jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiayan Xu | A. K. Patra | Amar Deep Pathak | Sharan Shetty | Detlef Hohl | Roberto Car
Source: arXiv (Cornell University) (preprint, 2025-12-04)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy configurations and molecular dynamics simulations at finite temperature. These are computationally demanding tasks that are intractable within DFT for large systems. In the last two decades, machine learning interatomic potentials (MLIPs) have been successfully used to substantially increase the size and time scales accessible to simulations approximating DFT accuracy. However, training reliable MLIPs is non-trivial as it requires many costly DFT calculations. Recently, several universal MLIPs (uMLIPs) have been developed, which are trained on large datasets that cover a wide range of molecules and materials. Here, we benchmark the accuracy and the efficiency of these uMLIPs in describing Cu nanoparticles supported on Al$_2$O$_3$ surfaces against our domain-specific DP-UniAlCu model. We find that the MACE-OMAT can reproduce reasonably well the low-energy structures found in global optimization at an energy accuracy comparable to DP-UniAlCu. Interestingly, the MatterSim-v1.0.0-1M model, which exhibits larger deviations in the binding energies, can find even more stable configurations than the other two models in some supported nanoparticle sizes, showing its capability in structure exploration. For MD simulations, MACE-OMAT and MatterSim-v1.0.0-1M can qualitatively reproduce the mean-squared displacements of Cu atoms (MSD$_\mathrm{Cu}$) predicted by DP-UniAlCu, albeit at roughly two orders of magnitude higher cost. We demonstrate that the uMLIPs can be very useful in simulating supported nanoparticles even without any fine-tuning, though their reduced efficiency remains a limiting factor for large-scale simulations. Density functional theory ; Decoupling (probability) ; Benchmarking ; Benchmark (surveying) ; Molecular dynamics ; Range (aeronautics) ; Energy minimization ; Interatomic potential Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Chad W. Stone | Peter J. Mahon
Source: Journal of The Electrochemical Society (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Application of Raman Spectroscopy for the Simultaneous Estimation of pH and Sulfate Concentrations in Highly Concentrated Sulfuric Acid Herein contains a preliminary study with the goal of measuring pH and sulfate concentration of sulfuric acid electrolyte in situ, of a lead-acid battery. A series of solutions with varying pH and sulfate concentration were analysed using Raman spectroscopy. A model based on principal component analysis (PCA) and an artificial neural network (ANN) was trained to accurately predict the concentration and pH of these solutions. A custom-designed lead-acid battery was created to facilitate Raman microscopic measurements of the electrolyte adjacent to the electrode during operation. These measurements were processed based on a trained PCA-ANN model. The results demonstrate the effectiveness of this approach to estimate both the pH and sulfate concentration in the solution adjacent to the electrode interface. Based on the obtained outcomes, application of this method to the interior of a negative porous electrode should be able to independently estimate the pH and sulfate ion concentrations that can become decoupled during battery cycling. Sulfuric acid ; Raman spectroscopy ; Sulfate ; Chemistry ; Spectroscopy ; Inorganic chemistry ; Analytical Chemistry (journal) ; Environmental chemistry ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses
Authors: Jayanta Bhusan Deb | Christopher Varela | Fahim Faysal | Yi‐Ting Wang | Chiranjit Maiti | Jihua Gou
Source: Journal of Composites Science (article, 2025-05-13)
Field: Materials Science — Ceramics and Composites

Deep Artificial Neural Network Modeling of the Ablation Performance of Ceramic Matrix Composites in the Hydrogen Torch Test In recent years, there has been increasing interest in new materials such as ceramic matrix composites (CMCs) for power generation and aerospace propulsion applications through hydrogen combustion. This study employed a deep artificial neural network (DANN) model to predict the ablation performance of CMCs in the hydrogen torch test (HTT). The study was conducted in three phases to increase the accuracy of the model’s predictions. Initially, to predict the thermal behavior of ceramic composites, two linear machine learning models were used known as Lasso and Ridge regression. In the second step, four decision tree-based ensemble machine learning models, namely random forest, gradient boosting regression, extreme gradient boosting regression, and extra tree regression, were used to improve the prediction accuracy metrics, including root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R2 score), and mean absolute percentage error (MAPE), relative to the previously introduced linear models. Finally, to forecast the thermal stability of CMCs with time, an optimized DANN model with two hidden layers having rectified linear unit activation function was developed. The data collection procedure involved preparing CMCs with continuous Yttria-Stabilized Zirconia (YSZ) fibers and silicon carbide (SiC) matrix using a polymer infiltration and pyrolysis (PIP) technique. The samples were exposed to a hydrogen flame at a high heat flux of 183 W/cm2 for a duration of 10 min. A good agreement between the DANN model’s predictions and experimental data with an R2 score of 0.9671, RMSE of 16.45, an MAE of 14.07, and an MAPE of 3.92% confirmed the acceptability of the developed neural network model in this study. Composite material ; Artificial neural network ; Torch ; Materials science ; Matrix (chemical analysis) ; Ablation ; Artificial intelligence ; Computer science ; Engineering ; Aerospace engineering Advanced ceramic materials synthesis ; Advanced machining processes and optimization ; Thermal properties of materials
Authors: David Visentin | Mario Lovrić | Dejan Milenković | Robert Vianello | Željka Maglica | Kristina Tolić Čop | Dragana Mutavdžić Pavlović
Source: Data (article, 2025-12-10)
Field: Chemistry — Analytical Chemistry

Computational Dataset for Polymer–Pharmaceutical Interactions: MD/MM-PBSA and DFT Resources for Molecularly Imprinted Polymer (MIP) Design Molecularly imprinted polymers (MIPs) are promising sorbents for selectively capturing pharmaceutically active compounds (PhACs), but design remains slow because candidate screening is largely experimental or based on computationally expensive methods. We present MIP–PhAC, an open, curated resource of polymer–pharmaceutical interaction energies generated from molecular dynamics (MD) followed by MM/PBSA analysis, with a small DFT subset for cross-method comparison. This resource is comprised of two complementary datasets: MIP–PhAC-Calibrated, a benchmark set with manually verified pH-7 microstates that reports both monomeric (pre-polymerized) and polymeric (short-chain) MD/MMPBSA energies and includes a DFT subset; and MIP–PhAC-Screen, a broader, high-throughput collection produced under a uniform automated workflow (including automated protonation) for rapid within-polymer ranking and machine learning development. For each MIP—PhAC pair we provide ΔG* components (electrostatics, van der Waals, polar and non-polar solvation; −TΔS omitted), summary statistics from post-convergence frames, simulation inputs, and chemical metadata. To our knowledge, MIP–PhAC is the largest open, curated dataset of polymer–pharmaceutical interaction energies to date. It enables benchmarking of end-point methods, reproducible protocol evaluation, data-driven ranking of polymer–pharmaceutical combinations, and training/validation of machine learning (ML) models for MIP design on modest compute budgets. Benchmarking ; Benchmark (surveying) ; Ranking (information retrieval) ; Molecularly imprinted polymer ; Workflow ; Set (abstract data type) ; Resource (disambiguation) Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Filip Nikolić | Marko Čanađija
Source: Facta Universitatis Series Mechanical Engineering (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING OF STRUCTURE – PROPERTY RELATIONSHIPS: AN APPLICATION TO HEAT GENERATION DURING PLASTIC DEFORMATION In the present work, the heat generation during the plastic deformation of a multiphase material is studied using machine learning (ML) methods. The aim was to predict the temperature increase from the structure-property relationships (SPR) of a microstructure considering various Taylor–Quinney coefficients (TQCs), with the aim of achieving precision and computational efficiency suitable for industry. Using automatic microstructure generation to create datasets and finite element analysis (FEA) to obtain temperature increase – strain curves, the dataset facilitated the training of an ML model. A 3D convolutional neural network (CNN) was developed using the microstructural configuration and TQC value as input and the temperature increase – strain curve as output. The model demonstrated high prediction accuracy. The results indicated that the hard phase fraction significantly impacts the temperature increase, much more than the TQC values. This underlines the potential of the model for a better understanding of material behavior during deformation and its industrial applicability. Deformation (meteorology) ; Microstructure ; Heat generation ; Artificial neural network ; Convolutional neural network ; Finite element method ; Phase (matter) Machine Learning in Materials Science ; Metallurgy and Material Forming ; High Temperature Alloys and Creep
Authors: Than Htay | Cheng-Chun Huang | Vũ Thị | Yao-Chuan Tsai | Ching‐Yuan Su
Source: Carbon (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

High-precision and rapid detection of complex defects in transferred CVD graphene enabled by machine learning algorithms Graphene ; Computer science ; Materials science ; Artificial intelligence ; Algorithm ; Nanotechnology Graphene research and applications ; Advanced Memory and Neural Computing ; Radiation Effects in Electronics
Authors: Yuan Zhuang | Yonghao Gu | Beini Zhang | Jiang Wu | GuanHua Chen
Source: JACS Au (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accurate Exchange–Correlation Potentials for Reducing Delocalization Error in Density Functional Theory We propose a machine learning approach to generate accurate exchange-correlation potentials that reduce delocalization errors in density functional theory. By solving the Kohn-Sham equations using a deep neural network, electron densities and molecular properties can be calculated self-consistently. We demonstrate that for stretched molecular systems, where restricted calculations are employed to ensure the spin symmetry and density-driven delocalization errors become dominant, both traditional functionals and recent machine-learned functionals fail dramatically, leading to unphysical electronic charge distributions and electric dipole moments that diverge. Our trained functional, however, accurately captures the correct dissociation limit, providing electron densities, electric dipole moments, and atomic forces in excellent agreement with the reference CCSD data. By addressing delocalization errors from electron density to energy gradient calculations, our approach proves to be a potential tool for predicting molecular properties across a broad range of chemical species. Correlation ; Delocalized electron ; Density functional theory ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Minghong Jiang | Zhanfeng Wang | Yicheng Chen | Wenhao Zhang | Zhenyu Zhu | Wenjie Yan | Jianming Wu | Xin Xu
Source: Journal of Computational Chemistry (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

<scp>X2</scp> ‐ <scp>PEC</scp> : A Neural Network Model Based on Atomic Pair Energy Corrections With the development of artificial neural networks (ANNs), its applications in chemistry have become increasingly widespread, especially in the prediction of various molecular properties. This work introduces the X2-PEC method, that is, the second generalization of the X1 series of ANN methods developed in our group, utilizing pair energy correction (PEC). The essence of the X2 model lies in its feature vector construction, using overlap integrals and core Hamiltonian integrals to incorporate physical and chemical information into the feature vectors to describe atomic interactions. It aims to enhance the accuracy of low-rung density functional theory (DFT) calculations, such as those from the widely used BLYP/6-31G(d) or B3LYP/6-31G(2df,p) methods, to the level of top-rung DFT calculations, such as those from the highly accurate doubly hybrid XYGJ-OS/GTLarge method. Trained on the QM9 dataset, X2-PEC excels in predicting the atomization energies of isomers such as C<sub>6</sub>H<sub>8</sub> and C<sub>4</sub>H<sub>4</sub>N<sub>2</sub>O with varying bonding structures. The performance of the X2-PEC model on standard enthalpies of formation for datasets such as G2-HCNOF, PSH36, ALKANE28, BIGMOL20, and HEDM45, as well as a HCNOF subset of BH9 for reaction barriers, is equally commendable, demonstrating its good generalization ability and predictive accuracy, as well as its potential for further development to achieve greater accuracy. These outcomes highlight the practical significance of the X2-PEC model in elevating the results from lower-rung DFT calculations to the level of higher-rung DFT calculations through deep learning. Generalization ; Hamiltonian (control theory) ; Artificial neural network ; Density functional theory ; Computer science ; Computational chemistry ; Chemistry ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Mathematical optimization ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Sherif Abdulkader Tawfik
Source: ChemRxiv (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

Computational material science has a data problem The ever-increasing amount of data that is generated by computational material science tools has propelled the invention of new machine learning models, and subsequently assisted in the discovery of new materials. Here we present an overdue questioning of the data itself: is it suitable for training machine learning models? By examining the energy above the convex hull (Eh), electronic bandgap and formation energy data in the Materials Project dataset, we find Eh is an unsteady quantity, which is because the present materials the database do not have sufficient representation of the chemical spaces that is necessary to account for crystal decomposition. The unsteadiness of Eh also applies to DFT-computed voltages, because the computed voltage is the average of voltages obtained from the known cation-deficient stable materials. We also show the discrepancies in the reported electronic bandgap values in the Materials Project database, and the formation energy data can potentially shift due to arbitrary changes in interlayer distances of layered materials, or finding optimisation parameters that reduce the energy of the structure below the value deposited in the database. We discuss possible approaches to mitigate these data problems. Computer science ; Data science Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Yiming Chen | Jing‐Ya Deng | Zhijie Liu | Junchao Chen | Wei Wang | Yang Xiang | Xinghui Zhu | Changyun Li
Source: Scientific Reports (article, 2025-10-22)
Field: Chemistry — Analytical Chemistry

Grape sugar content prediction with multispectral alignment and improved residual network Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemical composition of grapes, has become a key technology for developing non-destructive testing devices. In this paper, we collected 2,880 randomly labeled multispectral images of Sunshine Rose grapes with a Changguang Yuchen MS600 PRO multispectral camera and measured the sugar content (in Brix values) of the labeled grapes with a handheld refractometer, using data exclusively from this grape variety. To address noise and misalignment issues in the multispectral images, we proposed preprocessing methods including Gaussian denoising and ECC (Enhanced Correlation Coefficient) algorithm registration. Based on a ResNet-50 residual network, we constructed a grape sugar content prediction regression model Improved-Res with SE (Squeeze-and-Excitation) attention modules, DSC (Depthwise Separable Convolutions), and Inception modules. The model's performance was evaluated by MSE (Mean Squared Error), MAE (Mean Absolute Error), and R<sup>2</sup> (R-Square) metrics. We compared the performance of four feature extraction methods combined with four traditional machine learning models, as well as seven deep learning models. The results showed that among traditional machine learning methods, the combination of color histogram feature extraction and the XGBoost regression achieved the best performance, with MSE, MAE, and R<sup>2</sup> of 1.35, 0.90 Brix, and 0.78, respectively. Among deep learning methods, the ResNet-50 model demonstrated the best performance, with MSE, MAE, and R<sup>2</sup> of 0.95, 0.96 Brix, and 0.84, respectively. Effective improvements of SE attention module, depthwise separable convolutions, and Inception module in the ResNet-50 model was confirmed through ablation experiments: the proposed Improved-Res model achieved MSE, MAE, and R<sup>2</sup> of 0.49, 0.55 Brix, and 0.92, respectively, which significantly outperformed traditional machine learning methods and classical deep learning models. Multispectral image ; Residual ; Hue ; Content (measure theory) ; Pattern recognition (psychology) ; Sugar ; Preprocessor ; Deep learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: W. Hao | Lei Chen | Guangtao Cheng | Yukun Tian
Source:  (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

Machine Learning Techniques Based on Hyperspectral Imaging for Waste Classification: A Review Waste classification is crucial for environmental protection and resource recovery. Existing technologies based on RGB images struggle to achieve precise identification of waste material components, resulting in low-quality recyclable waste streams and poor sorting accuracy. In contrast, hyperspectral imaging technology can simultaneously capture both spatial and spectral information of targets, offering a new technical approach for precise identification of waste material components. The powerful image processing capabilities of machine learning further enhance the potential of hyperspectral imaging technology in waste classification. However, there is currently a lack of systematic reviews on the combination of hyperspectral imaging and machine learning for waste classification. This study comprehensively reviews the application of hyperspectral imaging and machine learning technologies in waste classification. This includes traditional unsupervised classification methods, supervised classification methods, and deep learning methods. Commonly used metrics for evaluating classification accuracy are summarized. Finally, the current challenges are highlighted, and future research directions are explored. Hyperspectral imaging ; Identification (biology) ; Sorting ; Contextual image classification ; Resource (disambiguation) ; RGB color model Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Water Quality Monitoring and Analysis
Authors: Lijie Ding | Changwoo Do
Source: APL Machine Learning (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Deciphering the small-angle scattering of polydisperse hard spheres using deep learning We introduce a deep learning approach for analyzing the scattering function of the polydisperse hard sphere system. We use a variational autoencoder-based neural network to learn the bidirectional mapping between the scattering function and the system parameters, including the volume fraction and polydispersity. Such that the trained model serves both as a generator that produces a scattering function from the system parameters and an inferrer that extracts system parameters from the scattering function. We first generate a scattering dataset by carrying out molecular dynamics simulations of the polydisperse hard spheres modeled by the truncated-shifted Lennard-Jones model, then analyze the scattering function dataset using singular value decomposition to confirm the feasibility of dimensional compression. Then, we split the dataset into training and testing sets and train our neural network on the training set only. Our generator model produces a scattering function with significantly higher accuracy compared to the traditional Percus–Yevick approximation and β correction, and the inferrer model can extract the volume fraction and polydispersity with much higher accuracy than traditional model functions. Scattering ; Autoencoder ; Function (biology) ; Artificial neural network ; Hard spheres ; SPHERES ; Deep learning ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Optics ; Quantum mechanics Material Dynamics and Properties ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics