jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhinav S. Raman | Annabella Selloni
Source: Angewandte Chemie (article, 2025-06-17)
Field: Chemistry — Electrochemistry

Effect of Ambient Organic Acids on the Water Structure at TiO2${\rm TiO}_{2}$ Interfaces Abstract A molecular‐level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide () surfaces is relevant to many of its energy‐related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. These results provide a picture where carboxylic acids control the wettability of largely through acid‐base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self‐cleaning surfaces and photocatalytic devices. Chemistry ; Environmental chemistry ; Chemical engineering Electrochemical Analysis and Applications
Authors: Lijing Li | Yutian Wang | Kai Fan
Source: Food Bioscience (review, 2025-10-28)
Field: Materials Science — Materials Chemistry

Recent advances in carbon dots-loaded composite films for food preservation and freshness monitoring: A review Food packaging ; Food preservation ; Active packaging ; Internet of Things ; Food industry ; Composite number ; Carbon fibers ; Shelf life Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Julia
A. Stebani (22653164) | Iñigo Iribarren Aguirre (22693221) | Gohar A. Siddiqui (22653170) | Darren Wragg (9982520) | Alessio Gagliardi (1301763) | Angela Casini (164243)
Source: Figshare (article, 2025-11-26)
Field: Chemistry — Organic Chemistry

Computational Workflow to Unravel the Structural Dynamicsof Supramolecular Metallacages in Solution The structural dynamics of self-assembled metallacages is important because it determines their function and stability in different applications involving encapsulation and release of a guest molecule. We present here an integrated computational workflow to study the dynamic behavior of selected [Pd<sub>2</sub>L<sub>4</sub>]<sup>4+</sup> metallacages in explicit solvents (water and DMSO) and benchmark them for future <i>in silico</i> investigations of their host–guest chemistry, pivotal to their application as drug delivery systems. Two different pathways for the Molecular Dynamics (MD) simulations of the systems are explored, namely classical force field (FF) and Machine Learning Interatomic Potentials (MLIPs), to assess the conformational changes of two cage systems in solution, enabling evaluation of the performance vs computational cost for both methodologies. The proposed workflow offers a versatile framework to computationally assess the structural dynamics of supramolecular systems in solution, effectively bridging the gap between quantum-level accuracy and the temporal and spatial scales needed for simulations of different functional applications. Workflow ; Bridging (networking) ; Molecular dynamics ; Supramolecular chemistry ; Benchmark (surveying) ; System dynamics ; Field (mathematics) ; Stability (learning theory) Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes ; Surface Chemistry and Catalysis
Authors: Daniel F. Pessoa | Maria Betânia F. Marques | Wagner N. Mussel | Rita C. O. Sebastião
Source: Journal of the Brazilian Chemical Society (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Thermal Decomposition Kinetics of Corticosteroids Prednisone, Betamethasone Valerate and Hydrocortisone Acetate by Applying a MLP Network Corticosteroids are widely prescribed to treat inflammatory conditions. Understanding their thermal stability is essential to ensure their safety and efficacy during storage, processing, and pharmaceutical use. This study investigated the non-isothermal decomposition kinetics of prednisone, betamethasone valerate, and hydrocortisone acetate using thermogravimetric analysis. Vyazovkin Isoconversional Method was employed to evaluate the activation energy profiles, followed by kinetic model fitting to determine the pre-exponential factors. A multilayer perceptron neural network was implemented to identify the most suitable reaction mechanisms and complete the kinetic triplet for the reaction. Thermal analysis revealed three decomposition events for prednisone and betamethasone valerate and two events for hydrocortisone acetate, reflecting the influence of structural differences on their stability. The neural network provided more accurate predictions than the single kinetic models and indicated that most decomposition steps followed the Avrami-Erofeev model. In this context, nucleation and growth are interpreted as the stochastic initiation and propagation of reactive nuclei through molecular rearrangements and diffusion in the amorphous or liquid phases, rather than crystallization phenomena. Although originally developed for isothermal conditions, the Avrami-Erofeev approach has been widely and successfully applied to dynamic heating experiments as a phenomenological model for describing decomposition kinetics. These results provide unprecedented insights into corticosteroid stability, contributing to preformulation, quality control, and pharmaceutical development. Kinetics ; Decomposition ; Activation energy ; Thermal decomposition ; Nucleation ; Betamethasone ; Isothermal process Thermal and Kinetic Analysis ; Drug Solubulity and Delivery Systems ; Microencapsulation and Drying Processes
Authors: Ruihan Wang | Shiqi Wang | Qianji Han
Source: RSC Advances (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Accurate DDEC-charge-guided screening of high-performance metal–organic frameworks for SF<sub>6</sub>/N<sub>2</sub> separation Achieving high SF<sub>6</sub> uptake and SF<sub>6</sub>/N<sub>2</sub> selectivity is a key challenge in gas separation. High-throughput computational screening is an efficient strategy to identify high-performing adsorbents. However, these candidates may be overlooked because most studies rely on empirical partial charge assignments. In this study, we present a data-driven workflow that integrates accurate density-derived electrostatic and chemical (DDEC) partial atomic charges into grand canonical Monte Carlo (GCMC) simulations to accelerate the discovery of high-performance MOFs for SF<sub>6</sub>/N<sub>2</sub> separation. By screening the quantum-chemical metal-organic framework (MOF) database, several top-performing candidates with high SF<sub>6</sub> uptake and selectivity were identified. The key features for efficient separation were open metal sites, parallel aromatic surfaces, uncoordinated nitrogen atoms, and metal-oxygen-metal bridges. A machine learning model trained on the DDEC-based GCMC results achieved excellent predictive performance (coefficient of determination = 0.968, mean absolute error = 0.281 mmol g<sup>-1</sup>) and enabled rapid screening of 154 144 MOFs within 50 min. Zn-TCPP was selected for validation <i>via</i> density functional theory calculations, confirming the reliability of the proposed workflow. This study illustrates how quantum-chemical datasets facilitate high-throughput material discovery for challenging separations. Workflow ; Key (lock) ; Reliability (semiconductor) ; Partial charge ; Selectivity ; Density functional theory ; Monte Carlo method Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Machine Learning in Materials Science
Authors: Wakai, Hayato | Ishiwata, Shintaro | Seko, Atsuto
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global structure searches are performed for 11 bismuth-based binary systems, including Na-Bi, Ca-Bi, and Eu-Bi, under pressures ranging from 0 to 20 GPa, employing polynomial MLPs developed specifically for these systems. The searches reveal numerous compounds not previously reported in the literature and identify all experimentally known compounds that are representable within the explored configurational space. These results highlight the robustness and reliability of the current MLP-based structure search. The study provides valuable insights into the discovery and design of novel bismuth-based materials under both ambient and high-pressure conditions. Robustness (evolution) ; Binary number ; Property (philosophy) ; Ranging ; Binary classification ; Polynomial Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials
Authors: Ivan Saha | Lukáš Grajciar | Daniel Willimetz
Source: ChemRxiv (article, 2025-09-02)
Field: Materials Science — Surfaces, Coatings and Films

CRISP: Enhancing ASE Workflows with Advanced Molecular Simulation Post-Processing Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customisation, interactivity, and efficiency with large datasets. To address this, we developed CRISP (Comprehensive Repository for Insightful Simulation Post-Processing), an open-source Python toolkit designed to enhance workflows within the Atomic Simulation Environment (ASE). CRISP provides a versatile platform for detailed analysis and visualisation, featuring a customizable and modular design, various static analysis methods, interactive 3D visualisations, and parallel processing capabilities optimised for high-performance computing. We demonstrate its effectiveness through case studies, including the analysis of statistical convergence in zeolite simulations, subsampling large datasets for machine learning, and analysing the dynamic stability of atomic clusters. CRISP effectively bridges the gap between raw simulation data and actionable insights, offering an efficient solution for researchers and saving significant time in code development. Workflow ; Computer science ; Process engineering ; Engineering ; Database Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Tomohiro Baba | Lorenzo Gabriel Janairo | Novelyn Maging | Hoshea Sophia Tañedo | Ronnie Concepcion | Jeremy Jay Magdaong | Jose Paolo O. Bantang | Jesson Del-amen | Christian Joseph Ronquillo | Argel A. Bandala | Alvin B. Culaba
Source: AgriEngineering (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Advancements in Thin-Film Thermoelectric Generator Design for Agricultural Applications Thin-film thermoelectric generators (TFTEGs) emerge as critical components of self-sustaining agricultural systems because they can utilize temperature gradients to generate plant-transpiration-induced thermovoltage signal quantifiable to plant health status. This study examines the latest developments in TFTEG materials, device structures, manufacturing processes, and their integration into agricultural systems such as plant-wearable, canopy-level and stem-clipped TEGs. Key questions addressed include the ideal materials for TFTEG fabrication, their biocompatibility and eco-stability in agricultural settings, recent design and AI-assisted optimization advancements, and future research directions in non-conventional TEG applications. The analysis consolidates evidence from inorganic, organic, and hybrid thermoelectric materials with respect to performance in terms of flexibility, thermal stability, output power, and biocompatibility. Bibliometric analysis was employed to determine dominant research topics and gaps, especially with respect to sustainability and AI-augmented design. The review emphasizes the latest breakthroughs in structural optimization, flexible substrates, encapsulation strategies, and sensor integration for reliability enhancement in field environments. In addition, applications of AI, including neural network-based conditional Generative Adversarial Network, surrogate modeling, and multi-objective optimization, are discussed in relation to the improvement of thin-film TEG design and simulation processes. This study suggests that TFTEGs exhibit great potential in agricultural monitoring and plant wearable applications but material toxicity, mechanical degradation, and integration with AI are still major obstacles. Thermoelectric generator ; Sustainability ; Thermoelectric effect ; Reliability (semiconductor) ; Field (mathematics) ; Wearable computer Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Environmental Education and Sustainability
Authors: Xi Chen | Tingyu Yang | Hongshui Wang | Jing Jiang | Xiaoyan Zhao | Chunyong Liang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Advances in the Design and Application of Biodegradable Zinc Alloys and Composites: Dual Perspectives of in vitro-Based and in vivo-Based Properties Zinc ; In vivo ; Dual (grammatical number) ; In vitro ; Materials science ; Composite material ; Chemistry ; Metallurgy ; Biotechnology ; Biology ; Art Electrospun Nanofibers in Biomedical Applications ; Magnesium Alloys: Properties and Applications ; Infectious Aortic and Vascular Conditions
Authors: Haruna Adamu | Usman Bello | Usman Ibrahim Tafida | Khuzaifa Yahuza Muhammad | Kolo Alhaji Modu | Nurul Aini Amran | Umair Alam | Mohammad Qamar
Source: Journal of Energy Storage (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Beyond batteries: Architecting materials through surface chemistry and engineering for revolutionizing hydrogen storage MXenes ; Hydrogen storage ; Surface engineering ; Bridging (networking) ; Energy storage ; Supercapacitor ; Hydrogen ; Energy carrier ; Dehydrogenation Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion
Authors: Dr S N Prasad Kavya B S | D.Kalai Abirami
Source: International Journal of Innovative Research in Advanced Engineering (article, 2025-11-22)
Field: Chemistry — Analytical Chemistry

Automated Meat Quality Detection Using DenseNet-121 Transfer Learning Accurate, rapid evaluation of meat quality is essential in modern food processing and retail chains, directly impacting consumer health, regulatory standards, and economic stability. Conventional inspection methods based on manual observation or basic chemical assays—frequently suffer from subjectivity, variability, and inefficiency, motivating the exploration of advanced, automated alternatives. This research introduces a comprehensive deep learning framework employing DenseNet-121, a state-of-the-art convolutional neural network, to classify meat samples by freshness using digital images. By leveraging transfer learning on well-curated datasets and implementing robust preprocessing and augmentation strategies, the system efficiently extracts and interprets relevant color, texture, and morphological features indicative of spoilage and quality. Extensive experiments reveal that the proposed model achieves superior classification performance compared to traditional vision techniques, with substantial improvements in precision, recall, and robustness to input variations. The solution demonstrates high practical value for industrial automation, regulatory monitoring, and consumer-facing applications, paving the way for scalable, non-destructive, and objective meat quality assessment. Challenges related to dataset diversity, domain adaptation, and integration with complementary sensor modalities are acknowledged, highlighting fruitful directions for future investigation. Robustness (evolution) ; Preprocessor ; Convolutional neural network ; Transfer of learning ; Quality (philosophy) ; Deep learning ; Data pre-processing ; Domain (mathematical analysis) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Wakai, Hayato | Ishiwata, Shintaro | Seko, Atsuto
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global structure searches are performed for 11 bismuth-based binary systems, including Na-Bi, Ca-Bi, and Eu-Bi, under pressures ranging from 0 to 20 GPa, employing polynomial MLPs developed specifically for these systems. The searches reveal numerous compounds not previously reported in the literature and identify all experimentally known compounds that are representable within the explored configurational space. These results highlight the robustness and reliability of the current MLP-based structure search. The study provides valuable insights into the discovery and design of novel bismuth-based materials under both ambient and high-pressure conditions. Robustness (evolution) ; Binary number ; Property (philosophy) ; Ranging ; Binary classification ; Polynomial Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials
Authors: Yue Yu | Francesco Calcagno | Haote Li | Víctor S. Batista
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-18)
Field: Materials Science — Materials Chemistry

QOBRA: A Quantum Operator-Based Autoencoder for <i>De Novo</i> Molecular Design We introduce a variational quantum autoencoder tailored for de novo molecular design named QOBRA (Quantum Operator-Based Real-Amplitude autoencoder). QOBRA le circuits for real-amplitude encoding and the SWAP test to estimate reconstruction and latent-space regularization errors during back-propagation. Adjoint encoderand decoder unitary transformations and a generative process that ensures accurate reconstruction as well as novelty, uniqueness, and validity of the generated samples. We showcase QOBRA as applied to de novo design of Ca 2+ -, Mg 2+ -, and Zn 2+ -binding metalloproteins after training the generative model with a modest dataset. Significance Statement Recent advancements in classical generative machine learning have shown significant strides in molecular design for targeted applications. N onetheless, these advancements are fundamentally limited by classical computation based on binary units. We introduce a quantum computation-based ML framework employing qubits, which exhibits the ability to synthesize de novo molecular instances with specified properties from limited datasets. Quantum networks require exponentially fewer parameters than classical ones, enhancing their trainability and efficiency. While our demonstration focuses on metalloprotein primary sequences, the paradigm is adaptable to diverse molecular designs. This integration of AI and quantum computing holds potential to expand the scientific and technological frontiers of both domains within a practical framework. Autoencoder ; Quantum ; Quantum computer ; Encoding (memory) ; Binary number ; Generative model ; Generative grammar ; Computation Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Quanwei Yu (2676865) | Yujie Liu (188278) | Zhijia Wang (1589848) | Chengyong Wu (6650366) | Ruofei Zhang (3589421) | Lun Tan (4372222) | Lele Zhang (388913) | Cuiyu Guo (19359525) | Lantu Gou (2279446) | Weimin Li (131040) | Yang He (307595) | Yuxi Wang (450170)
Source: Figshare (article, 2025-09-20)
Field: Chemistry — Organic Chemistry

Discovery ofNovel 4,5-Dihydropyrrolo[3,4‑<i>c</i>]pyrazol-6(<i>2H</i>)‑one-Based TubulinInhibitors Targeting Colchicine Binding Site with Potent Anti-OvarianCancer Activity To address the toxicity of current microtubule inhibitors, we employed the GeminiMol deep learning model to screen the Zinc20 database, identifying a novel 4,5-dihydropyrrolo­[3,4-<i>c</i>]­pyrazol-6­(2<i>H</i>)-one scaffold (<b>Y1</b>) targeting the colchicine binding site. Subsequent optimization culminated in <b>Y60s</b>, a potent antiproliferative agent (SKOV3 IC<sub>50</sub> = 0.025 μM) that inhibits clonogenic formation, migration, and invasion of ovarian cancer cells. <b>Y60s</b> inhibited tubulin polymerization which in turn induced G2/M arrest and apoptosis in SKOV3 cells. <b>Y60s</b> demonstrated potent antitumor activity without observable toxicity in an SKOV3 xenograft model. The cocrystal structure of <b>Y8</b> in complex with tubulin was resolved, confirming the key binding mode of 4,5-dihydropyrrolo­[3,4-<i>c</i>]­pyrazol-6­(2<i>H</i>)-one compounds. This work showcases <b>Y60s</b> as a promising novel tubulin inhibitor and highlights the utility of deep learning model for rapid identification of bioactive compounds from large chemical databases. Colchicine ; Tubulin ; Microtubule ; Cancer ; Cancer cell ; Binding site ; Apoptosis ; Clonogenic assay Synthesis and biological activity ; Microtubule and mitosis dynamics ; Curcumin's Biomedical Applications
Authors: Kun Zang (475609) | Zezhong Wang (15138025) | Meilisha Xu (21491177)
Source: Figshare (article, 2025-06-05)
Field: Chemistry — Analytical Chemistry

From Neural Networksto Transformers: Achieving Highand Fast Precision in Fluorescence Correlation Spectroscopy Analysis Fluorescence correlation spectroscopy (FCS), known for its high sensitivity and temporal resolution, is a crucial tool for understanding molecular dynamics. This study develops a neural network-based fitting scheme and introduces the Fast Correlation Fitting Model (FCFM). The neural network fitting scheme accurately fits multiple parameters in FCS data. The FCFM, incorporating the Transformer architecture, a custom CorrelationLoss function, and an optimized process, enhances fitting accuracy and computational efficiency, surpassing traditional methods by 2 orders of magnitude in fitting speed. This provides an effective solution for FCS data analysis. By integrating a pretraining-fine-tuning approach, parameter mapping, and dynamic noise adjustments with CorrelationLoss fine-tuning, FCFM rapidly converges and captures FCS data features for quick and precise fitting. Its efficient parallel computing capabilities, coupled with CUDA acceleration, allow real-time data analysis even on low-specification hardware, making it more accessible to researchers. Our results show that FCFM achieves comparable fitting accuracy and exhibits enhanced performance on certain parameters compared to the classic weighted Levenberg–Marquardt algorithms, enabling rapid simultaneous fitting of thousands of data sets on a personal computer. This provides a tool for real-time acquisition of FCS data parameters in high-throughput FCS methods. The model’s flexibility to adapt to various formula-fitting needs demonstrates its broad application potential in analytical chemistry and other fields. Artificial neural network ; Curve fitting ; Sensitivity (control systems) ; Noise (video) ; Flexibility (engineering) ; Experimental data ; Correlation coefficient Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Advanced Fluorescence Microscopy Techniques