jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashna Jose | Emilie Devijver | Martin Uhrin | Noel Jakse | Roberta Poloni
Source: Open MIND (preprint, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples are known among the thousands of MOFs already synthesized. Computational screening enhanced by machine learning offers a powerful route to uncover these hidden candidates much more rapidly than trial-and-error experiments. However, progress is limited by the computational complexity of obtaining accurate adiabatic energy differences, as these typically require separate geometry optimizations for both spin states, a process that is technically challenging, prone to convergence failures, and difficult to automate at scale. To mitigate these issues, we introduce a data-efficient strategy based on Quantile Regression Tree-based Active Learning, designed to navigate large chemical spaces while remaining robust to noisy and scarce labels obtained from unrelaxed geometries. After actively selecting a 200-sized subset of representative MOFs for electronic-structure evaluation, a Random Forest regressor trained on this data accurately identifies SCO-relevant candidates despite label noise, recovering 82% of true positives with only two false negatives. Applying the model to the unlabeled dataset yields a new collection of high-confidence SCO MOFs, which we denote pSCO-105. This work shows that spin crossover can be reliably identified from limited and imperfect data through smart training-set selection, enabling accelerated screening of SCO MOFs. Process (computing) ; False positive paradox ; Quantile ; Noisy data ; Random forest ; False positives and false negatives ; Convergence (economics) ; Noise (video) ; Active learning (machine learning) Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Chuang Yang | Jiangong Yu | Zhi Li | Xiaoming Zhang
Source: Applied Mathematical Modelling (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Applicability of Shear Horizontal guided waves in non-classical continuum theory and calibration of higher-order mode scale parameters Continuum hypothesis ; Elasticity (physics) ; Modal ; Shear (geology) ; Length scale ; Scale (ratio) ; Shear waves Nonlocal and gradient elasticity in micro/nano structures ; Ultrasonics and Acoustic Wave Propagation ; Nonlinear Photonic Systems
Authors: Dmitry Korogod | Olga Chalykh | Max Hodapp | Nikita Rybin | Ivan S. Novikov | Alexander V. Shapeev
Source: The Journal of Chemical Physics (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential. We show that the explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models, we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules. Coulomb ; Tensor (intrinsic definition) ; Coulomb barrier ; Moment (physics) ; Charge (physics) ; Equivariant map ; Density functional theory Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Zaid Allal | Hassan N. Noura | Flavien Vernier | Ola Salman | Khaled Chahine
Source: International Journal of Thermofluids (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Hydrogen absorption in metal hydrides: A dual-head tree-based framework for material class classification Metal hydrides represent a highly promising alternative for hydrogen storage due to their favorable absorption properties and relatively moderate requirements in terms of temperature and pressure during the uptake stage. Furthermore, their ability to undergo multiple absorption–desorption cycles without significant degradation makes them particularly well-suited for repeated charge–discharge operations. In this work, we propose a two-headed machine learning framework for the classification of metal hydrides. The first head determines whether a material belongs to the AB family of hydrides. If not, the second head classifies it into one of the remaining categories: MIS, SS, MG, or complex hydrides. This hierarchical modeling strategy, inspired by fault detection and diagnosis in biphasic systems, proved effective in improving classification accuracy. The framework was trained using six tree-based estimators: Random Forest, LightGBM, XGBoost, CatBoost, Gradient Boosting, and Extra Trees. Strong imputation techniques, including the KNN imputer, were also employed to address missing data and ensure robustness. The results demonstrate that for the first head, the Random Forest model achieved an accuracy of 88.6% in identifying the AB family class. For the second head, Gradient Boosting reached 91.61% accuracy, which was further improved to 92.56% after hyperparameter tuning. When compared to previous studies using the same dataset, our framework exhibits significantly stronger performance. Moreover, its predictions were validated through the integration of explainable artificial intelligence (XAI) methods, ensuring both interpretability and reliability. This framework will be further optimized to extend beyond class-level identification toward the precise prediction of hydride composition formulas, with the aim of better supporting the design of materials with enhanced hydrogen absorption capacities. • The imputation technique increased the number of usable training samples and improved overall modeling accuracy. • A two-tiered solution reduced computational complexity while enhancing classification performance. • Tree-based models effectively captured the underlying patterns for classifying metal hydride categories. • Explainable AI techniques validated the relevance and contribution of the selected features. Interpretability ; Random forest ; Hyperparameter ; Boosting (machine learning) ; Hydrogen ; Hydride ; Class (philosophy) ; Absorption (acoustics) Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Machine Learning in Materials Science
Authors: Ashish Sambhaji Uzgare
Source: International Journal of Chemistry Research (article, 2026-04-01)
Field: Chemistry — Spectroscopy

FAST PROTEIN STRUCTURAL ASSESSMENT USING CIRCULAR DICHROISM (CD) SPECTROSCOPY: AN UPDATED REVIEW Circular dichroism (CD) spectroscopy has become a rapid and versatile analytical technique for probing protein structure, playing a pivotal role in modern proteomics and structural genomics. This review integrates studies published between 1994-2025 to examine the principles, instrumentation, and diverse applications of CD spectroscopy. CD measures the differential absorption of left-and right-circularly polarized light by chiral molecules, enabling semi-quantitative estimation of protein secondary structures such as α-helices, β-sheets, and random coils using far-UV spectra, and providing insights into tertiary environments via near-UV signals. Its applications include monitoring protein folding and unfolding, characterizing intrinsically disordered proteins (IDPs), and assessing structural integrity in recombinant proteins and biopharmaceuticals. The combination of experimental CD data with computational tools, including vibrational CD (VCD) simulations, the BeStSel algorithm, and machine learning approaches, has improved accuracy, particularly for β-rich, flexible, or disordered protein systems. Despite limitations, such as the need for high-purity samples and approximate secondary structure estimation, CD’s minimal sample requirement, rapid data acquisition, and solution-based analysis make it an essential tool for initial structural evaluation. Looking forward, advances in computational integration, standardized reference databases, and expanded applications to dynamic protein complexes and challenging biomolecules promise to further enhance CD spectroscopy as a cornerstone technique in structural biology and biotechnology. Circular dichroism ; Biomolecule ; Structural biology ; Protein structure ; Protein folding ; Folding (DSP implementation) ; Intrinsically disordered proteins ; Protein secondary structure Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Origins and Evolution of Life
Authors: Tuomas Sormunen | Ella Mahlamäki | Satu-MarjaMäkelä | MikkoMäkelä
Source: Data in Brief (article, 2026-02-09)
Field: Chemistry — Analytical Chemistry

Introducing OpenTextile-NIR: Near-infrared hyperspectral imaging and photography dataset for optical identification of textiles This dataset presents the first open-access collection of near-infrared hyperspectral imaging (NIR-HSI) data for the optical identification of textiles, with a focus on supporting research in sensor-based textile sorting and recycling. The dataset comprises hyperspectral images, RGB photographs, and detailed metadata, including fibre composition and colour, for 71 post-industrial textile samples, collected in Finland. Over 11 million spectra are included in the hyperspectral images, with more than 6 million annotated, providing a robust foundation for machine learning and data analysis. In addition, we provide a single representative NIR spectra and RGB value for each sample in order to accommodate classic spectroscopic analysis. Used garments were sourced from a partner company specializing in end-of-life textile management, with ground truth information on fibre composition obtained from suppliers. Small pieces of each garment were measured using Specim SWIR 3 hyperspectral camera and photographed with high-resolution mobile phone camera (Samsung Galaxy A52). The dataset is organized into folders containing raw and processed data, including ENVI-format hyperspectral images, RGB images, as well as CSV files with mean spectra, mean RGB values, and sample metadata. An example Python script is provided to facilitate data access and processing. Potential reuse scenarios include classification of textiles by material or colour, prediction of natural fibre content, image segmentation, algorithm development for spectral classification, and use as a reference spectral library. The dataset's comprehensive structure and open availability address the limitations of previous research, which often relied on small or non-public datasets, and is intended to accelerate advances in optical identification technologies for textile recycling. Hyperspectral imaging ; Ground truth ; RGB color model ; Spectral signature ; Identification (biology) ; Sorting ; Photography ; Python (programming language) Spectroscopy and Chemometric Analyses ; Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: D. Liu | Ling Liu | Baixue Wang | Jianfeng Zhang | Hong‐Guo Lin | Xiaoyin Huang | Kang‐Jian Deng | Yang Zhou | Yunlong Pan | Bin Hu
Source: Analytical Science Advances (article, 2026-04-11)
Field: Chemistry — Spectroscopy

Wooden‐Tip Electrospray Ionization Mass Spectrometry Combined With Machine Learning for Differentiating Thyroid Tumours ABSTRACT Direct mass spectrometry (MS) analysis of human tissues at the molecular level has great potential for clinical diagnosis and biomarker discovery. However, conventional MS‐based analytical methods often require complicated and time‐consuming sample preparation, which limits their applicability in rapid clinical analysis. In this study, we developed a rapid analytical strategy by integrating ambient ionization MS with machine learning (ML) for the differentiation of different thyroid tumours. A disposable slim wooden tip (WT) was employed as both a sample holder and an electrospray emitter, enabling direct extraction and ionization of metabolites from tiny thyroid tissue samples under electrospray ionization (ESI) conditions. Using this WT–ESI–MS method, lipid profiles of thyroid tissues could be obtained within minutes without extensive sample preparation. A total of 45 thyroid samples, including 15 healthy tissues, 15 benign tumours and 15 malignant tumours, were analysed. The acquired MS data were further processed using ML‐based classification models to distinguish different tumours and identify potential lipid biomarkers. Structural characterization of representative lipids was also performed by MS/MS analysis. The results demonstrated that this WT–ESI–MS combined with ML provides a rapid and effective approach not only for differentiating tumour tissues and healthy samples but also for benign and malignant tumours, highlighting its potential application in clinical diagnosis and intraoperative tissue evaluation. Electrospray ionization ; Thyroid ; Mass spectrometry ; Ambient ionization ; Metabolomics ; Sample (material) ; Biomarker ; Biomarker discovery Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Gbenga Olorunmodimu | Darius Vrubliauskas | Jantana Yahuafai | Duangjai Todsaporn | Borwornlak Toopradab | Phornphimon Maitarad | Thanyada Rungrotmongkol | Chanat Aonbangkhen | Tanatorn Khotavivattana
Source: European Journal of Medicinal Chemistry (article, 2026-03-03)
Field: Chemistry — Organic Chemistry

Phenoxy-linked colchicine derivatives: A structure-based approach toward enhanced selectivity and α-tubulin interaction Selectivity ; Colchicine ; Docking (animal) ; Cytotoxicity ; Quantitative structure–activity relationship ; Potency ; Tubulin ; Cell culture ; Cancer cell Synthesis and biological activity ; Synthesis of heterocyclic compounds ; Synthesis and Characterization of Pyrroles
Authors: Gang Seob Jung | Lei Cheng
Source: npj Computational Materials (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study Abstract Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 q e , consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics. Ionic bonding ; Molecular dynamics ; Transferability ; Charge (physics) ; Artificial neural network ; Coulomb ; Density functional theory ; Residual ; Phase (matter) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: Max Hodapp | Guillaume Anciaux
Source: ArXiv.org (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one strategy is to supplement training protocols for MLIPs with additional learning methods, such as active learning, or fine-tuning. This strategy, however, yields very complex training protocols that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce. To address the above difficulties, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols. Workflow ; Software ; Python (programming language) ; Set (abstract data type) ; Parametric statistics ; Selection (genetic algorithm) ; Learning curve ; Toolbox Machine Learning in Materials Science ; Quantum many-body systems ; Scientific Computing and Data Management
Authors: Max Hodapp | Guillaume Anciaux
Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne) (preprint, 2026-01-03)
Field: Materials Science — Materials Chemistry

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one strategy is to supplement training protocols for MLIPs with additional learning methods, such as active learning, or fine-tuning. This strategy, however, yields very complex training protocols that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce. To address the above difficulties, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols. Workflow ; Software ; Python (programming language) ; Set (abstract data type) ; Parametric statistics ; Selection (genetic algorithm) ; Learning curve ; Toolbox Machine Learning in Materials Science ; Quantum many-body systems ; Scientific Computing and Data Management
Authors: Joe Boro | Alizé Griffin | Assel Aitkaliyeva | Jen Caseres | Ouyanatu Maina | Naomi Marks
Source: Journal of Alloys and Compounds (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting U3O8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs Embedding ; Pattern recognition (psychology) ; Projection (relational algebra) ; Deep learning ; Centroid ; Scanner ; Similarity (geometry) ; Support vector machine ; Image processing Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Machine Learning in Materials Science
Authors: A Gowrishankar | V Vijayan | J. Manoj Prabhakar | Divyansh Joshi | Ajit Pal Singh | P. G. Kubendran Amos
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

QPoroLyser: an accessible automated tool for quantitative analysis and fingerprint generation of porosity in microstructures Porosity ; Segmentation ; Population ; Shape analysis (program analysis) ; Property (philosophy) ; Image processing ; Pattern recognition (psychology) Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Xin Zhou | Zhihua Li | Xiaowei Huang | Xiangyu Hu | Baoze Liu | Jiyong Shi | Xiaobo Zou
Source: Journal of Food Measurement & Characterization (article, 2026-03-04)
Field: Chemistry — Analytical Chemistry

Artificial intelligence-driven food safety and quality control Quality (philosophy) ; Food safety ; Big data ; Food processing ; Adaptability ; Hazard ; Hazard analysis and critical control points ; Population Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Melamine detection and toxicity
Authors: Xiaokun Zhou | Tao Yang | Yao Qin | yue li | Qiang Wei | Jinhui Xie | Yanxun Wu | Yuchang Liu | Zengxi Wei | Shuangliang Zhao
Source: Chinese Journal of Chemical Engineering (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

Data-efficient and interpretable machine learning for iridium-catalyzed methanol carbonylation: Ru-enabled operating windows and process-ready guidance Carbonylation ; Methanol ; Bayesian probability ; Feature selection ; Iridium ; Acetic acid ; Ruthenium ; Iodide Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Bo Lei | Txai Sibley | Elizabeth A. Holm
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Few-Shot Segmentation of Complex Microstructures: Minimizing Training Images by Maximizing Representativeness and Diversity Segmentation ; Convolutional neural network ; Training (meteorology) ; Selection (genetic algorithm) ; Representativeness heuristic ; Annotation ; Pattern recognition (psychology) ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Mineral Processing and Grinding