jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shamoon Tariq | Madiha Sardar | Muhammad Shafiq | Muhammad Huzaifa | Mohammad Nur-e-Alam | Yan Wang | Zaheer Ul‐Haq
Source: ACS Omega (article, 2025-05-07)
Field: Chemistry — Organic Chemistry

Molecular Insights into the Binding of Organophosphate Flame Retardant Key Metabolites with Mineralocorticoid and Estrogen Receptors Flame retardants (FR) encompass a wide range of chemicals designed to inhibit and reduce the spread of fire by forming protective layers on materials. While originally considered relatively safe due to their rapid metabolism, growing evidence indicates that organophosphate flame retardants (OPFRs) can be extensively released into the environment, leading to toxic effects in humans, particularly endocrine disruption. Although the endocrine-disrupting potential of OPFRs is well-documented, the mechanisms through which their metabolites exert toxic effects remain largely unexplored. In this study, a comprehensive computational framework incorporating molecular docking, density functional theory, and all-atom molecular dynamic simulations were employed to investigate the binding and interactions of three key OPFR metabolites, BCIPP, BDCIPP, and DPHP, with human estrogen receptors (ER) and mineralocorticoid receptors (MR). The results revealed that these metabolites formed stable and compact complexes with both MR and ER, although high per residue atomic fluctuations were observed in ER complexes, likely due to the reactive nature of the metabolites. Binding free energy analysis further indicated favorable interactions between the OPFR metabolites and target receptors. Principal component analysis, leveraging machine learning algorithms, showed consistent motion, while free energy profiles demonstrated stable energy basins with minimal variations. These findings suggest that OPFR metabolites have strong binding affinities with MR and ER, hinting at their potential endocrine-disrupting effects at the molecular level. This study lays the groundwork for future research into the hazards posed by OPFR metabolites. Organophosphate ; Fire retardant ; Mineralocorticoid ; Estrogen receptor ; Chemistry ; Key (lock) ; Endocrinology ; Receptor ; Internal medicine ; Pharmacology ; Biochemistry ; Medicine ; Biology ; Organic chemistry ; Pesticide Chemical Thermodynamics and Molecular Structure ; Free Radicals and Antioxidants ; Thermal and Kinetic Analysis
Authors: Ignacio Sanjuán | David Franco | Qun‐Gao Chen | Chu‐Chen Chueh | Wen‐Ya Lee | Antonio Guerrero
Source: ACS Energy Letters (article, 2025-10-02)
Field: Materials Science — Polymers and Plastics

Proton Migration-Modulated n-Doped Poly(benzodifurandione) Organic Electrochemical Transistors Used for Neuromorphic Computing Applications Neuromorphic computing has emerged as a promising technology that can overcome the limitations that traditional systems face in the Big Data era. Organic electrochemical transistors (OECTs) are potential candidates for artificial synapses in neuromorphic hardware. However, due to their ambient instability, n-type OECTs have not been successfully applied to date in organic artificial synapses, limiting the fabrication of complementary logic circuits. In this work, we prove the potential of the n-doped poly-[benzodifurandione] (referred to in the literature as PBFDO or n-PBDF) polymer to fabricate high-performance n-type OECTs for neuromorphic applications using protons as the principal migrating ions. We demonstrate that n-PBDF-based OECTs show high stability and dual working modes (accumulation and depletion) in a NaPF<sub>6</sub> electrolyte. The devices exhibit resistive switching and synaptic plasticity promoted by the H<sup>+</sup> of the electrolyte. The n-PBDF OECTs also show high-quality long-term potentiation (LTP)/depression (LTD) behavior at low gate voltages (0.8 V) and short pulses (50-500 ms). The applicability of n-PBDF OECTs in neuromorphic computing is successfully validated by simulation with a deep neural network (DNN) model for handwritten digit recognition with different Gaussian noise levels. This work opens new avenues for the future development of n-type OECTs for building (bio)-electronic circuits, such as (bio)-sensing and neuromorphic computing. Neuromorphic engineering ; Transistor ; Artificial neural network ; Limiting ; CMOS ; Fabrication ; Resistive touchscreen ; Voltage Conducting polymers and applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: Roman-David Kulko | Andreas Hanus | Benedikt Elser
Source: Sensors (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Generative Artificial Intelligence for Synthetic Spectral Data Augmentation in Sensor-Based Plastic Recycling The reliance on deep learning models for sensor-based material classification amplifies the demand for labeled training data. However, acquiring large-scale, annotated spectral data for applications such as near-infrared (NIR) reflectance spectroscopy in plastic sorting remains a significant challenge due to high acquisition costs and environmental variability. This paper investigates the potential of large language models (LLMs) in synthetic spectral data generation. Specifically, it examines whether LLMs have acquired sufficient implicit knowledge to assist in generating spectral data and introduce meaningful variations that enhance model performance when used for data augmentation. Classification accuracy