jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Caini Zheng | Ke Luo | Soumi Das | Pengyu Chen | Zhengyuan Shen | Joseph Jaye | Daoyuan Li | Michelle A. Calabrese | Theresa M. Reineke | Mahesh K. Mahanthappa | Kevin D. Dorfman | Timothy P. Lodge | J. Ilja Siepmann
Source: The Journal of Physical Chemistry B (article, 2025-07-31)
Field: Chemistry — Organic Chemistry

Exploring the Self-Assembly of Glycolipids into Three-Dimensional Network Phases Glycolipids are sugar-based amphiphiles that play crucial roles in many biological processes. Under thermotropic and lyotropic conditions, glycolipids self-assemble into a variety of mesophases, including cocontinuous network phases, such as the double gyroid. In this work, a two-stage molecular dynamics simulation workflow is developed to probe network formation for solvent-free amphiphiles at different temperatures. In the first stage, the structural evolution of systems initiated in lamellar and hexagonally packed cylinder arrangements is examined as an indicator of the likelihood of network formation. In the second stage, initial configurations for four network phases are obtained by applying an external guiding field to overcome any nucleation barriers, and the stability of these network phases after switching off the guiding field is investigated. A convolutional neural network is trained and applied to assign a morphology to each snapshot from the simulation. Three anomerically pure and water-free glycolipids (2-decyl-tetradecyl-d-maltoside, 2-decyl-tetradecyl-d-cellobioside, and 2-decyl-tetradecyl-d-galactoside) were examined in this study, and the predicted phase diagrams show good alignment with experimental observations from differential scanning calorimetry and temperature-dependent small-angle X-ray scattering. The workflow presents a facile approach to probe network phase stability and paves the way for the discovery of new network-forming amphiphiles. Glycolipid ; Chemistry ; Materials science ; Biochemistry Surfactants and Colloidal Systems ; Supramolecular Self-Assembly in Materials ; Lipid Membrane Structure and Behavior
Authors: Alexandra M. Vázquez-Salgado | Sara Zalba | Juan José Lasarte | Marı́a J. Garrido
Source: Advanced Drug Delivery Reviews (review, 2025-12-12)
Field: Materials Science — Biomaterials

Emerging nanoparticle-based therapies for pancreatic cancer: Current clinical landscape Pancreatic cancer ; Clinical trial ; Tumor microenvironment ; Immune system ; Drug delivery ; Pancreatic ductal adenocarcinoma ; Extracellular vesicles ; Drug ; Organ-on-a-chip ; Nanomedicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Pancreatic and Hepatic Oncology Research
Authors: Hao Zhou | Khalid Zobaid Adnan | Wyatt Allen Jones | Tianli Feng
Source: Physical review. B./Physical review. B (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Thermal boundary conductance in standalone and nonstandalone GaN/AlN heterostructures predicted using machine-learning interatomic potentials GaN/AlN interfaces are essential in advanced high-power and high-frequency electronic devices, where effective thermal management is crucial for optimal performance and reliability. This work investigates the thermal boundary conductance (TBC) of standalone and nonstandalone GaN/AlN heterostructures using nonequilibrium molecular dynamics (NEMD) driven by accurate machine learning interatomic potentials trained from density-functional theory calculations. For the standalone interface, the TBC is found to be $\ensuremath{\sim}600\phantom{\rule{0.16em}{0ex}}\mathrm{MW}\phantom{\rule{0.28em}{0ex}}{\mathrm{m}}^{\ensuremath{-}2}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$ at room temperature after quantum correction. The result revises previous NEMD predictions ($400--2000\phantom{\rule{0.16em}{0ex}}\mathrm{MW}\phantom{\rule{0.28em}{0ex}}{\mathrm{m}}^{\ensuremath{-}2}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$) using empirical interatomic potentials. When a second GaN/AlN interface is brought close to the original interface, the TBC of the original interface can increase to $1000\phantom{\rule{0.16em}{0ex}}\mathrm{MW}\phantom{\rule{0.28em}{0ex}}{\mathrm{m}}^{\ensuremath{-}2}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$, and this value gradually decreases with increasing distance between the two interfaces. When more interfaces are introduced in proximity, the original interface's TBC can be further enhanced to above $1150\phantom{\rule{0.16em}{0ex}}\mathrm{MW}\phantom{\rule{0.28em}{0ex}}{\mathrm{m}}^{\ensuremath{-}2}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$. After comparing double interfaces, superlattices, and random multilayers, it is concluded that such enhancement of TBC is not caused by the emergence of superlattice modes but rather by the ballistic transport of the existing phonon modes of each material. Additionally, a ``critical separation distance'' (${l}_{\mathrm{cs}}$) is defined as the threshold beyond which the two interfaces no longer influence each other and behave independently. ${l}_{\mathrm{cs}}$ is determined by the mean free path of the phonons filtered by the interfaces that transport in the middle layer between two interfaces. Our findings provide insights into the thermal transport mechanisms that may aid the design of future electronic devices. Heterojunction ; Phonon ; Superlattice ; Thermal conductivity ; Interatomic potential ; Non-equilibrium thermodynamics ; Conductance ; Work (physics) ; Mean free path Thermal properties of materials ; GaN-based semiconductor devices and materials ; Machine Learning in Materials Science
Authors: Xueliang Sun | Shuang Lu | Shuyue Shan | Zhongwei Zhang | Jie Chen
Source: Physical review. B./Physical review. B (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Origin of superdiffusive thermal transport in one-dimensional van der Waals atomic chains As a unique transport regime at the nanoscale, superdiffusive thermal transport has demonstrated its significance in nanophysics and potential applications in nanoscale thermal management. However, notable controversies between theoretical and experimental works persist regarding its origin in one-dimensional (1D) systems. Here we investigate the superdiffusive thermal transport behavior in 1D van der Waals $\mathrm{NbS}{\mathrm{e}}_{3}$ nanowires using molecular dynamics and lattice dynamics simulations combined with a machine learning potential. As a signature of superdiffusive thermal transport, a power-law length-dependent thermal conductivity $\ensuremath{\kappa}\ensuremath{\propto}{L}^{\ensuremath{\beta}}$ is uncovered at small diameters. The effects of phonon confinement and elastic stiffening on superdiffusive thermal transport are elucidated. We demonstrate that the stiffening of the elastic constant only appears along the nanowires, affecting a subset of phonon modes with minimal impact on thermal transport. The low-frequency phonons, which significantly contribute to thermal transport, are predominantly affected by phonon confinement. Our theoretical analysis reveals that phonon confinement enhances the momentum-conserving scattering events, inducing phonon hydrodynamics and enabling the observed superdiffusive thermal transport. Our results provide valuable insights into nanoscale thermal transport, which are crucial for phonon transport and thermal management at the nanoscale. van der Waals force ; Physics ; Thermal ; Van der Waals strain ; Van der Waals radius ; Van der Waals surface ; Condensed matter physics ; Statistical physics ; Materials science ; Quantum mechanics ; Thermodynamics ; Molecule Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Maryna Kachaeva | Stepan Pilyo | Victor Zhirnov | Vasyl Kovalishyn | Ivan Semenyuta | Yevheniia Velihina | Larysa Metelytsia | В. С. Броварец
Source: SynOpen (article, 2025-08-21)
Field: Chemistry — Organic Chemistry

Design, Synthesis, Anticancer Screening, and Virtual Analysis of New 7-Sulfonyldiazepane- and 7-Sulfonylpiperazine-Substituted Oxazolo[4,5-d]pyrimidines Abstract The design of novel anticancer agents has been advanced by applying machine learning techniques, leading to several classification and regression structure-activity relationship models specifically tailored for the human glioblastoma SNB-75. Using these models, a virtual library was screened to identify compounds with potential activity against the SNB-75 cell line. A series of new 7-sulfonyldiazepane- and 7-sulfonylpiperazine-substituted oxazolo[4,5-d]pyrimidines were synthesized and evaluated for their antitumor activity against the NCI-60 cell lines. 2-(4-Methylphenyl)-5-phenyl-7-[4-(phenylsulfonyl)piperazin-1-yl][1,3]oxazolo[4,5-d]pyrimidine demonstrated cytotoxic activity and a high level of selectivity to CNS SNB-75, while 2-(4-methylphenyl)-7-[4-(methylsulfonyl)-1,4-diazepan-1-yl]-5-phenyl[1,3]oxazolo[4,5-d]pyrimidine showed very high activity against the non-small lung cancer NCI-H460 cell line. The compounds met druglike criteria according to ADMET analysis. Molecular docking investigations of synthesized compounds demonstrated complex formation with carbonic anhydrase XII as a potential mechanism. This makes these compounds attractive for further study as agents against non-small cell lung cancer and CNS cancer. Virtual screening ; Combinatorial chemistry ; Chemistry ; Computer science ; Stereochemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Synthesis of heterocyclic compounds
Authors: Prasanta Bandyopadhyay | Bienfait Kabuyaya Isamura | Paul L. A. Popelier
Source: The Journal of Chemical Physics (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Accurate prediction of electron correlation energies of topological atoms by delta learning from the Müller approximation FFLUX is a polarizable machine-learning force field that deploys pre-trained kernel-based models of quantum topological properties in molecular dynamics simulations. Despite a track record of successful applications, this unconventional force field still uses Lennard-Jones parameters to account for dispersion effects when performing in-bulk simulations. However, optimal Lennard-Jones parameters are system-dependent and not easy to calibrate. Fortunately, physics-informed dispersion energies can be obtained from the two-particle density matrix (2PDM) of any system using correlated wavefunctions. The only challenge is that the 2PDM is a humongous object whose calculation is very time-consuming and memory-greedy. In this proof-of-concept study, we utilize the Δ-learning method to address both problems using a small set of water trimers. More specifically, we obtain pure two-electron correlation energies with the aug-cc-pVDZ basis set at the cost of Müller-approximated 2PDM calculated at a very small basis set, 6-31+G(d). We also benchmark different Δ-learning tasks designed by changing the baseline and target method and/or the basis set. Our experiments suggest that two-electron correlation energies of weakly relaxed water trimers can be accurately predicted via Δ-learning with a maximum absolute error of 1.30 ± 0.32 kJ/mol traded against a colossal computational speed-up of roughly 40 times. Basis set ; Basis (linear algebra) ; Wave function ; Statistical physics ; Kernel (algebra) ; Physics ; Electron ; Force field (fiction) ; Algorithm ; Computer science ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Density functional theory ; Geometry ; Discrete mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Stephen Yaw Owusu | Mark Amo-Boateng | Rushi U. Soni
Source: Journal of Materials Chemistry B (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Machine learning predictions of drug release from isocyanate-derived aerogels This work utilized machine learning (ML) algorithms to predict and validate the <i>in vitro</i> drug release kinetics of a short worm-like nanostructured isocyanate-derived aerogel: the first time ML has been employed to study the drug delivery properties of this important class of materials. The algorithms were first trained with sixteen datasets, each containing eight release data points, before using them to predict the release profiles of the unknown. The predicted data was validated <i>via</i> the random sampling and cross-validation techniques. In both instances, the established models were used to predict the release kinetics of four aerogel nanostructures with known experimental release profiles. A good correlation between the experimental and predicted release profiles was observed, with gradient boosting being the best-performing algorithm (<i>R</i><sup>2</sup> > 0.9). Furthermore, the ranking of the importance of each input feature for drug release from the aerogels aligns with previous studies, validating the rationale behind the modeling. Morphology, quantified by the <i>K</i>-index (contact angle/porosity), and the macropore-to-mesopore ratios were found to be the most influential factors, after time, in determining drug release profiles. The findings from this study suggest that ML can serve as a valuable tool for predicting the drug release kinetics of aerogels, thereby saving time and cost involved in conducting laborious drug delivery experiments. We envisage that this study will provide a foundation for future related computational works and reduce the trial-and-error experimental approach to solving scientific problems. Isocyanate ; Aerogel ; Drug ; Materials science ; Drug delivery ; Work (physics) ; Kinetics ; Chemical engineering ; Composite material ; Nanotechnology ; Mechanical engineering ; Engineering ; Pharmacology ; Polyurethane ; Medicine Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Qi Li | J. Zhao | Na Lin | Xiufeng Cheng | Xian Zhao | Zhaojun Liu | Zhitai Jia | Mengyuan Hua
Source: The Journal of Physical Chemistry Letters (article, 2025-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Edge-Dependent Step-Flow Growth Mechanism in β-Ga<sub>2</sub>O<sub>3</sub> (100) Facet at the Atomic Level Homoepitaxial step-flow growth of high-quality β-Ga<sub>2</sub>O<sub>3</sub> thin films is essential for the advancement of high-performance Ga<sub>2</sub>O<sub>3</sub>-based devices. In this work, the step-flow growth mechanism of the β-Ga<sub>2</sub>O<sub>3</sub> (100) facet is explored by machine-learning molecular dynamics simulations and density functional theory calculations. Our results reveal that Ga adatoms and Ga-O adatom pairs, with their high mobility, are the primary atomic species responsible for efficient surface migration on the (100) facet. The asymmetric monoclinic structure of β-Ga<sub>2</sub>O<sub>3</sub> induces a distinct two-stage Ehrlich-Schwoebel barrier for Ga adatoms at the [00-1] step edge, contributing to the suppression of double-step and hillock formation. Furthermore, a miscut toward [00-1] does not induce the nucleation of stable twin boundaries, whereas a miscut toward [001] leads to the spontaneous formation of twin boundaries. This research provides meaningful insights not only for high-quality β-Ga<sub>2</sub>O<sub>3</sub> homoepitaxy but also the step-flow growth mechanism of other similar systems. Facet (psychology) ; Enhanced Data Rates for GSM Evolution ; Mechanism (biology) ; Flow (mathematics) ; Materials science ; Crystallography ; Chemistry ; Physics ; Computer science ; Mechanics ; Psychology Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Mwende Mbilo | Robinson Musembi | John Peter Kachira | Martin Nyamunga | Ibrahim Musanyi | Samuel Wafula | Madallah Yusuf
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2025-07-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First Principles Study of the Properties of Cs2GaAgF6 Double Halide Perovskite Compound for Optoelectronic and Thermoelectric Applications Halide ; Perovskite (structure) ; Thermoelectric effect ; Materials science ; Optoelectronics ; Inorganic chemistry ; Chemistry ; Crystallography ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Perovskite Materials and Applications ; Inorganic Fluorides and Related Compounds
Authors: Erman Çokduygulular | Çağlar Çetinkaya
Source: Solar RRL (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Novel Photovoltaic Optimization for Tailored Ultra‐Thin CdTe‐Based Solar Cells This study presents a machine learning‐based design framework utilizing deep Q‐learning (DQL) to optimize ultra‐thin CdTe solar cells with active layer thicknesses ranging from 100 to 400 nm. By coupling the transfer matrix method for optical analysis with SCAPS‐1D simulations for electrical modeling, the DQL agent effectively explored the complex parameter space, optimizing the thicknesses of all key layers, including SnO 2 , CdS, CdTe, MoO 3 , and Au. The DQL framework intelligently adjusted each layer based on electromagnetic wave propagation and absorption profiles, enhancing internal reflection and light trapping within sub‐micron geometries. Even at extremely low absorber thicknesses (e.g., 100 nm), the optimized structures achieved high photovoltaic performance, with power conversion efficiencies up to 9.39% and J sc values exceeding 11 mA/cm 2 . At 400 nm, efficiency increased to 15.75% with J sc of 20.86 mA/cm 2 . These results demonstrate that efficient photon harvesting and carrier transport are achievable through full‐stack optimization. External quantum efficiency and absorption spectra confirmed the integrated optical‐electrical enhancement achieved by DQL. This work highlights the capabilities of reinforcement learning in high‐dimensional solar cell design problems and provides a scalable approach for developing next‐generation, lightweight, efficient, and material‐conscious photovoltaic technologies. Cadmium telluride photovoltaics ; Photovoltaic system ; Materials science ; Thin film ; Thin film solar cell ; Optoelectronics ; Engineering physics ; Computer science ; Nanotechnology ; Engineering ; Electrical engineering Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Mengru Zhang | Teng Ma | Murat Ozlek | A. Özgür Yazaydın
Source: Journal of Colloid and Interface Science (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Machine learning-assisted exploration of covalent organic frameworks for short-chain per- and polyfluoroalkyl substances (PFAS) removal from water Short-chain per- and polyfluoroalkyl substances (PFAS) are increasingly being adopted as alternatives to long-chain PFAS, which have been regulated or banned in several countries due to their persistence in water and soil and associated human health risks. However, short-chain PFAS are also environmentally persistent and highly mobile, and research on their adsorptive removal from water remains scarce. This challenge is compounded by the poor performance of conventional adsorbents for short-chain PFAS. In this study, we employed machine learning-assisted computational molecular modeling to investigate the removal of perfluorobutanoic acid (PFBA), a representative short-chain PFAS, using covalent organic frameworks (COFs). We examined the structure-performance relationship governing PFBA selectivity in the presence of water. Our results show that the type of linkage plays a critical role in PFBA selectivity. Specifically, triazine-, borazine-, boronate ester-, and borosilicate-based COFs exhibit the highest PFBA selectivity. However, because boron-based COFs are known to have poor water stability, triazine-based COFs emerge as the most promising candidates for PFBA removal from water. The impact of fluorine functionalization depends strongly on the linkage type: it enhances selectivity in azine- and imine-based COFs but reduces it in symmetric hexagonal linkages such as borazine, triazine, and boroxine. This reduction is attributed to fluorine's high electronegativity disrupting the electron density distribution in these symmetric structures. Covalent bond ; Selectivity ; Electronegativity ; Adsorption ; Surface modification ; Fluorine ; Human health Covalent Organic Framework Applications ; Per- and polyfluoroalkyl substances research ; Luminescence and Fluorescent Materials
Authors: Filipe Pereira | Helena Lopes | Leandro Pinto | Filomena Soares | Rosa Vasconcelos | José Machado | Vı́tor Carvalho
Source: Textile Research Journal (article, 2025-04-25)
Field: Materials Science — Polymers and Plastics

Yarn quality analysis by using computer vision and deep learning techniques Yarn quality is important in the manufacture of fabrics and textile products. Yarn analysis ensures that final products meet desired quality standards. Furthermore, the quality of the yarn directly affects the characteristics of the fabric, such as strength, durability, texture, and appearance. Therefore, the textile industry uses yarn quality monitoring and control methods throughout the entire production process, from the selection of raw materials to the manufacture of finished products. This helps to prevent defects, minimize waste, and ensure customer satisfaction. There is currently a commercial equipment, USTER TESTER 6, that measures yarn quality in an industrial environment, which is made up of intelligent sensors of different types. This equipment, characterized by its high cost and size, collects some yarn parameters, such as mass, hairiness, spectrogram, and twist. However, there is a gap in the market for a low-cost system capable of obtaining more characteristics of the yarn, integrated into an industrial environment, and using nondestructive samples. This paper presents an innovative prototype for yarn analysis using computer vision and deep learning techniques, with remote access, able to respond to the needs of Industry 4.0 and industrial digitalization. The prototype demonstrates significant advancements in performance metrics, with improvements of 5–6% in mAP0.5 and 11–12% in mAP0.5:0.95 compared with the standard YOLOv5s6 model. Using a robust 10k-fold cross-validation, the system ensures reliable performance evaluation on unseen data. Comparisons with USTER TESTER 3 indicate a relative error below 4% for parameters such as diameter and linear mass, validating the prototype’s accuracy in key measurements. Yarn ; Quality (philosophy) ; Artificial intelligence ; Computer vision ; Computer science ; Deep learning ; Engineering drawing ; Computer graphics (images) ; Engineering ; Mechanical engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Surface Roughness and Optical Measurements