jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Guo | Marcos F. Calegari Andrade | Christopher Hahn | Ambarish Kulkarni | Nitish Govindarajan
Source: ChemRxiv (preprint, 2025-04-10)
Field: Chemistry — Electrochemistry

Understanding cation and surface charging effects at electrified interfaces using neural network interatomic potentials Electrified interfaces are at the heart of electrochemical processes related to several technologically important applications, yet they remain poorly understood. This lack of understanding is primarily due to their complex and dynamic nature, that poses significant challenges in developing realistic models. To overcome some of these challenges, we train message-passing neural network interatomic potentials based on the MACE architecture to perform long time-scale molecular dynamics (MD) simulations (O(ns)) of electrified Au interfaces with near-first-principles accuracy. Specifically, we study the impact of surface charging and cation identity (Li+, Na+, K+, Cs+) to obtain detailed insights into various interfacial properties, including the structure and dynamics of (near surface) water molecules, cations, and the double layer capacitance. In general, we find that surface charging has a greater impact on these interfacial properties compared to that of the cation identity. Furthermore, we find that the estimated double layer capacitance from MACE/MD simulations (17-19 muF/cm2) at cathodic potentials is largely insensitive to the cation identity. Our work demonstrates the potential of message passing neural network potentials to obtain a detailed atomistic-level understanding of electrified interfaces. Artificial neural network ; Surface (topology) ; Chemical physics ; Interatomic potential ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Molecular dynamics ; Mathematics ; Geometry Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Fukang Xing | Chengzong Dai | Lingfeng Meng | Songfeng Wang | Jie Ren | Rongguang Zhu | Kai Wei
Source: Industrial Crops and Products (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

Fusing band and feature attention in CNN-LSTM: A dual-attention framework for Hyperspectral-based precise prediction of nicotine levels in cured tobacco leaves Rapid and reliable quantification of key chemical constituents in cured tobacco leaves remains a critical challenge in industrial processing. This study introduced an intelligent prediction framework that combines hyperspectral imaging (HSI) with a deep neural network model enhanced by dual-attention modules. A multi-region spectral acquisition strategy was employed to capture representative reflectance data from different leaf areas. To improve feature learning, a Dual-Attention-enhanced CNN-LSTM (DACL) model was developed by incorporating a Band Attention (BA) module for adaptive spectral band selection and a Self-Attention (SA) module for deep feature refinement. The DACL model was trained on multi-region averaged spectra and systematically evaluated against classical machine learning models (PLSR, SVR) and deep learning baselines (CNN, LSTM, CNN-LSTM). The DACL approach achieved superior performance, with an R² of 0.857, an RMSE of 0.443, and an RPD of 2.642 on the independent test set. Visualization of the attention modules confirmed that the model consistently prioritized wavelengths corresponding to nicotine’s characteristic absorption bands, thereby enhancing both predictive accuracy and interpretability. The results demonstrate that the fusion of spectral and deep feature attention within a CNN-LSTM architecture, together with multi-ROI spectral acquisition, provides a robust and interpretable solution for real-time nicotine assessment in cured tobacco. The proposed method offers new insights for quality control in tobacco processing and highlights the broader potential of AI-driven hyperspectral analysis in agricultural product evaluation. • Multi-region information of cured tobacco leaf was obtained and analyzed by modeling. • Dual-attention enhanced CNN-LSTM (DACL) model was developed to detect nicotine content. • Dual-attention module was analyzed by feature visualization and model comparison. • DACL model achieved good prediction results with R 2 of 0.857, RPD of 2.64. Hyperspectral imaging ; Feature (linguistics) ; Pattern recognition (psychology) ; Feature selection ; Artificial neural network ; Deep learning ; Reflectivity ; Sensor fusion Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Khowshik Dey | Murat Barışık | Liang Yu
Source: Journal of Molecular Liquids (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Machine learning-guided reparameterization of the TIP4P water model for accurate thermal and electrical property predictions Overfitting ; Water model ; Dielectric ; Work (physics) ; Thermal conductivity ; Thermal ; Thermal diffusivity ; Molecular dynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Yuqing Ding | You Kuai | R.-L. Li | Xinzhao Xu | Bo Wang | Zhihui Wang | Yanfang Liu | Yuchao Dong | Shunjie Chen | Meng Guo | Yunqi Liu | Yan Zhao
Source: Advanced Science (article, 2025-08-04)
Field: Materials Science — Polymers and Plastics