is reported exclusively as a proxy for structural plausibility of the augmented spectra; maximizing augmentation performance itself is not the study's goal. From as little as one empirical mean spectrum per class, LLM-guided simulation produced data that enabled up to 86% accuracy, evidence that the generated variation preserves class-distinguishing information. While the approach performs best for spectral distinct polymers, overlapping classes remain challenging. Additionally, the transfer of optimized augmentation parameters to unseen classes indicates potential for generalization across material types. While plastic sorting serves as a case study, the methodology may be applicable to other domains such as agriculture or food quality assessment, where spectral data are limited. The study outlines a novel path toward scalable, AI-supported data augmentation in spectroscopy-based classification systems. Computer science ; Artificial intelligence ; Sorting ; Machine learning ; Synthetic data ; Generative model ; Scalability ; Pattern recognition (psychology) ; Generative grammar ; Algorithm ; Database Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jun Gao | Zhenhuan Gao | Xue Fan | Sergei Manzhos | Tongyi Zhang | Yi Liu
Source: Journal of Applied Physics (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Transfer learning of high-dimensional features via attention-based embedding for Ni-based superalloys In feature engineering, one often reduces redundant dimensionality of raw feature space through selection. However, feature reduction poses a risk of removing critical yet unknown influence factors. In this work, we develop a general dimensionality augmentation approach for feature learning using attention-based embedding vectors constructed by transfer learning, to represent materials' compositions and elementary physiochemical descriptors beyond conventional concatenation to achieve more accurate prediction of materials properties. First, the high-dimensional embedded features [explicit Learnable Parameter Attention (LPA) and implicit Transformer-based Self-Attention (TSA)] were constructed by attention-based pretrained self-supervised learning using deep neural-network models [multi-layer perceptron (MLP)] with materials descriptor labels calculated analytically where attention scores can be used as feature importance. Next, the MLP models equipped with these embedded features were further fine-tuned by transfer learning to high-value property regions, which outperformed the MLP and tree-based baseline models with conventional features of concatenated compositions and descriptors in predicting melting temperatures of Ni-based superalloys as a case study. Finally, the inverse design via global optimization using a genetic algorithm (GA) combined with the embedded features (GA-MLP_LPA/TSA) was performed with the attention-based feature importance as a crossover probability. The integrated protocol surpassed the baseline Bayesian optimization in several statistical metrics. The proposed embedded feature models can capture correlations and couplings among features, providing a robust and efficient high-dimensional materials representation generally applicable to both forward property prediction and inverse materials design via machine learning. Feature (linguistics) ; Dimensionality reduction ; Concatenation (mathematics) ; Pattern recognition (psychology) ; Curse of dimensionality ; Embedding ; Bayesian optimization ; Multilayer perceptron Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Model Reduction and Neural Networks
Authors: Omer Kaspi | Yaniv Y. Avissar | Arnon Grafit | Ron Chibel | Olga Girshevitz | Hanoch Senderowitz
Source: Molecular Informatics (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Machine Learning‐Based Identification of Petroleum Distillates and Gasoline Traces Using Measured and Synthetic GC Spectra from Collected Samples Ignition cases involving arsons are typically handled by forensic experts who examine spectra of samples collected from scenes of fire to test for the existence or absence of ignitable liquids. This is tedious work, since many cases do not involve such liquids. To facilitate this process, we have developed in this work a Machine Learning (ML)-based workflow for samples' classification based on their gas chromatography (GC) chromatograms (i.e., spectra). To this end, annotated spectra of 181 samples containing three groups of liquids (petroleum distillates, gasoline, and an assortment of other substances) collected from fire scenes as well as two reference databases were obtained from the Israeli Department of Identification and Forensic Sciences (DIFS). These spectra were used for the derivation of ML-based classification models using three algorithms, namely, kNN, representative spectrum, and random forest (RF) giving rise to reliable predictions. To increase the size of the dataset to a level that would enable the usage of more advanced ML algorithms, we have used the experimental spectra to develop a new spectra synthesis algorithm and utilized it to generate a large dataset of synthetic spectra. This dataset was used for the derivation of new kNN, RF, and representative spectrum models as well as deep learning (DL) models producing F1-scores over an independent test set composed entirely of "real" spectra ranging from 0.74-0.95, 0.86-0.95, 0.30-0.75, and 0.85-0.96 for kNN, RF, representative spectrum, and DL, respectively. Following the completion of the work, a second set of real spectra was provided to us by DIFS, and modeling it with the second set of models yielded F1-scores ranging from 0.92-0.96, 0.96-1.00, 0.71-0.82, and 0.95-0.98 for kNN, RF, representative spectrum, and DL, respectively. These results therefore suggest that for this dataset, performances depend more on the size of the dataset used for model training than on the ML algorithm. We propose that the workflow and spectra synthesis algorithm developed in this work could be readily applied to other forensic domains where samples are characterized by spectra, either solely or in combination with other parameters. Gasoline ; Distillation ; Petroleum ; Identification (biology) ; Chromatography ; Chemistry ; Analytical Chemistry (journal) ; Environmental science ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yanning Yin | Stefan Willitsch
Source: Physical Review Applied (article, 2025-08-11)
Field: Chemistry — Spectroscopy

Ion counting and temperature determination of Coulomb-crystallized laser-cooled ions in traps using convolutional neural networks Coulomb crystals---ordered structures of cold ions confined in ion traps---have applications in a variety of research fields. The number and temperature of the ions forming the Coulomb crystals are two key attributes of interest in many trapped-ion experiments. Here, we present a fast and accurate approach to determining these attributes from fluorescence images of the ions based on convolutional neural networks (CNNs). In this approach, we first generate a large number of images of Coulomb crystals with different numbers of ions at different temperatures using molecular-dynamics simulations and then train CNN models on these images to classify the desired attributes. The classification performance of several common pretrained CNN models is compared in example tasks. We find that for crystals with numbers of ions in the range 100--299 and secular temperatures of 5--15 mK, the best-performing model can discern variations of a single ion with an accuracy of 93% and temperature changes of 1 mK with an accuracy of 92%. Since the trained models can be directly integrated into experiments, in situ determination of these attributes can be realized in a noninvasive fashion. This has the potential to greatly enhance the analysis and control of trapped ions in real time. Ion ; Coulomb ; Atomic physics ; Materials science ; Laser ; Convolutional neural network ; Physics ; Optics ; Nuclear physics ; Electron ; Quantum mechanics ; Computer science ; Machine learning Mass Spectrometry Techniques and Applications ; Cold Atom Physics and Bose-Einstein Condensates ; Atomic and Subatomic Physics Research
Authors: Liang Zhao | Fei Xu | David Porter | Yachun Wang
Source: Materials Characterization (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Quantification of line dislocations in FFTF irradiated HT9 cladding by deep learning method Materials science ; Cladding (metalworking) ; Irradiation ; Metallurgy ; Composite material ; Nuclear physics Nuclear Materials and Properties ; Fusion materials and technologies ; Superconducting Materials and Applications
Authors: Heng Zhao | Balázs D. Lőrincz | Tobias Henkes | Dénes Berta | Péter R. Nagy | Alexandre Tkatchenko | Stefan Vuckovic
Source: ChemRxiv (preprint, 2025-05-19)
Field: Materials Science — Materials Chemistry

Accurate Density Functional Theory for Non-Covalent Interactions in Charged Systems Accurately modeling non-covalent interactions (NCIs) involving charged systems remains an outstanding challenge in Density Functional Theory (DFT), with implications across natural and life sciences, engineering, e.g., in biochemistry, catalysis, and materials science. For these interactions, the interplay between electrostatics, polarization, and dispersion leads to systematic errors of up to tens of kcal/mol in standard dispersion-enhanced DFT methods. We solve this problem by introducing (r2SCAN+MBD)@HF, a DFT method without empirically fitted parameters that combines the r2SCAN functional and many-body dispersion, both evaluated on Hartree-Fock densities. We show that the unique synergy of these three components enables balanced treatment of short- and long-range correlation, which is crucial for accurate description of NCIs involving charged systems. Evaluations on standard benchmarks and a new Metal Ion Protein Clusters dataset introduced here show that (r2SCAN+MBD)@HF significantly improves accuracy for NCIs involving charged systems while maintaining robust performance for neutral systems. Given the ubiquity of such interactions, (r2SCAN+MBD)@HF is broadly applicable from biochemistry and materials science, including for generating high-quality data to train machine-learning force fields. Density functional theory ; Covalent bond ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Protein Structure and Dynamics
Authors: Hai Huang | Junhao Peng | K.K. Li | Jian Zhou | Zhimei Sun
Source: ArXiv.org (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate atomistic dynamic simulations. Trained by derivative-free optimization, the normal NEP achieves good accuracy, but suffers from inefficiency due to the high-dimensional parameter search. To overcome this problem, we present a gradient-optimized NEP (GNEP) training framework employing explicit analytical gradients and the Adam optimizer. This approach greatly improves training efficiency and convergence speedily while maintaining accuracy and physical interpretability. By applying GNEP to the training of Sb-Te material systems(datasets include crystalline, liquid, and disordered phases), the fitting time has been substantially reduced-often by orders of magnitude-compared to the NEP training framework. The fitted potentials are validated by DFT reference calculations, demonstrating satisfactory agreement in equation of state and radial distribution functions. These results confirm that GNEP retains high predictive accuracy and transferability while considerably improved computational efficiency, making it well-suited for large-scale molecular dynamics simulations. Transferability ; Convergence (economics) ; Training (meteorology) ; Inefficiency ; Limit (mathematics) ; Training set ; Distribution (mathematics) ; State (computer science) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: V. Villazón-León | Rosa Elba Sánchez Suárez | Adrián Bonilla‐Petriciolet | Juan Carlos Tapia-Picazo
Source: Fluid Phase Equilibria (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

A group contribution-based machine learning model to estimate the triple-point temperature Chemistry ; Triple point ; Group (periodic table) ; Point (geometry) ; Thermodynamics ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Yongheng Yan | Ruijie Xu | Zhiyu Zhao | Tingting Gao | Guangyi Shao | X. Lucas Lu | Chun-Shu Wei | Xueqing Zhao
Source: Food Chemistry X (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Integrating digital image analysis, flash GC E-nose, and SHAP-driven interpretable deep learning for non-destructive aging assessment of citri reticulatae pericarpium This study proposes a novel non-destructive method for evaluating the aging time and quality of citri reticulatae pericarpium (CRP) by integrating digital image and flash gas chromatography electronic nose (GC <i>E</i>-nose). Digital imaging was employed to extract color features and texture characteristics, while a self-developed one-dimensional convolutional neural network-gated recurrent unit (1D-CNN-GRU-Attention) deep learning model achieved an exceptional classification accuracy of 98.19 % for CRP samples aged between 0 and 12 years. SHapley Additive explanation (SHAP) analysis enhanced model transparency by identifying critical color and texture attributes influencing classification. Flash GC <i>E</i>-nose identified 33 volatile compounds, with Lasso and Random Forest (RF) models pinpointing seven key aroma markers linked to aging. Furthermore, partial least squares regression (PLSR) models demonstrated strong correlations between image features and major chemical components (total flavonoids, phenolic acids, and (+)-limonene). This approach overcomes limitations of traditional methods and provides an interpretable framework for non-destructive quality assessment. Flash (photography) ; Electronic nose ; Digital image analysis ; Artificial intelligence ; Digital imaging ; Digital image ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Image processing ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Danish Khan | O. Anatole von Lilienfeld
Source: Proceedings of the National Academy of Sciences (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Generalized convolutional many-body distribution functional representations Modern machine learning (ML) models of chemical and materials systems with billions of parameters require vast training datasets and considerable computational efforts. Lightweight kernel or decision tree-based methods, however, can be rapidly trained, leading to a considerably lower carbon footprint. We introduce generalized convolutional many-body distribution functionals (cMBDF) as highly compute and data-efficient atomic representations for accurate kernels that excel in low-data regimes. Generalizing the MBDF framework, cMBDF encodes local chemical environments in a compact fashion using translationally and rotationally invariant functionals of smooth atomic densities weighted by interaction potentials. The functional values can be efficiently evaluated by expressing them in terms of convolutions which are calculated via fast Fourier transforms and stored on predefined grids. In the generalized form, each atomic environment is described using a set of functionals uniformly defined by three integers; many-body, derivative, weighting orders. Irrespective of size/composition, cMBDF atomic vectors remain compact and constant in size for a fixed choice of these orders controlling the structural and compositional resolution. While being up to two orders of magnitude more compact than other popular representations, cMBDF is shown to be more accurate for the learning of various quantum properties such as energies, dipole moments, homo-lumo gaps, heat capacity, polarizability, optimal exact-exchange admixtures, and basis-set scaling factors. Applicability for organic and inorganic chemistry is tested as represented by the QM7b, QM9, and VQM24 datasets. Due to its compactness, model training and testing times are reduced from 23 h to 8 min, implying a corresponding reduction in carbon footprint. Kernel (algebra) ; Scaling ; Invariant (physics) ; Weighting ; Reduction (mathematics) ; Set (abstract data type) ; Fourier transform ; Convolution (computer science) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions