical computer science ; Artificial intelligence ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ruiqi Wang | Kuan-Miao Lin | Hao‐Yi Huang | Chengqi Zhang | Maowen Li
Source: Journal of Physics Conference Series (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of mechanical properties of metallic materials: data augmentation strategies Abstract Accurate prediction of key mechanical properties of metallic materials is essential to accelerating the development and application of new materials. However, existing studies have been insufficient in systematically verifying the effectiveness of data enhancement strategies and comparing the performance of multiple machine learning models, especially in utilizing enhanced datasets to improve prediction accuracy. This study aims to systematically evaluate the impact of data enhancement strategies on the prediction accuracy of the mechanical properties of metallic materials. Based on the publicly available material property dataset, this study constructs a data processing flow that includes data cleaning, feature engineering, oversampling based on the binning of target variables, and synthetic minority class oversampling techniques to generate the original and augmented datasets. On this basis, five machine learning models were trained and evaluated for their prediction performance on both datasets. The results indicate that the applied data enhancement strategies significantly improve the prediction accuracy of all models. Oversampling ; Machine learning ; Computer science ; Predictive modelling ; Artificial intelligence ; Key (lock) ; Property (philosophy) ; Data mining ; Feature engineering ; Class (philosophy) ; Deep learning Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Kevser Kübra Kırboğa | Büşra Boz | Ferda Mindivan
Source: Hacettepe Journal of Mathematics and Statistics (article, 2025-05-25)
Field: Materials Science — Biomaterials

Explainable machine learning for statistical prediction of polymer fiber properties using process parameters The modeling and optimization of electrospinning parameters are essential for controlling the fiber diameter and material properties. This study uses machine learning to examine the effects of multiple electrospinning parameters on fiber diameter. Ten regression models were evaluated, with hyperparameter optimization performed using grid search cross-validation and Bayesian optimization with multiple fold configurations. The Random Forest model demonstrated superior performance (root mean square error = 129.308, coefficient of determination = 0.542, mean absolute error = 104.014, mean absolute percentage error = 0.371). Further improvement was achieved through Bayesian optimization (root mean square error = 127.400, coefficient of determination = 0.555, mean absolute percentage error = 0.360). Extreme Gradient Boosting and Gradient Boosting also showed high accuracy, while linear models performed poorly. The Shapley Additive Explanations analysis identified rotational speed as the most influential parameter (value = 0.473), followed by flow rate (0.36), porosity (0.32) and needle diameter (0.27), all positively affecting fiber diameter. In contrast, voltage (-0.24), temperature (-0.19), towing (-0.14), and humidity (-0.13) showed negative impacts. Experimentally, Polycaprolactone (Molecular number = 80,000) nanofibers were manufactured at three rotation speeds (150, 450 and 750 revolutions per minute), resulting in fiber diameters of 100.09, 154.0, and 175.45 nanometers, respectively. These findings reveal complex interactions between the electrospinning parameters and the fiber morphology, demonstrating the potential of machine learning to optimize nanofiber production. Mathematics ; Fiber ; Statistical learning ; Process (computing) ; Polymer ; Machine learning ; Artificial intelligence ; Composite material ; Computer science ; Materials science Electrospun Nanofibers in Biomedical Applications ; Advanced Neural Network Applications
Authors: Bianchi, Michele Giovanni | Fiorentin, Michele Re | Risplendi, Francesca | Pirri, Candido Fabrizio | Parrinello, Michele | Bonati, Luigi | Cicero, Giancarlo
Source: arXiv (Cornell University) (preprint, 2025-11-24)
Field: Materials Science — Materials Chemistry

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics Simulating electrified metal/water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively expensive with ab initio methods. We present TRECI, a data-efficient workflow for constructing machine learning force-fields (ML-FFs) that achieve ab initio-level accuracy in electronically grand-canonical molecular dynamics. By leveraging transfer learning from general-purpose and domain-specific models, TRECI enables stable and accurate simulations across a wide potential range using a reduced number of reference configurations. This efficiency allows the use of high-level meta-GGA functionals and rigorous surface-electrification schemes. Applied to Cu(111)/water, models trained on just one thousand configurations yield accurate molecular dynamics simulations, capturing bias-dependent solvent restructuring effects not previously reported. TRECI offers a general strategy for characterising diverse materials and interfacial chemistries, significantly lowering the cost of realistic constant-potential simulations and expanding access to quantitative electrochemical modelling. Constant (computer programming) ; Molecular dynamics ; Workflow ; Yield (engineering) ; Electrochemistry ; Range (aeronautics) ; Ab initio Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xiwei Zhou | Yuan Yuan | Tingting Liu | Tao Chen | Jun Wang | Ligang Zhang | Liang Wu | Aitao Tang | Xianhua Chen | Nele Moelans | Fusheng Pan
Source: Materials Today Chemistry (article, 2025-12-19)
Field: Materials Science — Biomaterials

High-temperature activity coefficient prediction: Thermodynamic-Machine learning synergistic modeling of binary Mg-based alloy system at 500 °C Alloy ; Activity coefficient ; Valence (chemistry) ; Magnesium ; Binary number ; Magnesium alloy ; Work (physics) ; Material properties ; Melting point Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Jaka Fajar Fatriansyah | Irma Hartia Tihara | Mohammad Rayhan Ramadano | Andreas Federico | Agrin Febrian Pradana | Siti Norasmah Surip | Nicolas Gascoin
Source: Results in Engineering (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

A machine learning framework for screening phenyl phthalimide derivatives as corrosion inhibitors based on dataset generated by DFT and molecular dynamics simulations • A framework that combines computational calculation (DFT and MD) with machine learning to screen phenyl phtalamide derivative compounds as corrosion inhibitor. • ANN shows highest accuracy (R 2 =93.18), in comparison to XGBoost (R 2 =81.29), XTRees (R 2 =84.59), KNN (R 2 =58.36), and SVR (R 2 =73.74). • B06[C-N] and qnmax are the most important factor to affect inhibitor efficiency. Corrosion in metals, especially carbon steel, incurs substantial economic losses, estimated at 2.5 trillion USD per year, or 3.4% of the global GDP. This research examines the corrosion inhibition efficacy of 284 phenyl phthalimide derivatives through computational techniques and machine learning. The electronic structure properties, comprising E HOMO (mean: -6.36 eV, standard deviation: 0.49 eV), E LUMO (mean: -2.41 eV, standard deviation: 0.30 eV), and adsorption energy (mean: -171.23 kJ/mol, standard deviation: 27.50 kJ/mol), were determined through Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations, and correlated with experimentally obtained inhibition efficiency. Various machine learning models, such as Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and XGBoost, were utilized, with ANN demonstrating the highest prediction accuracy, reaching R² values of 93.18% for E HOMO and 91.12% for E LUMO . The complex relationship between molecular descriptor and electronic properties makes ANN perform better than XGBoost and XTRees. SHAP and PFI studies of feature importance revealed that descriptors B06[C-N] (PFI score: 0.016) and qnmax (PFI score: 0.009) are essential for inhibitor efficacy. Correlation-based filtering for feature reduction revealed that models utilizing a diminished set of 1,078 descriptors (down from 5,627) maintained robust performance, attaining a R² score of 91.47% for E HOMO . These findings create a strong computational framework for the screening and optimization of corrosion inhibitors, minimizing reliance on experimental trials and facilitating cost-effective, scalable synthesis of customized inhibitors. Subsequent efforts will be focused on experimental validation and the implementation of this methodology in more extensive chemical contexts. Molecular descriptor ; Phthalimide ; Support vector machine ; Artificial neural network ; Density functional theory ; Molecular dynamics ; Quantitative structure–activity relationship ; HOMO/LUMO Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xuke Li | Lianlian Fu | Yunhang Liu | Xiao Meng | Ming Li | Peiling Ke
Source: Journal of Applied Crystallography (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

CorFuncSAXSNet: deep-learning-driven extraction of nanostructural parameters from small-angle X-ray scattering data of polymeric materials Small-angle X-ray scattering (SAXS) analysis of semi-crystalline polymers remains a labour-intensive process requiring expert interpretation of correlation functions. To address this challenge, we present CorFuncSAXSNet: a deep neural network framework designed to directly predict nanostructural parameters – including lamellar crystalline thickness (dc) and amorphous layer thickness (da) – from 1D raw SAXS curves. Building upon SAXS datasets collected at the Shanghai Synchrotron Radiation Facility's BL19U2 beamline, we developed three neural architectures: a convolutional neural network, a residual network and a q -space attention network. Data augmentation strategies, including Gaussian noise injection and q -shift interpolation, improved model robustness against experimental uncertainties. Cross-validation results demonstrate that all networks achieve mean absolute errors of 0.109–0.112 nm for dc and 0.459–0.499 nm for da. Though amorphous layer predictions at large values exhibit higher errors due to dataset skewness (83.3% of data clustered at 4.5 &lt; dc &lt; 6.5 nm, 5.0 &lt; da &lt; 20.0 nm), our framework enables rapid parameter extraction (&lt;1 s per curve), reducing reliance on manual graphical methods. CorFuncSAXSNet bridges the gap between AI and synchrotron-based structural analysis, establishing a foundation for real-time smart beamline architectures. Beamline ; Small-angle X-ray scattering ; Materials science ; Scattering ; Convolutional neural network ; Amorphous solid ; Synchrotron radiation ; Synchrotron ; Computer science ; Optics ; Artificial intelligence ; Beam (structure) ; Physics ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Xin Li | Meng Xu | Tao Zheng | Chen Sun | Xin Lan | Jun Hu | Zechuan Yu
Source: Materials Today Communications (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Fast probe of hydrogen molecules binding on alpha-iron surface via a machine-learning method and high-throughput molecular dynamics simulations Materials science ; Molecular dynamics ; Throughput ; Molecule ; Alpha (finance) ; Hydrogen molecule ; Hydrogen ; Surface (topology) ; Chemical physics ; Hydrogen bond ; Nanotechnology ; Computational chemistry ; Organic chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Cheng Yan | Xiaming Feng | Patrick Mensah | Guoqiang Li
Source: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

Toward a general physics-informed neural network for amorphous shape memory polymer modelling Due to the complex behaviour of amorphous shape memory polymers (SMPs), traditional constitutive models often struggle with material-specific limitations, challenging curve-fitting, history-dependent stress calculations and error accumulation from stepwise calculation for governing equations. In this study, we propose a physics-informed artificial neural network (PIANN) that integrates a conventional neural network with a strain-based phase transition framework to predict the constitutive behaviour of amorphous SMPs. The model is validated using five temperature–stress datasets and four temperature–strain datasets, including experimental data from four types of SMPs and simulation results from a widely accepted model. PIANN predicts four key shape memory behaviours: stress evolution during hot programming, stress recovery following both cold and hot programming and free strain recovery during heating branch. Notably, it predicts recovery strain during heating without using any heating data for training. Comparisons with experimental data show excellent agreement in both programming (cooling) and recovery (heating) branches. Remarkably, the model achieves this performance with as few as two temperature–stress curves in the training set. Overall, PIANN addresses common challenges in SMP modelling by eliminating history dependence, improving curve-fitting accuracy and significantly enhancing computational efficiency. This work represents a substantial step forward in developing generalizable models for SMPs. Amorphous solid ; Artificial neural network ; Polymer ; Statistical physics ; Cognitive science ; Computer science ; Materials science ; Theoretical physics ; Physics ; Nanotechnology ; Engineering physics ; Psychology ; Artificial intelligence ; Chemistry ; Composite material Polymer composites and self-healing ; Advanced Memory and Neural Computing
Authors: Patryk Tajs | Mateusz Skarupski | Jakub Rydzewski
Source: Journal of Chemical Information and Modeling (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

<tt>NeuralTSNE</tt>: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly employed to analyze large volumes of high-dimensional MD data to gain insight into hidden information encoded in MD trajectories. Among many such techniques, t-distributed stochastic neighbor embedding (t-SNE) is especially popular. A parametric version of t-SNE that employs neural networks is less commonly known, yet it has demonstrated superior performance in dimensionality reduction compared to the standard implementation. Here, we present a Python package called NeuralTSNE with our implementation of parametric t-SNE. The implementation is done using the PyTorch library and the PyTorch Lightning framework and can be imported as a module or used from the command line. We show that NeuralTSNE offers an easy-to-use tool for the analysis of MD data. Python (programming language) ; Dimensionality reduction ; R package ; Computer science ; Artificial neural network ; Curse of dimensionality ; Artificial intelligence ; Pattern recognition (psychology) ; Computational science ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Yue Zhang | Robert J. Appleton | Kui Lin | Megan McCarthy | Jeffrey T. Paci | Subramanian K. R. S. Sankaranarayanan | Alejandro Strachan | Horacio D. Espinosa
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials Benchmark (surveying) ; Artificial neural network ; Multiscale modeling ; Transformative learning ; Domain (mathematical analysis) ; Work (physics) ; Ab initio ; Deep learning ; Potential field Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Flora Esposito | Ulderico Di Caprio | Bruno Rodrigues | Florence H. Vermeire | Idelfonso B. R. Nogueira | M. Enis Leblebici
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Predicting Surface Tension of Organic Molecules using COSMO-RS Theory and Machine Learning Surface tension is a fundamental property at the liquid/gas interface, influencing phenomena such as capillary action, droplet formation, and interfacial behavior in chemical engineering processes. Despite its significance, experimental determination of surface tension is time-intensive and impractical for in silico-designed compounds. Predictive models are essential for bridging this gap. This study expands on Gaudin's COSMO-RS-based model, which assumes uniform molecular orientation at the surface, by testing its predictive capability across broader temperatures (5�50�C) and developing a hybrid model combining first-principle and machine learning insights to improve Gaudin�s model predictions. The HM employs a serial configuration where COSMO-RS predictions serve as inputs alongside molecular descriptors, derived using the Mordred library. SHAP analysis guides feature selection, enhancing model interpretability. An artificial neural network refines predictions, optimized via Bayesian optimization for architecture and hyperparameters. Using a diverse dataset of 85 organic molecules, the HM demonstrates improved accuracy, reducing various metrics compared to standalone first-principle predictions. Surface tension ; Computer science ; Organic molecules ; COSMO-RS ; Molecule ; Artificial intelligence ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Chemical Sensor Technologies
Authors: Jeffrey A. Cramer | Caleb M. Bunton | Matthew C. Davis | Paige E. Sudol | Katherine M. Hinnant | Arthur W. Snow | Ramagopal Ananth
Source: Journal of Surfactants and Detergents (article, 2025-03-17)
Field: Chemistry — Analytical Chemistry

Predicting the firefighting efficacy of surfactants prior to synthesis via ensemble artificial neural network modeling of a foam performance database Abstract Research efforts incorporating machine learning (ML) are currently focused on developing replacements for the toxic and bio‐accumulative per‐ and polyfluorinated alkyl substances in fire suppressing foams. In the following work, ensembles of 10 artificial neural networks (ANN) were trained on a fire suppression database, described by Sudol et al., correlating area under the curve values obtained from 19‐cm gasoline and heptane pool fire extinction curves to the molecular descriptors of surfactants within various firefighting foams. These ANN model ensembles were then used to evaluate proposed surfactant structures to predict the firefighting effectiveness prior to laboratory synthesis. The two most promising surfactants were a tetrasiloxane diglucoside and a chlorotrisiloxane‐polyethyleneoxide (PEO). These surfactants were synthesized, and their fire extinction performances were assessed via 19‐cm gasoline and heptane pool fire experiments to validate the ANN predictions. The synthesis of the demonstrably high‐performing tetrasiloxane diglucoside surfactant is considered a successful ML application in the context of fluorine‐free firefighting surfactant research and development. Meanwhile, the synthesis of the low‐performing chlorinated PEO surfactant, which failed to meet predicted performance expectations, demonstrates the need for both comprehensive training data sets and the proper consideration of modeling redundancies to safeguard against unreliable ML‐derived performance predictions. Chemistry ; Artificial neural network ; Firefighting ; Database ; Artificial intelligence ; Computer science ; Organic chemistry Petroleum Processing and Analysis ; Polymer composites and self-healing ; Asphalt Pavement Performance Evaluation