jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yao Xu | Leyu Lin | Alois K. Schlarb
Source: Polymer (article, 2025-09-02)
Field: Materials Science — Biomaterials

Simulation-supported optimization of interlayer diffusion and crystallization in nanosilica-reinforced polylactic acid (PLA) manufactured by material extrusion Material extrusion (MEX) additive manufacturing has emerged as a promising approach for fabricating customized polymer components. However, optimizing interlayer bonding and crystallization remains a significant challenge, particularly for polylactic acid (PLA), due to competing effects of thermal history, crystallization kinetics, and limited interlayer diffusion. In this study, a simulation-supported interlayer diffusion model is proposed to elucidate the effects of processing parameters and material modifications on the interfacial diffusion and mechanical performance of PLA and nanosilica-reinforced PLA (PLA-S) nanocomposites. A time-dependent thermal field was established through finite element simulation, capturing the dynamic temperature evolution of printed layers. Based on this, a temperature-dependent diffusion-based theoretical framework was developed to quantify interlayer diffusion and its relation to crystallization behavior. The results revealed that although nanosilica accelerates crystallization, it simultaneously reduces interfacial diffusion during printing, the improved intrinsic stiffness of the nanocomposite compensates for the reduced diffusion, resulting in enhanced mechanical performance under optimized conditions. This work establishes a quantitative framework linking thermal simulation, crystallization kinetics, and mechanical evaluation, offering a strategy for optimizing processing-structure-property relationships in polymer-based additive manufacturing. • Nanosilica accelerates PLA crystallization but hinders interfacial diffusion. • Optimal strength is achieved when diffusion surpasses the critical threshold. • The results offer a general framework for process–structure-property optimization. Polylactic acid ; Crystallization ; Extrusion ; Materials science ; Diffusion ; Composite material ; Chemical engineering ; Polymer chemistry ; Polymer science ; Polymer ; Engineering ; Thermodynamics biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Xiaokai Liu | Hao He | Li Wang
Source: Environmental Technology (article, 2025-06-07)
Field: Materials Science — Biomaterials

Study on the structure characterisation and swelling properties of the CMSM In order to further understand the potential applications of cellulose membrane materials in adsorption, drug delivery, and biomedical applications, to optimise their material properties, improve their performance, and expand their application fields, carboxymethyl Salix powder (CMS) was prepared by etherification reaction using Salix powder (SPP) as the raw material. Carboxymethyl Salix powder membrane (CMSM) was subsequently prepared through wet spinning technology. The swelling properties of CMSM were investigated. The results showed that the swelling degree reached the maximum at 45 min, pH 5, temperature of 65°C, and a NaCl concentration of 0.04 mol/L, reaching 7.95 g/g. Through model fitting, it was found that CMSM fits well with the Fickian diffusion model in the early stage, while the entire swelling process fits well with the Schott model. Characterisation results indicate that the structure of CMSM is loose, porous, and uneven on the surface. This structure facilitates the diffusion of water molecules and enhances the water absorption performance of CMSM. The structure of CMSM is similar to the disordered structure of glass or plastic, presenting an amorphous structure. Additionally, CMSM exhibits good thermal stability. By regulating the swelling behaviour of CMSM, this study can develop innovative membrane materials with environmental responsiveness and multifunctionality, promoting the application of green and sustainable materials and providing theoretical support for the design of new composite membranes. Swelling ; Materials science ; Chemistry ; Chemical engineering ; Composite material ; Engineering Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications
Authors: David Mittermayr | Sandra Czaker | Wolfgang Roland | Joerg Fischer
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Diffusion Coefficients of Organic Compounds in Molten High-Impact Polystyrene Polystyrene ; Volatility (finance) ; Diffusion ; Thermal diffusivity ; Mass transfer ; Polymer ; Residual ; Thermal Polymer crystallization and properties ; Effects and risks of endocrine disrupting chemicals ; Membrane Separation and Gas Transport
Authors: Zhen Yang | Guibao Shen | Mimi Li | Liang Hou | Mushui Liu | Luozhou Wang | Tao Xin | Pengfei Wan
Source: arXiv (Cornell University) (preprint, 2025-03-04)
Field: Materials Science — Materials Chemistry

RectifiedHR: Enable Efficient High-Resolution Synthesis via Energy Rectification Diffusion models have achieved remarkable progress across various visual generation tasks. However, their performance significantly declines when generating content at resolutions higher than those used during training. Although numerous methods have been proposed to enable high-resolution generation, they all suffer from inefficiency. In this paper, we propose RectifiedHR, a straightforward and efficient solution for training-free high-resolution synthesis. Specifically, we propose a noise refresh strategy that unlocks the model's training-free high-resolution synthesis capability and improves efficiency. Additionally, we are the first to observe the phenomenon of energy decay, which may cause image blurriness during the high-resolution synthesis process. To address this issue, we introduce average latent energy analysis and find that tuning the classifier-free guidance hyperparameter can significantly improve generation performance. Our method is entirely training-free and demonstrates efficient performance. Furthermore, we show that RectifiedHR is compatible with various diffusion model techniques, enabling advanced features such as image editing, customized generation, and video synthesis. Extensive comparisons with numerous baseline methods validate the superior effectiveness and efficiency of RectifiedHR. Diffusion ; Hyperparameter ; Rectification ; Image (mathematics) ; Noise (video) ; Energy (signal processing) ; Efficient energy use ; Image processing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Stochastic Gradient Optimization Techniques
Authors: Hongbin Zhuang | Junfeng Wang
Source: Chemical Physics Letters (article, 2025-01-26)
Field: Materials Science — Materials Chemistry

Porous polymer films with nanoscale Turing patterns by polymerization-induced liquid interfacial self-assembly Herein, we design a polymerization-induced liquid interfacial self-assembly strategy inspired by the Turing diffusion model for preparing the porous polymer films with Turing patterns. We find that the porous polymer films can be obtained when the polymerization rate (Pr) of solvophilic blocks is at least 3 times that of the Pr of solvophobic blocks, and the pore structure and the pore size can be regulated by the concentration ratio (R C ) and polymerization rate ratio (R P ) of solvophilic block's monomers to solvophobic block's monomers. We also demonstrate that intact porous polymer films cannot be self-assembled by the traditional block copolymers . Porous polymer films with nanoscale Turing patterns by polymerization-induced liquid interfacial self-assembly. • Polymerization-induced liquid interface self-assembly strategy inspired by the Turing model. • The pattern and size of pores can be regulated by the polymerization rate ratio and the concentration ratio. • The intact porous polymer films cannot be prepared by the traditional block copolymers . Nanoscopic scale ; Polymerization ; Polymer ; Porosity ; Materials science ; Self-assembly ; Turing ; Chemical engineering ; Nanotechnology ; Plasma polymerization ; Polymer chemistry ; Chemical physics ; Chemistry ; Composite material ; Computer science Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Block Copolymer Self-Assembly
Authors: Duncan Rose | R. Jacob | Johannes Kirchmair | Thierry Langer
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

NEAT: Neighborhood-Guided, Efficient, Autoregressive Set Transformer for 3D Molecular Generation Transformer-based autoregressive models offer a promising alternative to diffusion- and flow-matching approaches for generating 3D molecular structures. However, standard transformer architectures require a sequential ordering of tokens, which is not uniquely defined for the atoms in a molecule. Prior work has addressed this by using canonical atom orderings, but these do not ensure permutation invariance of atoms, which is essential for tasks like prefix completion. We introduce NEAT, a Neighborhood-guided, Efficient, Autoregressive, Set Transformer that treats molecular graphs as sets of atoms and learns an order-agnostic distribution over admissible tokens at the graph boundary. NEAT achieves state-of-the-art performance in autoregressive 3D molecular generation whilst ensuring atom-level permutation invariance by design. Autoregressive model ; Permutation (music) ; Transformer ; Molecular graph ; Prefix ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xu Dong | Zhongyu Yang | Sisi Yuan | Jiantao Yao | Jiangqiang Li | J. Ji
Source: ArXiv.org (preprint, 2025-07-09)
Field: Materials Science — Materials Chemistry

MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation Three-dimensional molecular generators based on diffusion models can now reach near-crystallographic accuracy, yet they remain fragmented across tasks. SMILES-only inputs, two-stage pretrain-finetune pipelines, and one-task-one-model practices hinder stereochemical fidelity, task alignment, and zero-shot transfer. We introduce MODA, a diffusion framework that unifies fragment growing, linker design, scaffold hopping, and side-chain decoration with a Bayesian mask scheduler. During training, a contiguous spatial fragment is masked and then denoised in one pass, enabling the model to learn shared geometric and chemical priors across tasks. Multi-task training yields a universal backbone that surpasses six diffusion baselines and three training paradigms on substructure, chemical property, interaction, and geometry. Model-C reduces ligand-protein clashes and substructure divergences while maintaining Lipinski compliance, whereas Model-B preserves similarity but trails in novelty and binding affinity. Zero-shot de novo design and lead-optimisation tests confirm stable negative Vina scores and high improvement rates without force-field refinement. These results demonstrate that a single-stage multi-task diffusion routine can replace two-stage workflows for structure-based molecular design. Workflow ; Fragment (logic) ; Novelty ; Substructure ; Diffusion ; Task (project management) ; Similarity (geometry) ; Prior probability Machine Learning in Materials Science ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Emily Pachêco Squizatto | Iare Soares Ribeiro | Marcus R. Pereira | Fábio Júnior Moreira Novaes | Márcio José da Silva | Luciano G. Moura | Geferson Gustavo Wagner Mota da Silva | Renê Chagas da Silva | Jemmyson Romário de Jesus
Source: ACS Omega (article, 2025-08-21)
Field: Chemistry — Inorganic Chemistry

A Niobium Coordination Polymer as an Efficient Sorbent for Caffeine Detection in Surface Water Herein, it is reported the synthesis of a niobium-based metal-organic framework (MOF), [Nb-(Bez-(COO)<sub>2</sub>)] <sub><i>n</i></sub> , for the extraction of caffeine from surface waters. The material was synthesized and characterized by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) analysis, which confirmed the coordination between the ligand (1,4-benzenodicarboxylic, (Bez-(COO)<sub>2</sub>)) and niobium (Nb) with a morphology composed of hexagonal rods, high crystallinity, and a surface area of 94.7 m<sup>2</sup> g<sup>-1</sup>. The extraction process was optimized using a response surface methodology, evaluating three factors: (i) mass of the MOF (100-500 mg), (ii) solution pH (5.0-9.0), and (iii) temperature (25-45 °C). The optimal conditions for caffeine extraction were determined as 10 mg of material, pH 9.0, and temperature of 25 °C. Adsorption studies showed that the Freundlich isotherm model provided the best fit (<i>R</i> <sup>2</sup> = 0.9498), suggesting adsorption on a heterogeneous surface. Kinetic studies showed that the intraparticle diffusion model better described the adsorption process (<i>R</i> <sup>2</sup> = 0.9554), highlighting physisorption by intraparticle diffusion as the predominant mechanism. Thermodynamic parameters revealed spontaneous and exothermic adsorption, with Δ<i>G</i> values between -5.052 and -4.668 kJ mol<sup>-1</sup>. The developed analytical method showed a linear range from 1.0 to 20 μg mL<sup>-1</sup>, with good linearity (<i>R</i> <sup>2</sup> = 0.9978), a limit of detection and quantification of 0.54 μg mL<sup>-1</sup>, and 1.78 μg mL<sup>-1</sup>, respectively. Accuracy was confirmed by recovery of 95.6 ± 1.1% at 4.5 μg mL<sup>-1</sup>. Moreover, the [Nb-(Bez-(COO)<sub>2</sub>)] <sub><i>n</i></sub> material demonstrated high reusability, maintaining its extraction efficiency after five consecutive adsorption-desorption cycles. These results confirm the robustness, efficiency, and sustainability of [Nb-(Bez-(COO)<sub>2</sub>)] <sub><i>n</i></sub> for environmental monitoring and remediation applications. Sorbent ; Niobium ; Caffeine ; Polymer ; Coordination polymer ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Metallurgy ; Medicine ; Adsorption ; Engineering ; Internal medicine Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Gas Sensing Nanomaterials and Sensors
Authors: Andrea Gayon-Lombardo | Ehecatl Antonio del Rio‐Chanona | Catalina A. Pino-Muñoz | Nigel P. Brandon
Source: ArXiv.org (preprint, 2025-06-07)
Field: Materials Science — Materials Chemistry

Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties. Kernel (algebra) ; Space mapping ; Gaussian process ; Generator (circuit theory) ; Battery (electricity) ; Bayesian optimization ; Gaussian function ; Electrode ; Porosity Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Nagham Hameed Abood | Sundus Hadi Merza
Source: Bulletin of the Chemical Society of Ethiopia (article, 2025-05-27)
Field: Chemistry — Organic Chemistry

Preparation, kinetics, and thermodynamic investigation of Janus green dye removal from aqueous solutions using MnO2 nanoparticles as an adsorbent In this study, manganese dioxide (MnO₂) nanoparticles (NPs) were synthesized via the hydrothermal method and utilized for the adsorption of Janus green dye (JG) from aqueous solutions. The effects of MnO₂ NPs on kinetics and diffusion were also analyzed. The synthesized NPs were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDX), and Fourier-transform infrared spectroscopy (FT-IR), with XRD confirming the nanoparticle size of 6.23 nm. The adsorption kinetics were investigated using three models: pseudo-first-order (PFO), pseudo-second-order (PSO), and the intraparticle diffusion model. The PSO model provided the best fit (R² = 0.999), indicating that the adsorption process is chemical in nature. Kinetic analysis revealed that the MnO₂ surface exhibited faster adsorption kinetics during the film diffusion step (rate constant 0.2776) compared to intraparticle diffusion (0.0042) at 298.15 K. Thermodynamic parameters, including enthalpy change (∆H*), entropy change (∆S*), and Gibbs free energy change (∆G*) of activation, were calculated from the Eyring equation to be 37.48 kJ/mol and -144.69 J/mol, respectively. The positive ∆G* value (80.62 kJ/mol) suggests that the adsorption process is energy-intensive, requiring higher energy to form chemical bonds between JG and MnO₂ nanoparticles. KEY WORDS: Hydrothermal method, Janus green dye, Manganese dioxide nanoparticles, Kinetic behavior, Intraparticle diffusion model Bull. Chem. Soc. Ethiop. 2025, 39(8), 1509-1523. DOI: https://dx.doi.org/10.4314/bcse.v39i8.5 Chemistry ; Aqueous solution ; Kinetics ; Adsorption ; Janus ; Nanoparticle ; Chemical engineering ; Nanotechnology ; Physical chemistry Nanomaterials for catalytic reactions ; Pickering emulsions and particle stabilization ; Minerals Flotation and Separation Techniques
Authors: Thomas Christen
Source:  (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

The Einstein Relation in Nonlinear Electric Conduction Models of HVDC Insulation Material The Einstein relation (ER) connecting mobility and diffusion constant of charge carriers is often used to determine the diffusion constant from the mobility in drift-diffusion models for electric transport in HVDC insulation materials. While in linear response theory the ER is universally valid according to the fluctuationdissipation theorem, this is not true in the nonlinear region. However, under certain conditions at local thermodynamic equilibrium, the ER can be generalized to nonlinear electric transport with replacing the mobility by the differential mobility. A consistent diffusion model should then consider a locally anisotropic diffusion tensor with coefficients associated with the differential mobility in electric field direction and with the low-field mobility in perpendicular directions. Because of the smallness of diffusion effects in HV insulation at typical electric field values where nonlinearity plays a role, the correction of the diffusion model has often a negligible impact. Significant effects may appear, e.g., for very short length scales and strong nonlinearities. Einstein relation ; Electric field ; Nonlinear system ; Diffusion ; Thermal conduction ; Constant (computer programming) ; Fick's laws of diffusion ; Drift current ; Field (mathematics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Xie, Fengyu
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Original data from "Chemical-space completeness: a new strategy for crystalline materials exploration" This dataset contains all generated data, machine-learned forcefield and crystal structure generative models from the paper "Chemical-space completeness: a new strategy for crystalline materials exploration". all_generated_structure_data.csv: A data table containing all generated structures, with their original structures, DPA-3 relaxed structures, DPA-3 evaluated energies and energy above hull included. All crystal structure columns contain json serialization of a pymatgen.Structure object, which can be deserialized through json.loads, then Structure.from_dict(). full.zip: contains all DFT computation data from selected structures per iteration. In the paper, we only selected from iteration 0 (folders with the tag "mp") to iteration 3 for fine-tuning the DPA-3 model. All DFT data is in the dpdata format (check https://docs.deepmodeling.com/projects/dpdata/en/master/index.html for further description) last.ckpt: The fine-tuned, chemical-system and energy-above-hull-conditioned MatterGen model at iteration 7. config.yaml: Necessary configuration file for the MatterGen package to recognize and infer from last.ckpt. See MatterGen github repository (https://github.com/microsoft/mattergen) for further details. frozen.pth: The fine-tuned DPA-3 model at iteration 3, N_train = 4050, used for iterative energy-above-hull evaluations and phase-diagram computation. We did NOT use this model for the conductivity screening section, but the pre-trained MatterSim-5M model instead (see https://github.com/microsoft/mattersim) JSON ; Computation ; Table (database) ; Iterative method ; Serialization ; Data structure ; Data format ; Crystal (programming language) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography