jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Cheng | Jianqiao Chen | Ran Duan | Xiangcheng Yang | Jianfeng Yu | Qian Tang | Huabin Wang | Rui Xu
Source: Results in Engineering (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Hydrothermal extraction of chlorogenic acid from tobacco waste facilitated by Fe catalyst and optimization of process parameters • Fe-HTE efficiently extracted CA (20.31±0.12 mg/g) from TW with the organic solvents-free processes • Machine learning (ANN, R2=0.995) outperformed RSM (R2=0.930) in parameter optimization with minimal prediction error (CV=1.82%) • FeSO4 catalysis enhanced lignocellulose depolymerization via radical-mediated mechanisms while stabilizing CA against degradation. • The Fe-HTE method based circular economy strategy underutilized TW into high-value byproducts. Tobacco waste (TW) contains a significant amount of chlorogenic acid (CA), while conventional extraction methods suffer from low efficiency (normally yielding amount within 3.48-12.88 mg/g) and the potential solvent secondary pollution. This study introduced the Fe-assisted hydrothermal extraction method (Fe-HTE) to enhance CA recovery from TW. Further, the FeSO 4 was identified as an effective catalyst with the mechanistic speculations were that Fe-HTE processes promoted cell wall disruption and CA release through Fenton-like reactions and formation of complexations with leached CA. Moreover, based on Box-Behnken design, the interactive effects of major parameters, including hydrothermal temperature, time, pH, and FeSO 4 dosage, were optimized via both Artificial Neural Network (ANN) and Response Surface Methodology (RSM) models. The ANN model demonstrated significantly higher prediction accuracy (R 2 = 0.995) than the RSM model (R 2 = 0.930). The optimal process parameters were determined as hydrothermal temperature was 153°C, time was 71 min, pH was 3.5, and FeSO 4 to TW mass ratio was 24 mg/g, respectively, resulted in CA yielding amount reached 20.31 ± 0.12 mg/g (Coefficient of variation 1.82%, mean absolute deviation 4.461%). The method was optimized through a synergistic process involving catalysis and the machine learning model. This work not only converted TW into high-value byproducts but also reduced organic solvents consumption, offering a green, mild and potentially scalable pathway for sustainable resource recovery from agricultural wastes. Chlorogenic acid ; Response surface methodology ; Catalysis ; Hydrothermal circulation ; Extraction (chemistry) ; Depolymerization ; Process (computing) Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Extraction and Separation Processes
Authors: Chengkai Jin | Xunhua Zhao
Source: ChemRxiv (preprint, 2025-05-23)
Field: Materials Science — Materials Chemistry

Screening of Electrocatalysts at the Level of Kinetic Barriers under Realistic Potential and Solvation: eNRR as a Case Study Kinetic barriers under realistic solvation and potential conditions have been found to play critical roles in electrochemical reactions, however they have not been applied in massive screening of electrocatalysts mainly due to the high computational cost to obtain them. Here we demonstrate how to establish a quantitative relation between thermodynamics and kinetic barriers, and use it to guide electrocatalyst screening from a large number of candidates, taking single-atom@coinage-metal (M1@CM) alloys catalyzing electrochemical nitrogen reduction reaction (eNRR) as an example. For CM = Cu or Ag or Au, separated linear relations are found, indicating good scaling relations between the free energy changes based on the computational hydrogen electrode model and the kinetic barriers calculated from the constant-potential hybrid-solvation dynamic model. Furthermore, we show a unified mapping from ΔG to Ea is accessible with prediction error about 0.05 eV across the three hosts using machine learning regression methods. Based on these relations, the high-active zone is identified: Re1@Ag, Mo1@Ag, and Re1@Cu are predicted to have the highest eNRR activity, where the representative Re1@Ag is selected to assess kinetic barriers of the full eNRR path. Indeed it exhibits all barriers no higher than 0.85 eV, significantly lower than other reported systems if barriers of all steps are examined. This work not only presents a new screening workflow to find an extraordinary eNRR catalyst, but also demonstrates how to employ the quantitative relation between thermodynamics and kinetic barriers from constant-potential ab initio molecular dynamics to significantly accelerate accurate screening of electrocatalysts. Solvation ; Kinetic energy ; Chemistry ; Materials science ; Chemical physics ; Nanotechnology ; Thermodynamics ; Physics ; Ion ; Classical mechanics ; Organic chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Jiawen Liang | Li Wang | Dandan Kong | Jia Li | Yongzheng Ma | Chenglin Wang | Lixia Li | Zhenhuan Liu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

ATR-FTIR spectroscopy coupled with deep learning for the identification and quantitative detection of Panax notoginseng adulteration Panax notoginseng ; Adulterant ; Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Identification (biology) Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Ginseng Biological Effects and Applications
Authors: Rania Gamal | A. V. Hamza | Abdelgwad Elashry
Source: Journal of Energy Storage (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Hybrid ML–EIS framework for ionic conductivity prediction in solid-state battery electrolytes Overfitting ; Principal component analysis ; Ionic conductivity ; Dielectric spectroscopy ; Dimensionality reduction ; Curse of dimensionality ; Mean squared error ; Electrolyte ; Conductivity Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Chen Li (54018) | Yinqiao Zhang (22601086) | Fanxing Zhang (18426805) | Yichun Li (12419116) | Yao Shi (1774876) | Xiaoyong Cao (4401154) | Chunlei Wei (11619960) | Nan Xu (107466) | Yi He (408132)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Discovery of Solubilizersto Enhance the SwellingKinetics of Epoxy SU‑8 via Virtual Screening The slow swelling kinetics of SU-8 epoxy resist hinders its removal in semiconductor fabrication processes. To accelerate solubilizer discovery for swelling, we developed a virtual screening framework integrating molecular dynamics (MD) and machine learning. Using the diffusion coefficient within SU-8 as a key descriptor for solubilizer efficacy, we generated an MD-derived data set to train a graph convolutional neural network (GCN), enabling diffusivity predictions across >20 million compounds. This pipeline identified 4,6-decadienal (4,6-DDA) as a promising candidate. Experimentally, a 10 wt % aqueous solution of 4,6-DDA achieved 80.16% SU-8 swelling within 60 min, which was 3-fold greater than that of propylene carbonate. Simulations confirmed rapid 4,6-DDA penetration and enhanced performance in water, which is consistent with experimental measurements. This work demonstrates a rational simulation-guided strategy for the discovery of functional molecules for industrial processes. Swelling ; Kinetics ; Epoxy ; Virtual screening ; Thermal diffusivity ; Penetration (warfare) ; Fabrication ; Aqueous solution ; Convolutional neural network Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Epoxy Resin Curing Processes
Authors: Tianle Yue (16405553) | Danh Nguyen (6587579) | Vikas Varshney (1458727) | Ying Li (38224)
Source: Figshare (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Assessing the Effectivenessof Neural Networks andMolecular Dynamics Simulations in Predicting Viscosity of Small OrganicMolecules Viscosity is a crucial material property that influences a wide range of applications, including three-dimensional (3D) printing, lubricants, and solvents. However, experimental approaches to measuring viscosity face challenges such as handling multiple samples, high costs, and limited compound availability. To address these limitations, we have developed computational models for viscosity prediction of small organic molecules, utilizing machine learning (ML) and nonequilibrium molecular dynamics (NEMD) simulations. Our ML framework, which includes feed-forward neural networks (FNN) and physics-informed neural networks (PINN), is based on the largest data set of small molecule viscosities compiled from the literature. The PINN model, in particular, incorporates temperature dependence through a four-parameter model, allowing for the direct prediction of continuous temperature-dependent viscosity curves. The ML models demonstrate exceptional prediction accuracy for the viscosity of various organic compounds across a wide range of temperatures. External validation of our models further confirms that the ML prediction models outperform the NEMD approach in predicting viscosity across a diverse range of organic molecules and temperatures. This highlights the potential of ML models to overcome limitations in traditional MD simulations, which often struggle with accuracy for specific molecules or temperature ranges. Our further feature importance analysis revealed a strong correlation between molecular structure and viscosity. We emphasize the key role of substructures in determining viscosity, offering deeper molecular insights for material design with tailored viscosity. Viscosity ; Artificial neural network ; Molecular dynamics ; Range (aeronautics) ; Organic molecules ; Set (abstract data type) ; Molecular descriptor ; Feature (linguistics) ; Molecule Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: wenchao ji | Yuwei Fu | hankai zhang | Chenhui Zhou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Enhanced Multiscale Modeling of High-Rate Sodium-Ion Batteries Integrating Electrochemical Dynamics and Thermal Safety Analysis Electrochemistry ; Ion ; Thermal ; Thermal analysis ; Sodium ; Computer science ; Dynamics (music) ; Materials science ; Nuclear engineering ; Chemistry ; Engineering ; Thermodynamics ; Physics ; Metallurgy ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Sherzod Korabayev | Abrorjon Homidjonov | Kh. G. Soloev | Nuriddin Musayev | Orinboy Quryozov
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

AI-driven discovery of high-efficiency perovskite-organic hybrid materials for next-generation photovoltaics This study introduces an innovative artificial intelligence (AI) framework designed to accelerate the discovery of highefficiency perovskite-organic hybrid materials for next-generation photovoltaics. We use a generative Crystal Diffusion Variational Autoencoder (CDVAE) and a predictive Graph Neural Network (GNN) to help us come up with new crystal structures from scratch and test them quickly in a large chemical space that can't be reached by traditional experimental methods. The CDVAE could make a lot of different perovskite-organic hybrids, up to a million. The GNN, on the other hand, was able to make accurate predictions about important properties like thermodynamic stability (formation energy) and power conversion efficiency (PCE). It did this with very high coefficients of determination (R<sup>2</sup> &gt; 0.9) on test data that was not used for training. Through multi-objective optimisation and high-throughput virtual screening, we found a number of good candidates. These candidates were compositions that contained fluorinated aromatic organic cations and had DFT-validated PCEs greater than 23% and significantly negative formation energies. This meant that they could be made and would stay stable. This all-encompassing AI-driven strategy cuts the time it takes to find new materials from years to just weeks. This means that you don't have to use costly and time-consuming empirical methods as much. The results indicate that integrating generative and predictive machine learning models may address the stability-efficiency trade-off in perovskite solar cells. This could lead to the independent discovery of new energy materials. Artificial neural network ; Autoencoder ; Stability (learning theory) ; Generative grammar ; Photovoltaics ; Crystal structure prediction ; Hybrid system ; Chemical space ; Hybrid material Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Jinzhe Zeng (9852355) | Duo Zhang (148835) | Anyang Peng (5039447) | Xiangyu Zhang (630437) | Sensen He (21237890) | Yan Wang (15435) | Xinzijian Liu (4859956) | Hangrui Bi (18846514) | Yifan Li (327507) | Chun Cai (125495) | Chengqian Zhang (1540024) | Yiming Du (9075528) | Jia-Xin Zhu (11197608) | Pinghui Mo (21237893) | Zhengtao Huang (21237896) | Qiyu Zeng (12457668) | Shaochen Shi (12431620) | Xuejian Qin (21237899) | Zhaoxi Yu (11050938) | Chenxing Luo (21237902) | Ye Ding (40594) | Yun-Pei Liu (10529907) | Ruosong Shi (21237905) | Zhenyu Wang (580934) | Sigbjørn
Løland Bore (4803612) | Junhan Chang (16844245) | Zhe Deng (291456) | Zhaohan Ding (21237908) | Siyuan Han (9609478) | Wanrun Jiang (7066433) | Guolin Ke (12122175) | Zhaoqing Liu (18103613) | Denghui Lu (13200650) | Koki Muraoka (2632864) | Hananeh Oliaei (14333592) | Anurag Kumar Singh (8232771) | Haohui Que (13258896) | Weihong Xu (22366) | Zhangmancang Xu (19324363) | Yong-Bin Zhuang (15853262) | Jiayu Dai (2868452) | Timothy J. Giese (1314369) | Weile Jia (5131736) | Ben Xu (1497229) | Darrin M. York (58145) | Linfeng Zhang (4568230) | Han Wang (254423)
Source: Figshare (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

DeePMD-kit v3:A Multiple-Backend Framework for MachineLearning Potentials In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks, such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of the DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multibackend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLP packages and of differentiable molecular force fields. This architecture allows seamless back-end switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Interoperability ; Architecture ; Software ; Face (sociological concept) ; System integration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Protein Structure and Dynamics
Authors: Wei Jia | Mingyu Zhang | Shuai Li | Shu Li
Source: Crystals (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation The growth velocity of the crystal–melt interface during solidification is one of the important parameters that determine the crystal growth morphology. However, both experimental investigations and theoretical calculations are time-consuming and labor-intensive. Moreover, machine learning (ML)-based methods are severely limited by the limited amount of available experimental data. In this work, the crystal–melt interface velocity of four alloy systems under different values of undercooling was calculated by molecular dynamics simulation. The results showed a similar trend to the experimental data. A framework including molecular dynamics (MD) calculation and a transfer learning (TL) model was proposed to predict the interface velocity of binary alloys during free solidification. In order to verify the effectiveness of the model, eight ML models were constructed based on pure experimental data for model comparison. The prediction ability of the different models was assessed from two perspectives: interpolation and extrapolation. The results show that, regardless of whether it is interpolation or extrapolation, the TL model driven by both physical information and experimental data is superior to ML models driven solely by experimental data. The interpretability analysis method reveals the specific role of feature values in the interface velocity prediction of binary alloys. Dendrite (mathematics) ; Growth velocity ; Molecular dynamics ; Dynamics (music) ; Binary number ; Materials science ; Supercooling ; Statistical physics ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Computational chemistry ; Biology Solidification and crystal growth phenomena ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties
Authors: Massimiliano Lupo Pasini | Pei Zhang | Jong Youl Choi | German Samolyuk | Markus Eisenbach | Ying Yang
Source: ChemRxiv (preprint, 2025-01-09)
Field: Materials Science — Materials Chemistry

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys. Ternary operation ; Tantalum ; Niobium ; Materials science ; Vanadium ; Transferability ; Binary number ; Thermodynamics ; Density functional theory ; Refractory metals ; Enthalpy ; Computer science ; Chemistry ; Computational chemistry ; Metallurgy ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Puneet Mishra | Martijntje Vollebregt | Yizhou Ma | Maria Font‐i‐Furnols
Source: ArXiv.org (preprint, 2025-11-06)
Field: Materials Science — Materials Chemistry

A MATLAB tutorial on deep feature extraction combined with chemometrics for analytical applications Background In analytical chemistry, spatial information about materials is commonly captured through imaging techniques, such as traditional color cameras or with advanced hyperspectral cameras and microscopes. However, efficiently extracting and analyzing this spatial information for exploratory and predictive purposes remains a challenge, especially when using traditional chemometric methods. Recent advances in deep learning and artificial intelligence have significantly enhanced image processing capabilities, enabling the extraction of multiscale deep features that are otherwise challenging to capture with conventional image processing techniques. Despite the wide availability of open-source deep learning models, adoption in analytical chemistry remains limited because of the absence of structured, step-by-step guidance for implementing these models. Results This tutorial aims to bridge this gap by providing a step-by-step guide for applying deep learning approaches to extract spatial information from imaging data and integrating it with other data sources, such as spectral information. Importantly, the focus of this work is not on training deep learning models for image processing but on using existing open source models to extract deep features from imaging data. Significance The tutorial provides MATLAB code tutorial demonstrations, showcasing the processing of imaging data from various imaging modalities commonly encountered in analytical chemistry. Readers must run the tutorial steps on their own datasets using the codes presented in this tutorial. Deep learning ; Hyperspectral imaging ; Image processing ; Feature extraction ; MATLAB ; Focus (optics) ; Pattern recognition (psychology) ; Artificial neural network Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Zulman Arif Gani
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Accelerated Material Phonon Property Prediction in Limited Data Regime Through a Robust Context-Gated GCN Hybrid Architecture ABSTRACT Prediction of lattice vibration frequencies (phonon density of states peaks) is a fundamental parameter in the design of thermal and electronic materials. Ab-initio simulation methods such as Density Functional Theory (DFT) offer high accuracy but are hampered by high computational costs, especially for large-scale material screening. Graph-based Deep Learning approaches (Graph Neural Networks/GNNs) offer fast solutions, but often suffer from convergence instability (gradient explosion) and low accuracy when trained on small, highly variable datasets. This study proposes a hybrid architecture: Context-Gated Robust GCN. This method integrates (1) a Graph Convolutional Network stabilized by target normalization and gradient pruning, (2) an adaptive Gating Mechanism that modulates crystal features based on environmental context, and (3) Gradient Boosting (XGBoost) as the final predictor. Evaluation on the Matbench Phonons dataset shows that this hybrid method achieves stable convergence with an R2 score of around 0.88 (depending on the data split), significantly outperforming a complex GNN model without stabilization. Residual analysis shows a normal error distribution, validating the model's reliability as a fast material pre-selection tool. Keywords: Materials Informatics, Graph Neural Networks, XGBoost, Phonons, Hybrid AI, Small-Data Regime. Boosting (machine learning) ; Residual ; Normalization (sociology) ; Convolutional neural network ; Phonon ; Computation ; Lattice (music) ; Convergence (economics) Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices