jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Yang | Jun Li
Source: ChemRxiv (article, 2025-09-10)
Field: Chemistry — Inorganic Chemistry

WDK-VENUS: A Streamlined and Modular Workflowfor High-Temperature Dynamics and Kinetics ofAtom-diatom Collisions High-precision dynamic simulations of hypersonic flows are crucial for hightemperature aerodynamics, particularly in addressing non-equilibrium effects in turbulent flows. The quasi-classical trajectory (QCT) method, based on microscopic molecular collisions, is a key approach to tackle this challenge. By generating state-to-state (StS) integral cross sections (ICSs), QCT simulations enable detailed modeling of high-temperature thermochemical non-equilibrium flows. However, existing dynamic programs do not have an automated workflow that converts trajectory data into reaction rate coefficients. This work introduces a modular QCT Workflow for Dynamics and Kinetics of atom-diatom (A + BC) collisions built upon the VENUS program, WDK-VENUS. The VENUS program is amended to enable handling high rovibrational initial conditions. An automated and efficient bmax test module is introduced. Additionally, an external Python code is provided with four modules: sampling, Gaussian process regression (GPR), model validation, and equilibrium/non-equilibrium rate coefficient calculation. The main function of this workflow is to use GPR machine learning methods to fit models, export large amounts of high-precision ICSs, and calculate equilibrium/non-equilibrium rate coefficients with less human intervention. The modular design of WDK-VENUS simplifies QCT calculations, supports batch processing, and integrates Python analysis tools. This workflow is expected to facilitate broader applications in interdisciplinary fields. Modular design ; Python (programming language) ; Workflow ; Gaussian process ; Kriging ; Trajectory ; Computational fluid dynamics ; Process (computing) Radioactive element chemistry and processing ; Advanced Materials Characterization Techniques ; Mass Spectrometry Techniques and Applications
Authors: Jenoff E. De Vrieze (21820271) | Michiel Verswyvel (1775569) | Kinza Y. Ghulam (21820274) | Bart-Jan Niebuur (2949180) | Tobias Kraus (1362102) | Markus Gallei (605235) | Norbert Niessner (18097229)
Source: Figshare (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Water-Induced Transparency Loss in Styrene ButadieneBlock Copolymers: Mechanism, Morphology, and Predictive Modeling Water-induced transparency loss in styrene–butadiene block copolymers (SBCs) has been investigated under a variety of conditions. Consistent with earlier work on homopolymers, the opacity after prolonged water exposure is expected to be caused by water clustering, which results from stronger water–water than water–polymer interactions. The water clusters distort the surrounding polymer matrix, causing local changes in the refractive index. It was found that the hard phase has only a minor contribution to the transparency loss, while the rubbery phase appears to be the major contributor. However, the loss of transparency was found not to be directly proportional to the volume of the soft phase, and a significant effect of the block copolymer morphology was observed, which was confirmed by a series of transmission electron microscopy and SAXS measurements. This effect is particularly evident in the transition from a continuous hard phase through a co-continuous morphology to a continuous soft phase. The acquired insights were subsequently used to predict long-term optical performance in SBCs to provide a tool in product development. Loss of transparency predictions was proven to be adequate through a classical regression-extrapolation approach using a limited data set, accurately simulating performance beyond 2600 h exposure time using only 600 h of measurement time. Additionally, it was shown that artificial neural networks could provide a solid tool in predicting performance even prior to synthesis, granted that the selection of descriptors is complete and the appropriate amount of data is supplied with a proper spread over the descriptor space. Transparency (behavior) ; Opacity ; Copolymer ; Block (permutation group theory) ; Phase (matter) ; Polymer ; Refractive index Block Copolymer Self-Assembly ; Polymer crystallization and properties ; Machine Learning in Materials Science
Authors: Animesh Karmakar (21538078) | Dhananjay (17700860) | Tarak Karmakar (810867)
Source: Figshare (article, 2025-06-13)
Field: Chemistry — Inorganic Chemistry

Understanding Dynamics of Nanocluster–OrganicFrameworks and Gas Diffusion from Machine Learning Potential-BasedSimulations Nanocluster–organic frameworks (NOFs) are emerging materials with broad applications in sensing, photocatalysis, and optoelectronics. These photofunctional materials have an excellent luminescence switching response to gases, such as oxygen (O<sub>2</sub>) and volatile organic compounds. However, their atomistic structural evolution and the mechanism governing the diffusion of small molecules inside them remain elusive. In this study, we developed machine learning potentials to accurately model an experimentally synthesized NOF, [Ag<sub>12</sub>(S<sup><i>t</i></sup>Bu)<sub>8</sub>­(CF<sub>3</sub>COO)<sub>4</sub>(bpy)<sub>4</sub>)]<sub><i>n</i></sub>, and we investigated its structure and dynamics using molecular dynamics simulations. Furthermore, we used on-the-fly probability-enhanced sampling simulations to study O<sub>2</sub> gas diffusion inside NOF pores and construct the free energy surface. The O<sub>2</sub> gas predominantly localizes around the bipyridine linker, consistent with previous experimental observations. The pore-to-pore diffusion barrier of ∼16–18 kJ/mol for O<sub>2</sub> suggests feasible diffusion of the O<sub>2</sub> at room temperature. This work presents the first-ever integrated approach for modeling a fully flexible NOF and gas diffusion within it with DFT-level accuracy. Diffusion ; Molecular dynamics ; Work (physics) ; Gaseous diffusion ; Dynamics (music) ; Molecule ; Energy (signal processing) Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Magnetism in coordination complexes
Authors: Zacharie Waysenson | Arthur France‐Lanord | Alessandra Serva | Patrice Simon | Mathieu Salanne | A. Marco Saitta
Source: ChemRxiv (preprint, 2025-05-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrode flexibility enhances electrolyte dynamics during supercapacitor charging Supercapacitors are energy storage devices with high power density and long cycle life. Combined with spectroscopy and electrochemistry, molecular simulations and theory have allowed to characterize their charging mechanisms, and we now have an excellent understanding of the effect of properties such as nanopore size or structural disorder on the supercapacitor performances. However, the influence of electrode flexibility remains to be addressed as state-of-the-art models focused on the polarization effects of the electrode, but enforced its structural rigidity, as an approximation. Here we overcome this limitation by integrating a constant‑potential molecular dynamics scheme with a state‑of‑the‑art machine‑learning potential for carbon, while controlling the applied potential. Using nanoporous sp$^2$/sp$^3$ carbon electrodes filled with an ionic liquid electrolyte, we compare the behavior of the rigidified and flexible frameworks; the latter allowing for local atomic relaxation, breathing modes, etc. We demonstrate that flexibility significantly enhances in-pore ionic diffusivity, thus shortening the characteristic charging time by a factor of three relative to the rigid analogue, while the specific capacitance remains in the experimental range ($\approx 140~ \text{F} \cdot \text{g}^{-1}$) for both cases. More specifically, our analyses demonstrate that flexibility accelerates co‑ion expulsion, mitigates pore overcrowding, and promotes a homogeneous induced‑charge profile that penetrates deeply from the very start of the bias onset, explaining the improved kinetics. By explicitly linking the electrochemical response to mechanical and vibrational constraints, this work paves the way toward rational and optimal energy storage device design. Supercapacitor ; Electrolyte ; Flexibility (engineering) ; Electrode ; Materials science ; Chemistry ; Capacitance ; Mathematics Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Zuojun Wang | Jinrui Zhao | Yiang Wang | Junyi Yan | Qingjia Bao | Peng Cao
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2025-09-16)
Field: Chemistry — Spectroscopy

An adaptive deep learning reconstruction for both hyperpolarized 13C magnetic resonance spectroscopic imaging and deuterium metabolic imaging Motivation: Heteronuclear magnetic resonance spectroscopic imaging (MRSI) can assess tumor aggressiveness and response to treatments. Regarding its slow acquisition and starving signal-to-noise ratio, efficient deep-learning reconstruction adaptive for different applications is required. Goal(s): To propose an adaptive deep learning method for reconstructing high-quality MRSI. Approach: A deep learning prior was trained using singular maps extracted from hyperpolarized 13C MRSI and deuterium metabolic imaging (DMI), generated through multi-pool exchange and free induction decay. The prior was incorporated with SPICE and date fidelity terms for MRSI reconstruction. Results: The model was evaluated on various datasets, demonstrating its generalizability in reconstructing high-quality MRSI using high acceleration rates. Impact: The generalizability of the proposed pipeline for high-quality MRSI reconstruction has been demonstrated in various applications, including HP 13C MRSI and DMI, suggesting its feasibility as a molecular imaging tool for both scientific and clinical applications. Magnetic resonance spectroscopic imaging ; Deep learning ; Iterative reconstruction ; Pipeline (software) ; Magnetic resonance imaging ; Generalizability theory ; Real-time MRI Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Inorganic Fluorides and Related Compounds
Authors: Ardiana Topi | Daniel Thimi Hudhra | Dritan Topi
Source: Preprints.org (preprint, 2025-01-13)
Field: Chemistry — Analytical Chemistry

Machine Learning Algorithmic Models for the Authentication of Albanian Mono-Cultivar Olive Oils Distinguished for its nutritional benefits and high economic value, olive oil has faced issues with adulteration and fraud. As production increases, the need to identify olive oil by specific cultivars and regions has become more pressing. Analyzing chemical data related to the origin and olive cultivars will facilitate advanced quality control and authenticity practices for Albanian olive oil, enhancing its competitiveness as an organic product. While traditional empirical methods have been relied upon to detect olive oil fraud and evaluate quality, this study pioneers a modern approach using machine learning algorithms to differentiate authentic products from counterfeits. Establishing effective mechanisms and best practices to trace product origins and quality indicators will raise awareness about the risks of adulteration to both consumers&amp;#039; health and the broader food industry. To enhance the precision of origin predictions, the importance of data pre-processing steps—especially the normalization process following the isolation of independent features from the target variable- particularly crucial for distance-based algorithms like kNN that improve accuracy. Furthermore, performance metrics for all algorithms were evaluated, including k-Nearest Neighbors, Logistic Regression, Support Vector Machines, implement hyperparameter tuning techniques, and the best-performing model. Application of supervised machine-learning methods to categorize Albanian olive oils (OO) according to their chemical composition, aiding in identifying their geographical and cultivar origin. Our results indicate an accuracy of 62.5%, constrained by the limitations of the current dataset; however, we intend to expand the dataset in the future. Computer science ; Machine learning ; Olive oil ; Support vector machine ; Artificial intelligence ; Quality (philosophy) ; Categorization ; Product (mathematics) ; Hyperparameter ; Mathematics ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: Shailendra P. Joshi | Ashley Bucsek | Darren C. Pagan | Samantha Daly | Suraj Ravindran | Jaime Marian | Miguel A. Bessa | Surya R. Kalidindi | Nikhil Chandra Admal | Celia Reina | Somnath Ghosh | Jorge Viñals | Ellad B. Tadmor
Source: arXiv (Cornell University) (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling The design of structural &amp; functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sophisticated computational materials modeling frameworks that span a wide spectrum of length &amp; time scales in the mesoscale between atomistic &amp; continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, several gaps remain in this framework as it relates to advanced structural materials:(1) limited availability &amp; access to high-fidelity experimental &amp; computational datasets, (2) lack of co-design of experiments &amp; simulation aimed at computational model validation,(3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, &amp; (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation &amp; cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic. The proposal is to create a hub for Mesoscale Experimentation and Simulation co-Operation (hMESO)-that will (I) provide curation and sharing of models, data, &amp; codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, &amp; (III) provide a platform for education &amp; workforce development. It will engage experimental &amp; computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, &amp; large-scale cyberinfrastructure initiatives. Cyberinfrastructure ; Mesoscale meteorology ; Key (lock) ; Computational model ; Petascale computing ; Multidisciplinary approach ; Workforce ; Computational mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Taiko Ohshida | Toru Wada | Toshiaki Taniike
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Phase Analysis of Immiscible Polymer Blends via Multi-parameter AFM Imaging Atomic force microscopy ; Phase imaging ; Phase (matter) ; Materials science ; Polymer ; Nanotechnology ; Polymer blend ; Chemical engineering ; Chemistry ; Microscopy ; Composite material ; Optics ; Physics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Qun Chen | Arvind Pattamatta | David J. Srolovitz | Mingjian Wen
Source: ArXiv.org (preprint, 2025-10-05)
Field: Materials Science — Materials Chemistry

Atomistic Machine Learning with Cartesian Natural Tensors Atomistic machine learning (ML) is a transformative tool for accurate and efficient investigation of material behavior at the atomic scale. While such models have been constructed within Cartesian space to harness geometric information and preserve intuitive physical representations, they face inherent challenges - primarily due to the lack of a systematic symmetry-preserving framework for representing arbitrary physical tensors. We address these challenges by proposing Cartesian Natural Tensor Networks (CarNet) as a general framework for atomistic ML. We first develop the theory of irreducible representations using Cartesian natural tensors (their creation, operation, as well as the decomposition and reconstruction of physical tensors such as the elastic constant tensor). Leveraging this machinery, we design an equivariant Cartesian model and demonstrate its exceptional performance across diverse atomistic ML tasks. CarNet enables the development of highly accurate and reliable interatomic potentials for both materials and molecular systems. Furthermore, structure-property relationships can be readily constructed for tensorial quantities ranging from simple properties like the dipole moment to arbitrary high-rank tensors with complex symmetries such as the elastic constant tensor -- capabilities that were previously inaccessible. This work removes theoretical barriers and unleashes the power of Cartesian approaches for advanced atomistic ML in the understanding and design of new materials. Cartesian coordinate system ; Tensor (intrinsic definition) ; Cartesian tensor ; Curse of dimensionality ; Moment (physics) ; Homogeneous space ; Vector space ; Simple (philosophy) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Parichit Sharma | Hasan Kurban | Mehmet Dalkılıç | Mustafa Kurban
Source:  (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Instance-Based Learning-Driven Density of States Analysis in Functionalized Fullerene Derivatives for Optimizing Organic Photovoltaics We employ instance-based machine learning to evaluate functionalized fullerene derivatives as electron-acceptor materials for organic photovoltaics. Using density of states (DOS) data from seven fullerene-based models, we identify key electronic properties that influence photovoltaic efficiency. Our results demonstrate that k-nearest neighbor methods achieve superior predictive accuracy, underscoring the potential of instance-based learning in accelerating material discovery and optimization for next-generation photovoltaics. Organic solar cell ; Fullerene ; Photovoltaics ; Materials science ; Nanotechnology ; Computer science ; Photovoltaic system ; Chemistry ; Polymer ; Engineering ; Organic chemistry ; Electrical engineering ; Composite material Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Bishal Sarkar | Saumendu Deb Roy | Dibyendu Shil
Source: Journal of Biological Methods (article, 2025-11-17)
Field: Materials Science — Biomaterials

Nanogels as next-generation drug delivery systems: Design, advances, and biomedical applications Background: Nanogels are next-generation smart and stimuli-responsive nanocarriers for drug delivery, characterized by high water capacity, excellent biocompatibility, tunable size, and environmental responsiveness. Their nanoscale cross-linked polymer networks enable the delivery and encapsulation of diverse therapeutic agents, such as small molecules, proteins, nucleic acids, and imaging agents. Emerging strategies have facilitated the synthesis of stimuli-responsive nanogels (e.g., redox-, pH-, temperature-, enzyme-responsive), multifunctional hybrid systems, and targeted drug delivery platforms tailored to disease-specific microenvironments. These nanogels have demonstrated promising applications in oncology, infectious diseases, ocular therapy, central nervous system targeting, gene therapy, and vaccine delivery. The integration of machine learning and artificial intelligence has further enabled predictive optimization and formulation. In addition, new approach methodologies, such as organ-on-a-chip and in silico models, provide human-relevant, ethical, and cost-effective approaches for evaluating safety, pharmacokinetics, and therapeutic efficacy. Although challenges related to scale-up, reproducibility, and regulatory approval remain, a number of nanogel-based products are advancing toward clinical application. Objective: This review explores the composition, functionalization, biomedical applications, and future prospects of nanogels, emphasizing their potential to enable precision medicine and drug discovery. Conclusion: Nanogels are promising platforms for precision and personalized medicine, offering routes for controlled, targeted, and intelligent drug delivery that can revolutionize future therapeutic strategies. Nanocarriers ; Drug delivery ; Targeted drug delivery ; Drug ; Gene delivery ; Personalized medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Authors: Dongyu Bai | Junxian Liu | Yihan Nie | Yuantong Gu | Dongchen Qi | Arkady V. Krasheninnikov | Liangzhi Kou
Source: Small Methods (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Ferroelectric Domains and Evolution Dynamics in Twisted CuInP <sub>2</sub> S <sub>6</sub> Bilayers Polar domains and their manipulation-particularly the creation and dynamic control-have garnered significant attention, owing to their rich physics and promising applications in digital memory devices. In this work, using density functional theory (DFT) and deep learning molecular dynamics (DLMD) simulations, it is demonstrated that polar domains can be created and manipulated in twisted bilayers of ferroelectric CuInP<sub>2</sub>S<sub>6</sub>, as a result of interfacial ferroelectric (antiferroelectric) coupling in AA (AB) stacked region. Unlike the topological polar vortex and skyrmions observed in superlattices of (PbTiO<sub>3</sub>)<sub>n</sub>/(SrTiO<sub>3</sub>)<sub>n</sub> and sliding bilayers of BN and MoS<sub>2</sub>, the underlying mechanism of polar domain formation in this system arises from stacking-dependent energy barriers for ferroelectric switching and variations in switching speeds under thermal perturbations. Notably, the thermal stability and polarization lifetimes are highly sensitive to twist angles and temperature, and can be further manipulated by external electric fields and strain. Through multi-scale simulations, this study provides a novel approach to exploring how twist angles influence domain evolution and underscores the potential for controlling local polarization in ferroelectric materials via rotational manipulation. Ferroelectricity ; Condensed matter physics ; Materials science ; Polar ; Molecular dynamics ; Polarization (electrochemistry) ; Skyrmion ; Chemical physics ; Physics ; Dielectric ; Optoelectronics ; Chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies