jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyang Bi | Dezhi Cao | Xinyue Wang | Dingkai Hu | Qiang Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Correlation between Σ-Profile Characteristics and Infinite Dilution Activity Coefficients of Choline Chloride-Urea Des: Experimental Determination and Machine Learning Interpretation Choline chloride ; Interpretation (philosophy) ; Dilution ; Urea ; Chemistry ; Correlation ; Activity coefficient ; Chloride ; Chromatography ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Thermodynamics ; Physics ; Physical chemistry ; Biochemistry ; Organic chemistry Free Radicals and Antioxidants ; Electron Spin Resonance Studies ; Synthesis and biological activity
Authors: Andre Niyongabo Rubungo (22857264) | Fernando Fajardo-Rojas (9080868) | Diego A. Gómez-Gualdrón (1635055) | Adji Bousso Dieng (22857267)
Source: Figshare (article, 2025-12-16)
Field: Chemistry — Inorganic Chemistry

Highly Accurate andFast Prediction of MOF Free Energyvia Machine Learning A major bottleneck in translating computational screening of metal–organic frameworks (MOFs) to laboratory synthesis is the uncertain synthetic accessibility of computer-generated MOF designs. While MOF free energy calculations are emerging as a way to anticipate synthetic accessibility, the computational cost of traditional molecular simulation methods remains incompatible with the immense MOF design space, which spans trillions of structures. Here, we propose and demonstrate the efficient prediction of this important quantity using machine learning. Our approach is based on three ingredients: (1) a newly curated data set of nearly 1 million MOFs (MOFMinE) and their properties, including simulated strain energies, as well as simulated free energies for a subset of these structures; (2) a novel sequence representation of MOFs, called MOFSeq, that incorporates both local and global MOF features; and (3) a large language model (LLM-Prop) pretrained on strain energy prediction and then fine-tuned on free energy prediction using MOFSeq. Our trained model achieves a mean absolute error (MAE) of 0.789 kJ/mol<sub>MOFatom</sub> on free energy prediction. Remarkably, this same model, without retraining, demonstrated exceptional versatility by predicting whether MOFs were above or below an empirical free energy-based synthesizability threshold with a 97% <i>F</i>1 score. Furthermore, it correctly selected the lowest free energy MOF polymorph with an average accuracy of 78.1%, with this accuracy increasing to 100% for free energy differences greater than 4.05 kJ/mol<sub>MOFatom</sub> and remaining above 60% even when having to sort out free energy differences as low as 0.16 kJ/mol<sub>MOFatom</sub>. Bottleneck ; Energy (signal processing) ; sort ; Representation (politics) ; Set (abstract data type) ; Free energy perturbation ; Efficient energy use Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: Yifan Fang (7587602) | Cipeng Wu (22700216) | Chentao Zhang (11494997) | Yizhu Xu (3094161) | Zhibin Yin (1785358) | Zhouyi Xu (4178311) | Wei Hang (1341051)
Source: Figshare (article, 2025-11-27)
Field: Chemistry — Spectroscopy

RSR-MSI: Reference-Based Super-Resolution for MassSpectrometry Imaging of Tissues and Single Cells High-spatial-resolution mass spectrometry imaging (MSI) visualizes molecular distributions in tissues and cells. However, achieving higher spatial resolution typically necessitates smaller pixel dimensions and an increased number of pixels, leading to longer data acquisition times and diminished analytical throughput. Although deep learning approaches have demonstrated significant potential in MSI, they typically require large training data sets or paired images, which are often unavailable. Herein, we propose the reference-based super-resolution for mass spectrometry imaging (RSR-MSI) method, with optical microscopy images as reference frames to extract abundant texture information. By integrating this with ion intensity data from the original MS images, we develop an image-specific super-resolution network. Employing solely a single low-resolution MS image coupled with a reference optical image, we successfully reconstruct high-resolution MS images for biological tissues and single cells, producing results with rich chemical and textural details. This approach significantly decreases the routine pixel-by-pixel scanning time by an order of magnitude while achieving high spatial resolution using existing mass spectrometry instruments without any customized modifications. Overall, our work introduces and validates the application of image super-resolution methods within the realm of single-cell MSI at subcellular resolution, paving the way for the development of high-spatial-resolution and high-throughput MSI for cellular biology research. Mass spectrometry imaging ; Mass spectrometry ; Pixel ; Image resolution ; Resolution (logic) ; Data acquisition ; Image processing ; Pattern recognition (psychology) Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Fluorescence Microscopy Techniques
Authors: Kaniz Fatema
Source: ChemRxiv (preprint, 2025-01-27)
Field: Chemistry — Organic Chemistry

Benchmarking of Density Functional Theories for Combustion Reactions in Alkanes by Evaluating Thermodynamic Properties Computational calculations are playing vital role in this era of artificial intelligence and machine learning which is capable to lessen the burden of experimental expenses. Inevitably, to benchmark different theoretical methods is essential to produce accurate results while keeping in mind the computational cost. In this study, different functionals of Density Functional Theory (DFT) with 2 different basis sets were benchmarked to compute thermodynamic properties, enthalpy, Gibbs free energy, and entropy, of alkane combustion reactions. Results indicate a linear relationship between the number of carbon atoms and reaction parameters, with deviations arising from method-dependent approximations. LSDA and dispersion-corrected methods demonstrated closer agreement with experimental values with correlation-consistent basis set, while higher-rung functionals like PBE and TPSS exhibited significant errors with split-valence basis set, especially for larger chain lengths. Notably, convergence issues were observed for n-hexane with PBE and TPSS, attributed to near-degenerate states and SCF instability. These findings highlight the importance of functional choice along with the basis set to define the orbitals to accurately predict thermodynamic properties. Benchmarking ; Combustion ; Density functional theory ; Thermodynamics ; Chemistry ; Computational chemistry ; Physical chemistry ; Economics ; Physics Chemical Thermodynamics and Molecular Structure ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Wuqing Tao | Wenkai Zhao | Qidong Zhao | Yonghou Xiao
Source: Inorganic Chemistry (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

Ensemble-Learning-Guided Optimization Design for Metal–Organic Framework Adsorbents toward CO Adsorption Metal-organic frameworks (MOFs) hold great potential for carbon monoxide (CO) adsorption owing to their large pore volume, diverse periodic network structures, and designability. Machine learning is anticipated to provide optimization parameters for designing high-efficiency MOFs adsorbents, avoiding time-consuming experiments. Here, we proposed an ensemble-learning strategy accounting for multidimensional analysis of features to rationally design pore geometries, structural properties, and synthesis conditions of MOFs toward high performance for CO adsorption. The extreme gradient boosting model exhibited the best predictive performance (<i>R</i><sup>2</sup> > 0.95) under limited data set size. Porous characteristic was identified as a dominant factor in pristine MOFs. Prediction results illustrated that MOFs featuring one-dimensional, two-dimensional, microporous, and isolated pores were optimal for CO adsorption, with 0.4-0.6 cm<sup>3</sup>/g total pore volume. This enhanced adsorption capacity can be attributed to the shortened molecular diffusion pathways. The relative significance of structural parameters followed: space groups > geometry > topology. The optimal structural configuration involved space group of <i>R</i>3<i>m</i>, binuclear paddle wheel geometry, and scorpionate-like topology. Regarding transition metal-modified MOFs, incorporated Cu(I) demonstrated the strongest binding affinity toward CO, while Fe(II) and Ni(II) could serve as effective binding sites. This work offers a theoretical guidance for designing efficient adsorbents toward CO adsorption. Chemistry ; Adsorption ; Metal-organic framework ; Metal ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: Yong-Bo Shi | Yu Bao | Shuo Cao | Ye Su | Keke Song | Lu Xiao | Ping Qian
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unraveling the transport behavior of BP and BAs using the Boltzmann transport equation and machine learning methods Boltzmann equation ; Thermal conductivity ; Scattering ; Phonon ; Phonon scattering ; Lattice Boltzmann methods ; Convection–diffusion equation ; Rayleigh scattering Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Long Jiao | Yuanyuan Liu | Bijia Chen | Tianlong Zhang
Source: Current Analytical Chemistry (article, 2025-08-06)
Field: Chemistry — Analytical Chemistry

Discrimination of Ophiopogonis Radix from Different Planting Districts by using Infrared Spectroscopy Combined with One-dimensional Convolutional Neural Network Introduction: The chemical composition of Ophiopogonis Radix originating from different districts is somewhat different due to the different planting environment. This difference affects the pharmaceutical efficacy of Ophiopogonis Radix, and therefore poses a challenge to the quality assessment and control of Ophiopogonis Radix. This study aims to develop a method to discriminate Ophiopogonis Radix from various planting districts by using infrared (IR) spectroscopy in combination with a one-dimensional convolutional neural network. Methods: The spectral data of Ophiopogonis Radix samples from five planting districts were collected and preprocessed with Savizkg-Golag (SG) smoothing and the Standard Normal Variate (SNV) algorithm. Back propagation (BP) artificial neural network (ANN) and one-dimensional convolutional neural network (1D-CNN) were employed to build the pattern recognition model for discriminating the investigated Ophiopogonis Radix samples. Results: The discriminant accuracy of the ANN model after SG and SNV preprocessing reached 94.42% and 96.98% respectively. The discrimination accuracy of the 1D-CNN model after SG and SNV preprocessing reached 98.67% and 99.33%, respectively. It is demonstrated that both the ANN model and the 1D-CNN model are able to discriminate Ophiopogonis Radix samples from different planting districts. Moreover, the developed 1D-CNN model shows higher discrimination accuracy than the ANN model, and SNV preprocessing is better than SG preprocessing for improving the accuracy of the discrimination model. Discussion: In this study, the classification performance differences between the SNV-ANN and SNV-CNN models were further compared and analyzed through confusion matrices. The SNVCNN model exhibited superior classification performance compared to the SNV-ANN model. Conclusion: IR coupled with 1D-CNN is a practicable and promising approach to discriminate Ophiopogonis Radix from different planting districts. Besides Ophiopogonis Radix, this approach could be used for the planting districts discrimination of other kinds of Traditional Chinese Medicines (TCM). As a high-throughput and data-driven spectroscopic analysis approach, it can be applied to the standardized quality assessment of TCM. Artificial intelligence ; Pattern recognition (psychology) ; Preprocessor ; Convolutional neural network ; Linear discriminant analysis ; Radix (gastropod) ; Artificial neural network ; Mathematics ; Computer science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research
Authors: Yu-Jie Cen | Sandro Wieser | Georg K. H. Madsen | Jesús Carrete
Source: ArXiv.org (preprint, 2025-08-09)
Field: Materials Science — Materials Chemistry

Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective Due to their aspect ratio and wide range of thermal conductivities, nanotubes hold significant promise as heat-management nanocomponents. Their practical use is, however, often limited by thermal resistance introduced by structural defects or material interfaces. An intriguing question is the role that structural symmetry plays in thermal transport through those defect-laden sections. To address this, we develop a framework that combines representation theory with the mode-resolved Green's function method, enabling a detailed, symmetry-resolved analysis of phonon transmission through defected segments of quasi-1D systems. To avoid artifacts inherent to formalisms developed for bulk 3D systems, we base our analysis on line groups, the appropriate description of the symmetries of quasi-1D structures. This categorization introduces additional quantum numbers that partition the phonon branches into smaller, symmetry-distinct subsets, enabling clearer mode classification. We employ an Allegro-based machine learning potential to obtain the force constants and phonons with near-ab-initio accuracy. We calculate detailed phonon transmission profiles for single- and multi-layer MoS$_\mathrm{2}$-WS$_\mathrm{2}$ nanotubes and connect the transmission probability of each mode to structural symmetry. Surprisingly, we find that pronounced symmetry breaking can suppress scattering by relaxing selection rules and opening additional transmission channels. Molecular dynamics shows that the behavior persists even when anharmonicity is considered. The fact that higher disorder introduced through defects can enhance thermal transport, and not just suppress it, demonstrates the critical role of symmetry in deciphering the nuances of nanoscale thermal transport. Phonon ; Anharmonicity ; Quantum ; Rotation formalisms in three dimensions ; Symmetry breaking ; Thermal ; Function (biology) ; Symmetry (geometry) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Ting-Ting Zhang | Ei Myat Mon Oo | Bilong Chen | Lin Liu | Quan-Bao Zhao | Jiachen Yang | Yu‐Ming Zheng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Enhancing Sulfate Reduction Performance by Gas Stripping in Acidogenesis: Modeling Optimization and Microbial Mechanisms Acidogenesis ; Reduction (mathematics) ; Stripping (fiber) ; Biochemical engineering ; Sulfate ; Process engineering ; Chemistry ; Environmental science ; Waste management ; Computer science ; Engineering ; Mathematics ; Anaerobic digestion ; Mechanical engineering Calcium Carbonate Crystallization and Inhibition ; Reservoir Engineering and Simulation Methods ; Minerals Flotation and Separation Techniques
Authors: Bhagyashree D. Lambture | Madhavi Ajay Pradhan
Source:  (article, 2025-10-30)
Field: Chemistry — Analytical Chemistry

Data-Driven Quality Assessment of Medicinal Plants using ML The evaluation of medicinal plants' quality is essential to ensuring their effectiveness, safety, and uniformity in pharmaceutical and therapeutic uses. Traditional methods rely on chemical, morphological, and genetic analysis, which are often time-consuming and require extensive expertise. In this study, we propose a Machine Learning (ML)-based algorithm that integrates diverse data sources–including phytochemical profiles, spectral imaging, genetic markers, and environmental factors–to qualitatively assess medicinal plants. The algorithm leverages feature selection techniques to identify the most predictive attributes for quality assessment, followed by the development of a classification and prediction model. The proposed method is validated against existing assessment techniques to determine its efficacy and reliability. According to experimental data, the ML-based strategy improves the quality evaluation process's accuracy and efficiency, which helps to standardize and authenticate medicinal plants. Medicinal plants ; Quality (philosophy) ; Selection (genetic algorithm) ; Feature selection ; Quality assessment ; Genetic algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Traditional Chinese Medicine Analysis
Authors: Ke Shu (1528786) | Dong-Yun Gui (1611307) | Wei-Xin Yan (18608959) | Chun-Hai Wang (1558318)
Source: Figshare (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Machine LearningTackles the Challenge of Powder X‑rayDiffraction Indexing for All Crystal Systems The indexing of powder X-ray diffraction (PXRD) in <i>ab initio</i> unknown structure determinations is a critical yet challenging step in crystallography, particularly for low-symmetry systems (e.g., monoclinic, triclinic) and/or large unit cell systems (<i>V</i> > 1000 Å<sup>3</sup>). In this work, a machine learning-based indexing method is presented, which achieves high-precision, end-to-end prediction of crystal symmetry and unit cell parameters from powder diffraction peaks for all crystal systems. The trained models (denoted as AIdex) achieve a top-5 accuracy of ∼97% in extinction group (symmetry class) identification, and a mean absolute percentage error (MAPE) <5% for indexing, demonstrating significant improvements in both accuracy and time consumed compared to traditional algorithms (TREOR/ITO/DICVOL). AIdex also shows high capacity for experimental applications, maintaining a success rate of ∼90% even under extreme conditions involving zero-shift error (±0.6°) and uncertainty noise (±0.15°). Applied to practical PXRD data, AIdex gives predicted unit cell parameters close to the experimentally refined ones (MAPE < 5%), serving as ideal initial inputs for further Pawley refinements. This leads to a new paradigm for rapid indexing in <i>ab initio</i> unknown structure determination, facilitating the advancement of crystallographic analysis toward automation and intelligent methodologies. Diffraction ; Search engine indexing ; Powder diffraction ; Automation ; Extreme learning machine ; Noise (video) ; Ideal (ethics) ; Crystal (programming language) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Tota Rakesh Kumar | Bodduru Kamesh | Giri Jayant | Mohammad Kanan | Dora Nagaraju | Javed Syed
Source: Results in Engineering (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Numerical simulation and machine learning framework approach for ballistic performance evaluation of aluminium structures • A comprehensive finite element analysis of high-velocity impacts on aluminum plates was conducted for blunt, hemispherical, and ogival projectile geometries. • Distinct fracture mechanisms—adiabatic shear plugging, petaling, and ductile hole enlargement were identified based on projectile nose shapes. • Numerical results using the Johnson–Cook material model showed excellent agreement with experimental absorbed energy measurements. • Machine learning models (SVR, RF, DT, MLP) were developed to accurately predict absorbed energy, significantly reducing computational cost. • The hybrid FEM-ML approach provides a rapid and reliable predictive framework for optimizing lightweight protective structures under ballistic loading. Understanding and accurately predicting the ballistic response behavior of structural materials is critical for the design and optimization of lightweight, high performance protective structures subjected to high velocity impact. This study presents a comprehensive numerical analysis of the ballistic perforation of aluminum plates impacted by rigid steel projectiles with blunt, hemispherical, and ogival nose geometries. Finite Element Method (FEM) simulations incorporating the Johnson–Cook constitutive and failure models were conducted in ABAQUS/Explicit scheme to evaluate absorbed energy during impact. The simulation results closely match experimental data, with an average deviation of less than 7% in numerically predicted absorbed energy values. To overcome limitations associated with computational cost and model complexity, supervised Machine Learning (ML) models which includes Support Vector Regression (SVR), Random Forest (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP), were developed to predict absorbed energy based on projectile geometry, impact velocity, and target thickness. Comparative assessments demonstrated that ML-based models maintained strong predictive accuracy. Detailed analysis of fracture patterns provide additional insights into failure mechanisms under different impact scenarios. The integrated FEM–ML framework proposed herein demonstrates enhanced predictive accuracy and computational efficiency, offering a promising tool for rapid ballistic performance evaluation and impact resistant design optimization. Ballistic limit ; Projectile ; Finite element method ; Support vector machine ; Ballistic impact ; Perforation ; Multilayer perceptron ; Ballistics High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Cellular and Composite Structures
Authors: Victor Milman | A. Ya. Perlov | Neil A. Spenley | Björn Winkler
Source: Materialia (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Accuracy of calculated elastic properties of inorganic materials: Density functional theory and machine-learned potentials Materials science ; Density functional theory ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Unmanaa  Nileen Dewanjee | G Deepak | Shi Bai | David Fieser | Yang Du | Marco Fronzi | Qin He | Anming Hu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-Sensitive SERS Analysis for PFOA Degradation Based on Femtosecond Laser-Processed Probes, DFT Calculation and Machine Learning Femtosecond ; Raman scattering ; Substrate (aquarium) ; Raman spectroscopy ; Nanoscopic scale ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser-Ablation Synthesis of Nanoparticles