jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ferdinand Breit
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Data2Value: Explainable, Inline Image – Based Monitoring for Robust Batch Crystallization Data2Value: Explainable, Inline Image–Based Monitoring for Robust Batch Crystallization Keywords: Crystallization; Inline image analysis; Multimodal deep learning; XAI; Digital twin; Batch-to-batch learning Crystallization not only governs product purity, but also particle size and shape. The latter two properties influence filterability, downstream handling and, for APIs, the sensitive solid form with regard to regulatory and intellectual property issues. Yet batch-to-batch variability persists because critical “crystallization parameters” (onset/induction, nucleation/growth dynamics, crystal size/shape/morph fraction) are rarely measured inline; conventional process analysis technology (e.g., turbidity, focused beam reflectance measurement) provides only partial coverage. Data2Value closes this gap by combining inline, image-based particle analytics with a multimodal, explainable deep-learning classifier that delivers early warnings and root-cause indications in real time. The classifier fuses time-series features (image-derived size/shape trajectories, process signals) with static context (e.g., supplier, recipe, operator) and provides human-interpretable attributions via explainable AI. To mitigate the problem of the limit availability of plant data, the multimodal deep learning classification model (see Fig. 1) are pre-trained on synthetic trajectories generated by a first-principles digital twin (population balance equation-based), then fine-tuned on laboratory and mini-plant batches with continuous acquisition; models are updated batch-to-batch to track parametric drift and plant-model mismatch. We outline the laboratory validation of the image pipeline, the curated reference database, and the industrial mini-plant integration, and we present planned KPIs for impact (false-batch reduction, cycle-time, rework/energy). The approach is sensor-agnostic and transferable to other particulate unit operations (e.g., precipitation, granulation, spray drying). This poster outlines the Data2Value project, detailing its architecture, data flows and validation strategy. It also highlights the pathway from in-line visibility to operational and explainable decisions in the batch crystallization process. Classifier (UML) ; Analytics ; Parametric statistics ; Context (archaeology) ; Process (computing) ; Batch processing ; Image processing Crystallization and Solubility Studies ; Fluid Dynamics and Mixing ; Spectroscopy and Chemometric Analyses
Authors: Xin Hao | Gang Gu | Shuyi Yang | Chong Peng | Tao E | Yun Li
Source: Journal of environmental chemical engineering (article, 2026-03-05)
Field: Materials Science — Biomaterials

Rational design of Ba(II)-cross-linked alginate gel for highly efficient and selective capture of Cu(II) via angular quantum number regulation Rational design ; Quantum ; Quantum chemical ; Design of experiments ; Quantum dot Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Viet-Ha Chu | Quang Hai Nguyen | Mai An Pham | Minh Tuan Luong | P.T. Tho | Duc‐Long Nguyen
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Competing bonding and anharmonicity control piezoelectricity and thermal transport in janus BrSbX monolayers We show how competing bonding strength and lattice anharmonicity govern piezoelectricity and thermal transport in Janus 1T-BrSbX monolayers (X = S, Se, Te). Using density-functional theory and density-functional perturbation theory, combined with machine-learning-accelerated third-order force constants and Boltzmann transport calculations, we map composition property relationships within a single symmetry family. A bonding analysis (COHP) reveals a monotonic reduction in Sb-X covalency from S → Te, which tracks the piezoelectric stress response <i>e</i> <sub>11</sub> and yields in-plane strain coefficients <i>d</i> <sub>11</sub> = 41.0, 22.1, 6.3 pm V<sup>-1</sup> for BrSbS, BrSbSe, and BrSbTe, respectively. In thermal transport, longitudinal-acoustic group velocities decrease from ∼3.41 to <2.78 km s<sup>-1</sup> (S → Te), but mode-averaged Grüneisen parameters diminish more strongly, so phonon lifetimes dominate the trend: <i>κ</i> <sub>L</sub>(300 K) = 5.94, 9.39, 12.32 W m<sup>-1</sup> K<sup>-1</sup> for S, Se, and Te, respectively. All monolayers are mechanically and dynamically stable. Together, these results establish a practical design rule: strengthening directional Sb-X covalency enhances <i>d</i> <sub>11</sub>, while reduced anharmonicity raises <i>κ</i> <sub>L</sub>; the chalcogen thus provides a clean chemical knob to balance electromechanical coupling against heat transport in 2D Janus pnictogen chalcogenides. Anharmonicity ; Piezoelectricity ; Janus ; Monolayer ; Phonon ; Thermal ; Boltzmann equation ; Lattice (music) 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena
Authors: Abdullah S. Al-Bogami | Ahmed Abdelhamid Maamoun | El-Refaie Kenawy | Khalid Z. Elwakeel | Abdullah Akhdhar | Ahmed M. Elgarahy
Source: Reactive and Functional Polymers (article, 2026-03-14)
Field: Materials Science — Polymers and Plastics

Rigid polyurethane foams – based composites for sustainable logistics transportation: Synthesis, structure, multifunctional applications, and artificial intelligence integration Polyurethane ; Data integration ; System integration ; Applications of artificial intelligence Polymer composites and self-healing ; Flame retardant materials and properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Omar Abou El Kheir | Marco Bernasconi
Source: arXiv (Cornell University) (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

On the origin of in-gap states in amorphous Ge$_2$Sb$_2$Te$_5$ The localized states in the band gap of amorphous phase change alloys like Ge$_2$Sb$_2$Te$_5$ control the electrical conduction via the Poole-Frenkel mechanism. Understanding the origin of in-gap states and their evolution in time during aging of the glass is therefore important for the control of the resistance drift in phase change memory devices. Here, we use a machine learning interatomic potential to generate several models of Ge$_2$Sb$_2$Te$_5$ whose electronic structure is then analyzed within density functional theory with a hybrid functional. A detailed statistical analysis of the structural motifs on which the in-gap states are localized, reveals that the vast majority of in-gap states involve wrong bonds (homopolar or Ge-Sb bonds) often accompanied by Ge in tetrahedral configurations or overcoordinated Ge and Sb atoms. Metadynamics simulations mimicking glass aging support the picture that structural relaxations lead to the depletion of in-gap states and then to an increase of resistance. The simulations thus provide important insights for the mitigation of the resistance drift in phase change memory devices. Metadynamics ; Amorphous solid ; Phase (matter) ; Density of states ; Band gap ; Phase-change memory ; Molecular dynamics ; Polyamorphism ; Density functional theory Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Fahhad H. Alharbi
Source: Electronic Structure (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Kinetic energy density functional: advances, challenges, and future directions Abstract Density functional theory (DFT) is widely used for electronic structure calculations, primarily utilizing the Kohn–Sham scheme. However, this approach reintroduces orbitals, resulting in a high computational cost. In contrast, the original orbital-free Hohenberg–Kohn DFT (OF-DFT) offers the potential for linear-scaling computations suitable for large systems. The advancement of OF-DFT relies on the development of an accurate and universal kinetic energy density functional (KEDF). This review surveys the progress, challenges, and prospects in KEDF development. It presents the essential physical and mathematical constraints that any KEDF must comply with, tracing the evolution from early models like Thomas–Fermi and von Weizsäcker to contemporary semi-local, nonlocal, and machine-learned approaches. While the developed KEDFs have improved the treatment of metals and some semiconductors, achieving transferable accuracy for molecules and systems with considerable density inhomogeneities remains a critical challenge. We highlight two emerging paradigms; the use of physics-guided machine learning to identify accurate KEDFs and information-theoretic approaches that provide deep insights. The path forward requires a renewed focus on fundamental physical constraints, steering the field away from purely empirical fitting toward a universal, computationally efficient KEDF that maximizes the advantages of OF-DFT. The main KEDFs are listed, including gradient expansions, enhancement-factor strategies, density-decomposition methods, and nonlocal KEDFs guided by linear-response theory. Computation ; Focus (optics) ; Field (mathematics) ; Kinetic energy ; Path (computing) ; Density functional theory ; Energy (signal processing) ; Tracing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Ying-Fan Lin | Jun-Hong Lin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Topology-driven bridging and matrix-dominated blocking in random thermal interfaces: An interpretable framework Homogenization (climate) ; Thermal conductivity ; Bridging (networking) ; Thermal conduction ; Thermal ; Scaling ; Nonlinear system ; Imperfect Thermal properties of materials ; Composite Material Mechanics ; Numerical methods in inverse problems
Authors: M. V. Arularasu | K. Tharun Sidharth
Source: Research Square (preprint, 2026-02-16)
Field: Materials Science — Polymers and Plastics

The efficiency of rare earth doped V 2 O 5 nanoparticles on photocatalytic and antibacterial activity Photocatalysis ; Photodegradation ; Rhodamine B ; Orthorhombic crystal system ; Nanoparticle ; Doping ; Band gap ; Hydrothermal circulation Transition Metal Oxide Nanomaterials ; Magnesium Oxide Properties and Applications ; Catalysis and Oxidation Reactions
Authors: Andrei V. Bykov | Andrei V. Shevelkov
Source: ChemRxiv (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Curated Dataset, Structure-Property Relationships and Machine Learning Prediction of Band Gaps for One-Dimensional Bismuth(III) and Antimony(III) Halides Vast structural diversity and remarkable optoelectronic properties of organic-inorganic and all-inorganic metal halides continue to stimulate huge research interest. While the outstanding performance of lead halide perovskites has defined the field of perovskite photovoltaics, concerns regarding lead toxicity and long-term stability have driven the search for lead-free alternatives. Low-dimensional bismuth(III) and antimony(III) halides have emerged as promising candidates; however, their rational design remains hindered by the lack of established structure-property relationships and structure predictive possibilities. In an effort to fill this knowledge gap, we report here an open curated dataset of one-dimensional (1D) bismuth(III) and antimony(III) halides featuring edge-sharing {MX4}chain-type anions (M-Bi, Sb; X-Cl, Br, I), including experimentally determined optical band gap values. Leveraging crystallographically accessible structural parameters, we constructed two descriptor spaces for compounds with an α-{MX4}anion-type: a primary space based on M-X bond lengths and X-M-X bond angles (Full Geometric Descriptor Space), and a reduced space comprising average bond lengths and distortion parameters of building units [MX6] (Reduced Distortion Descriptor Space). Both spaces additionally incorporate descriptors capturing the interchain X•••X contacts, anion composition, and crystallographic experiment temperatures. Given the limited data volume, we deliberately employed classical machine learning (ML) algorithms with cross-validation and stability analysis. The resulting models achieve prediction errors of 0.10 and 0.11 eV for the two descriptor spaces, approaching a typical experimental uncertainty of optical band gap determination. The feature importance analysis reveals that geometric descriptors of the inorganic anionic framework dominate over noncovalent interchain interaction descriptors in governing the band gap. To promote reproducibility and further development in the field, we released a complete dataset of group 15 1D-halometallates, trained models, and supporting materials in an open-access repository under the MIT license (Figure S1). Halide ; Band gap ; Stability (learning theory) ; Perovskite (structure) ; Distortion (music) ; Space (punctuation) ; Parameter space ; Bond length Machine Learning in Materials Science ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Carsyn R. Travis | Jared McMaster | Fatima Rivas
Source: Molecules (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

Advances in Natural Product Extraction: Established and Emerging Technologies Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Scope (computer science) ; Natural product ; Solvent extraction ; Drug discovery ; Workflow ; Isolation (microbiology) ; Supercritical fluid extraction ; Chemical space Chromatography in Natural Products ; Metabolomics and Mass Spectrometry Studies ; Phytochemical compounds biological activities
Authors: Safa Nouaa | Rachid Aziam | Saadia El Qdhy | Ridouan Benhiti | Gabriela Carja | Amina Soudani | MOHAMED CHIBAN
Source: Surfaces and Interfaces (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Alginate-based functional hydrogel beads for nutrient removal: Experimental insights and process optimization Adsorption ; Nitrate ; Endothermic process ; Self-healing hydrogels ; Phosphate ; Aqueous solution ; Dispersion (optics) ; Layered double hydroxides ; Polymer Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Adsorption and biosorption for pollutant removal
Authors: Zhiyong Zou | Jiajun Jiang | Menghua Li | Dongyu Yuan | Qianlong Wang | Ronghua Zhou | Huan Wang | Quqing Ku | Yafen Hu | Man Zhou | Lijia Xu
Source: Journal of Food Composition and Analysis (article, 2026-01-05)
Field: Chemistry — Analytical Chemistry

Data fusion of HSI and FHSI combined with improved deep learning models to identify the geographical origin of Lycium barbarum Traditional methods for identifying the geographical origin of Lycium barbarum ( L. barbarum ) often have exhibited limited sensitivity and specificity, rendering them inadequate for meeting the increasingly stringent quality control standards required for traditional Chinese medicinal materials. Therefore, this study proposes an innovative approach that integrates Hyperspectral Imaging (HSI) and Fluorescence Hyperspectral Imaging (FHSI) to construct a deep learning-based traceability model through multi-source spectral data fusion, enabling high-precision identification of L. barbarum from different geographical origin. Specifically, Isolation Forest (IF) was employed for outlier detection alongside three different combinations of spectral preprocessing methods. Classification was then performed using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BiLSTM) models. Furthermore, an improved Sparrow Search Algorithm (SCSSA) was proposed to optimize the hyperparameters of the BiLSTM model. Experimental results showed that the SCSSA-BiLSTM model based on data-level fusion achieved the best classification performance, with training and testing accuracies of 96.5% and 93.6%. Overall, the study demonstrated the feasibility and effectiveness of combining HSI and FHSI spectral fusion with SCSSA-optimized deep learning for the geographical origin identification of L. barbarum , offering methodological support for the rapid traceability of agricultural products. ● Deep fusion of HSI and FHSI enables origin identification of L. barbarum. ● IF removes outliers and significantly improves classification performance. ● Combined preprocessing and SCSSA-optimized BiLSTM improve accuracy. ● Samples from China and Canada improve model generalization. Hyperspectral imaging ; Pattern recognition (psychology) ; Deep learning ; Support vector machine ; Random forest ; Preprocessor ; Linear discriminant analysis ; Multispectral image Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture
Authors: Sofiia Chorna | Davide Tisi | Cesare Malosso | Wei Bin How | Michele Ceriotti | Sanggyu Chong
Source: Advanced Intelligent Systems (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials The past few years have seen the development of “universal” machine‐learning interatomic potentials (uMLIPs) capable of approximating the ground‐state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors (FREs), and observing how the trends are affected by the choice of training set and training protocol. We find that uMLIPs encode the chemical space in significantly distinct ways, with substantial cross‐model FREs. When variants of the same model architecture are considered, trends become dependent on the dataset, target, and training protocol of choice. We also observe that fine‐tuning of a uMLIP retains a strong pretraining bias in the latent features. Finally, we discuss how atom‐level features, which are directly output by MLIPs, can be compressed into global structure‐level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system. ENCODE ; Concatenation (mathematics) ; Feature (linguistics) ; Encoding (memory) ; Set (abstract data type) ; Range (aeronautics) ; Space (punctuation) ; Latent variable Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Rajanikanta Rana | Abinash Swain | Garima Bangar | Gopalan Rajaraman
Source: Inorganic Chemistry (article, 2026-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Uncovering Giant Magnetic Anisotropy in Co(II) Single-Ion Magnets via Combined Ab Initio and Machine-Learning Approaches Single-ion magnets, particularly low-coordinate Co(II) complexes, are promising for high-density data storage; however, achieving large effective energy barriers while retaining strong magnetic anisotropy remains an unresolved challenge. In this work, we systematically extracted three-coordinate Co(II) complexes from reported X-ray crystal structures and subsequently expanded this set to 1053 complexes, thereby capturing a broad chemical diversity that spans homoleptic {CoX<sub>3</sub>} species (X = C, N, O, S) and heteroleptic motifs such as {CoX<sub>2</sub>Y} and {CoCClX} (X, Y = C, N, O, S, Cl, Br, I). The D values in this data set range from +95 to -222 cm<sup>-1</sup>, with E/D ratios from 0 to 0.32, capturing diverse magnetic behavior. We have developed a machine learning (ML) model using geometric parameters such as bond lengths, angles, and deviations from ideal C<sub>3V</sub> geometry, etc. that predicts D, E/D, and g-factors with over 95% accuracy, with a mean absolute error of ∼12 cm<sup>-1</sup> and aligns well with both CASSCF results and available experimental data. Remarkably, our models also uncovered over 40 new Co(II) complexes with large negative D values (>180 cm<sup>-1</sup>) and low E/D ratios (0.01-0.07), demonstrating the capability of the ML-driven approach to accelerate the discovery of next-generation ambiently stable SIMs. Anisotropy ; Ab initio ; Magnetic anisotropy ; Homoleptic ; Ideal (ethics) ; Magnet ; Ab initio quantum chemistry methods ; Range (aeronautics) ; Molecule Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Erik Mueller | Nancy B. McAtee | Hossain M. Azam
Source: Journal of Failure Analysis and Prevention (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

A Review of Biocorrosion Issues and Mitigation Strategies in Concrete Sewers and Wastewater Collection Systems Sanitary sewer ; Wastewater ; Solid mechanics ; Sewage ; Sewage treatment Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Advanced oxidation water treatment
Authors: Chao Jiang | Weifeng Xie | M. Z. Wang | Tiege Zhou | Wendong Song | Yunliang Yue | Xuelong Hu
Source: Computational Materials Science (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Machine learning in magnetism: Progress, challenges, and perspectives Interpretability ; Complement (music) ; Property (philosophy) ; Curie temperature ; Focus (optics) Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Heusler alloys: electronic and magnetic properties