Aptamer‐Mediated Artificial Synapses for Neuromorphic Modulation of Inflammatory Signaling via Organic Electrochemical Transistor Artificial synaptic devices that mimic neuromorphic signal processing hold great promise for bioelectronic interfaces. However, most systems remain limited to physical stimuli or electroactive small molecules, lacking the ability to transduce biologically relevant protein signals. To address this limitation, an aptamer-mediated aqueous artificial synaptic transistor is developed capable of selectively responding to the interleukin-6 (IL-6) signal, a specifically expressed protein of inflammatory stress, via gate-voltage-induced synaptic modulation in biologically relevant electrolyte environments. Guided by molecular docking simulations, high-affinity aptamer sequences are identified for robust recognition of IL-6. The device demonstrates precise IL-6 capture and translation into neuromorphic electrical signals across various biological electrolytes (PBS, albumin, serum), with linear detection from 0.5 pm to 50 nm. Moreover, the device can convert IL-6 binding events into time and concentration-dependent electrical outputs, exhibiting significant synaptic plasticity and memory retention. When implanted into the caudal vein of sepsis mice, the device stably monitors IL-6 level and maintains reliable synaptic response to inflammatory-triggered elevations. Machine learning analysis enables accurate discrimination between normal and pathological states from device-generated signals. By bridging biochemical signals with neuromorphic encoding, this system outlines a conceptual framework for future integration between artificial and biological neural units, contributing to the hybrid neurosensory systems. Neuromorphic engineering ; Aptamer ; Computer science ; Modulation (music) ; Materials science ; Neuroscience ; Nanotechnology ; Artificial neural network ; Artificial intelligence ; Biology ; Physics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: Bowen Wang | Baowen Wang | Hejin Yan | Yongqing Cai
Source: Materials Today Physics (article, 2025-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Oxygen vacancy-driven interfacial alloying and mixing for enhanced heat transfer in gallium oxide Materials science ; Gallium oxide ; Gallium ; Mixing (physics) ; Oxygen ; Oxide ; Vacancy defect ; Metallurgy ; Chemical engineering ; Thermodynamics ; Crystallography Ga2O3 and related materials ; ZnO doping and properties ; Thermal properties of materials
Authors: Sai Pranav Reddy Guduru | Mkpe O. Kekung | Ryan Ott | Sougata Roy | Prashant Singh
Source: Acta Materialia (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys Brittleness ; Alloy ; Ductility (Earth science) ; Generative grammar ; Transformer ; High fidelity ; Hardening (computing) ; Turbine blade Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Khalil Benabderazag | Moussa Guebailia | Zouheyr Belouadah | Lotfi Toubal | Salah Eddine Tachi
Source: Fibers (article, 2025-03-31)
Field: Materials Science — Biomaterials

Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R2) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R2 &gt; 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring. Materials science ; 3d printed ; Composite material ; Nanotechnology ; Manufacturing engineering ; Engineering biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Natural Fiber Reinforced Composites
Authors: Jingyi Wei | Sixian Yu | Tianxiang Zhou | Jing Shang | Songling Liu | Feng Han | Xiao-Dong Li | Qi An
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

<i>In situ</i> SERS and <i>in situ</i> Raman: deciphering interfacial phenomena and processes Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are techniques for obtaining fingerprint-like vibrational spectral information from specific substrate surfaces. Attributed to its high sensitivity towards molecular electron density distributions, Raman or SERS employed during chemical reactions can assist in analyzing detailed chemical conversion processes. However, multiple influencing factors impede the development of <i>in situ</i> SERS substrates and in-depth data analysis. To systematically lay out the key issue to facilitate <i>in situ</i> SERS probing, we document the basic principles and recent advances in <i>in situ</i> Raman/SERS for studying surface reactions. The mechanism and influencing factors of SERS are first briefly introduced, and the types of interfacial phenomena, including the transition of species, the alteration of electron densities, the alteration of crystalline structures, are discussed in this review. We have systematically compiled species identifications and Raman spectral features reported in recent <i>in situ</i> studies, which are expected to serve future investigations, particularly those involving advanced computational analysis such as machine learning. Subsequently, the <i>in situ</i> Raman/SERS study of various types of surface reactions is performed. Finally, the challenges and perspectives of <i>in situ</i> SERS in deciphering surface processes are laid out, proposing our insights into future directions and challenges. We expect this review to spread out novel ideas concerning <i>in situ</i> SERS and inspire further studies regarding <i>in situ</i> SERS techniques or surface reaction processes. Raman spectroscopy ; In situ ; Materials science ; Substrate (aquarium) ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Analytical Chemistry (journal) ; Raman scattering ; Chemistry ; Optics ; Organic chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications
Authors: M. Engelhardt | Finn Mier | Markus O. Zimmermann | Frank M. Boeckler
Source: Journal of Chemical Information and Modeling (article, 2025-11-25)
Field: Chemistry — Physical and Theoretical Chemistry

A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen-π Interactions by Training Neural Networks Noncovalent interactions, such as halogen bonds (XB), play a crucial role in molecular recognition and drug design, yet halogen···π contacts remain comparatively underexplored. Here, we report a proof-of-concept QM-AI approach that integrates high-level quantum mechanical (QM) calculations with neural networks (NNs) to predict halogen···π interaction energies. Nearly 1.4 million MP2/TZVPP single-point calculations on halobenzene-benzene complexes were carried out to generate exhaustive training data, which were represented by simple geometric descriptors as input features for machine learning. The resulting neural network model is specifically designed to capture σ-hole-driven halogen···π interactions under well-defined geometric constraints. The resulting model reproduced reference interaction energies with excellent accuracy (<i>R</i><sup>2</sup> = 0.998, RMSE = 0.16 kJ/mol) and maintained strong performance on independent, randomly generated and PDB-derived test sets. Previously, we have demonstrated in a benchmarking study that "gold standard" CCSD(T) energies of this interaction can be appropriately represented by MP2/TZVPP calculations, but at a better calculation efficiency by 2 orders of magnitude (∼10<sup>2</sup>). Consequently, we herein exploit a methodological "extension" from CCSD(T) → MP2 → NNs. Our approach maintains accuracy close to CCSD(T) benchmarks while achieving a runtime acceleration of up to 8 orders of magnitude (∼10<sup>8</sup>) compared to MP2 calculations. This study demonstrates the feasibility of fast, accurate neural network models based on QM data for halogen···π interactions in a QM-AI approach. Artificial neural network ; Benchmarking ; Acceleration ; Exploit ; Feature (linguistics) ; Mean squared error ; Quantum ; Simple (philosophy) Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Sukanlaya Kornnum | Praeploy Chomkhuntod | Nick Schwaiger | Kanwara Limcharoen | Krittapong Deshsorn | Kulpavee Jitapunkul | Pawin Iamprasertkun
Source: Analytical Chemistry (article, 2025-01-31)
Field: Materials Science — Polymers and Plastics

Voltammetry Prediction and Electrochemical Analysis of Carbon Material from “Salt-In-Water” to “Water-In-Salt” Cyclic voltammetry (CV) is a standard method for assessing electrochemical properties in the electrochemical cells, typically in conventional aqueous contexts like 1 <i>m</i> solutions ("salt-in-water"). However, recent advancements have extended electrochemistry into superconcentrated regimes, such as "water-in-salt" solutions with concentrations above 10 to 20 <i>m</i>, which require large amounts of salt for experiments. To address this, machine learning (ML) has been applied, coupled with in-house data collection using lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) electrolytes. This work demonstrates the electrochemistry of YEC-8B in LiTFSI, given their broad potential window of up to 3.0 V across concentrations from 1 to 20 <i>m</i>. The CV profiles were divided into two models: the upper curve for charging and the lower curve for discharging. Data were normalized and segmented by percentiles, and a decision tree model was developed to predict outputs based on input parameters like LiTFSI concentration, scan rates, and potential window. The model predicted nine target variables with a mean absolute percentage error of approximately 2% for both the upper and the lower CV profile curves. Trapezoidal rule was then used to calculate the system's capacitance. Additionally, tests showed a 75% accuracy in predicting the potential window and a suitable scan rate. Overall, the model effectively demonstrated the relationship between "water-in-salt" electrolytes and CV profiles in an electrochemical context using a simple machine learning (ML) algorithm, which continues to expand the integration of data science and electrochemistry. Electrochemical window ; Chemistry ; Electrochemistry ; Horizontal scan rate ; Electrolyte ; Cyclic voltammetry ; Context (archaeology) ; Lithium (medication) ; Salt (chemistry) ; Analytical Chemistry (journal) ; Aqueous solution ; Biological system ; Electrode ; Chromatography Conducting polymers and applications ; Electrochemical Analysis and Applications ; Supercapacitor Materials and Fabrication
Authors: S. Chellaiya Thomas Rueshwin | R.D. Eithiraj
Source: Results in Engineering (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Data-driven insights into the electronic and thermoelectric properties of 1T-Li2O: A combined DFT and ML investigation • Dynamical stability was confirmed through phonon dispersion analysis. • Thermal stability was ascertained using Ab-initio Molecular Dynamics (AIMD) simulations. • Machine learning model (ML) was developed to determine the band gap. • The ratio of these effective masses suggests a low recombination rate, advantageous for thermoelectric energy generation. To examine the physical attributes of the 1T-Li 2 O, Density Functional Theory (DFT) was incorporated utilizing WIEN2k software. Dynamical stability was confirmed through phonon dispersion analysis, thermal stability was ascertained using Ab-initio Molecular Dynamics (AIMD) simulations, and thermodynamic stability was verified by calculating the cohesive energy. Specifically, to assess the electronic property, generalized gradient approximation (GGA) and hybrid exchange-correlation functionals were implemented. This study identified the 1T-Li 2 O as a direct, wide band gap semiconductor, with values of 2.56 eV and 3.74 eV obtained implementing GGA and hybrid functionals, respectively. A machine learning model (ML) was developed to determine the band gap. The predicted band gap values were 2.45 eV using random forest regression and 2.41 eV using linear regression. The physical properties were tailored by the impact of strain on the 1T-Li 2 O were studied. The variation in the band gap and hybridization were investigated. Optical analysis indicated a strong absorption coefficient, underscoring the 1T-Li 2 O monolayer’s potential for optoelectronic applications in the UV region for both strained and unstrained structures. Additionally, the material demonstrated promising thermoelectric properties, at 1000 K achieving a figure of merit (ZT) of 0.95 for unstrained structure, indicating its suitability for waste heat recovery. A ML model was developed to determine the ZT of 1T-Li 2 O using random forest regression and linear regression. The effective masses and the carrier mobilities of electrons and holes (heavy and light) were calculated, and the ratio of these effective masses suggests a low recombination rate, advantageous for thermoelectric energy generation. Thermoelectric effect ; Materials science ; Engineering physics ; Nanotechnology ; Physics ; Thermodynamics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Inalegwu S. Apeh | Thecla Okeahunwa Ayoka | Charles O. Nnadi | Wilfred Ofem Obonga
Source:  (article, 2025-04-21)
Field: Chemistry — Organic Chemistry

Modeling the Quantitative Structure–Activity Relationships of 1,2,4-Triazolo[1,5-a]pyrimidin-7-amine Analogs in the Inhibition of Plasmodium falciparum Triazolopyrimidine and its analogs represent an important scaffold in medicinal chemistry research. The heterocycle of 1,2,4-triazolo[1,5-a] pyrimidine (1,2,4-TAP) serves as a bioisostere candidate for purine scaffolds, N-acetylated lysine, and carboxylic acid. This study modeled the quantitative structure–activity relationship (QSAR) of 125 congeners of 1,2,4-TAP from the ChEMBL database in the inhibition of Plasmodium falciparum using six machine learning algorithms. The most significant features among 306 molecular descriptors, including one molecular outlier, were selected using recursive feature elimination. A ratio of 20% was used to split the x- and y-matrices into 99 training and 24 test compounds. The regression models were built using machine learning sci-kit-learn algorithms (multiple linear regression (MLR), k-nearest neighbours (kNN), support vector regressor (SVR), random forest regressor (RFR) RIDGE regression, and LASSO). Model performance was evaluated using the coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), p-values, F-statistic, and variance inflation factor (VIF). Five significant variables were considered in constructing the model (p < 0.05) with the following regression equation: pIC50 = 5.90 − 0.71npr1 − 1.52pmi3 + 0.88slogP − 0.57vsurf-CW2 + 1.11vsurf-W2. On five-fold cross-validation, three algorithms—kNN (MSE = 0.46, R2 = 0.54, MAE = 0.54, RMSE = 0.68), SVR (MSE = 0.33, R2 = 0.67, MAE = 0.46, RMSE = 0.57), and RFR (MSE = 0.43, R2 = 0.58, MAE = 0.51, RMSE = 0.66)—showed strong robustness, efficiency, and reliability in predicting the pIC50 of 1,2,4-triazolo[1,5-a]pyrimidine. The models provided useful data on the functionalities necessary for developing more potent 1,2,4-TAP analogs as anti-malarial agents. Plasmodium falciparum ; Amine gas treating ; Chemistry ; Structure–activity relationship ; Combinatorial chemistry ; Stereochemistry ; Biology ; Biochemistry ; Malaria ; In vitro ; Organic chemistry ; Immunology Synthesis and biological activity ; Synthesis and Biological Evaluation ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Muhammad Faizan | Mohammad Nahid Siddiqui
Source: Materials Research Bulletin (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Advances in scalable 2D material synthesis: Flash Joule heating and beyond Joule heating ; Flash (photography) ; Scalability ; Materials science ; Joule (programming language) ; Engineering physics ; Nanotechnology ; Optoelectronics ; Computer science ; Physics ; Thermodynamics ; Composite material ; Optics ; Database Graphene research and applications ; MXene and MAX Phase Materials ; 2D Materials and Applications