arXiv subjects

Xingfei Wei

Publications and source records attributed to Xingfei Wei.

5 recordsLinked to original sources

arXiv2026

Polymer-Linked Nanoparticle Networks Running on Heat Can Act as Computing Devices

Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.

physics.chem-ph↗

arXiv2026

Binding Affinity between Polymer Dots (Pdots) and Ovalbumin Protein at Varying pH

Determining the binding mechanisms between polymer dots and proteins is important for developing novel nanotechnologies for biomedicine and bioimaging. In this work, we use all-atom molecular dynamics simulations to determine the binding affinity of Pdots with ovalbumin protein at pH = 7 and 1. The selected Pdots are mixtures of Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-(2,1',3)-thiadiazole)] (PFBT) and poly(styrene/maleic anhydride) (PSMA) with varying composition. At pH = 7, the Pdots have a net negative charge due to the COO- functional groups on the PFBT, and the protein also has a net negative charge. At pH = 1, the Pdots are charge neutral with PFBT containing only COOH functional groups, and the protein also has a net positive charge. We sample the initial position of the protein by varying its initial position through all 6 orientations of a cube. For each orientation, we pull the protein towards the PFBT region of the Pdot. We compare the Coulombic and Lennard-Jones interaction energies for the 6 different interacting faces and two selected pH values. We find that the LJ interaction energies are similar among all 12 of these cases. The measured Coulombic interaction energies suggest that pH = 1 has better binding affinity than pH = 7. The potentials of mean force (PMF) along the pulling coordinates differ with pH. The PMFs from 2 of the 6 initial configurations at pH = 1 are negative whereas none of them are negative at pH = 7, confirming the preferred binding affinity when pH = 1. One of the faces at pH = 1 has the lowest PMF of about -30 kcal/mol, which is much lower than about 6 kcal/mol seen for the lowest case at pH = 7. Comparison of protein residue charge distributions at pH = 7 and 1 further shows that the electrostatic interaction is critical to the binding affinity, and negatively charged residues reduce at pH = 7 does not bind to negatively charged Pdot.

physics.chem-ph↗

arXiv2025

DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models -- viz. AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 -- to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pre-train CNN models using a large image dataset of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pre-trained CNN models, using a small experimental TEM dataset with 146 TEM images. All CNN models were found to have similar computational time requirements, while their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pre-trained CNN models ResNet50 and VGG16 have the highest and second highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement on the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the ligation number of nanostructures in large TEM images.

physics.chem-ph↗

arXiv2020

Thermal Transport in Polymers: A Review

In this article, we review thermal transport in polymers with different morphologies from aligned fibers to bulk amorphous states. We survey early and recent efforts in engineering polymers with high thermal conductivity by fabricating polymers with large-scale molecular alignments. The experimentally realized extremely high thermal conductivity of polymer nanofibers are highlighted, and understanding of thermal transport physics from molecular simulations are discussed. We then transition to the discussion of bulk amorphous polymers with an emphasize on the physics of thermal transport and its relation with the conformation of molecular chains in polymers. We also discuss the current understanding of how the chemistry of polymers would influence thermal transport in amorphous polymers and some limited, but important chemistry-structural-property relationships. Lastly, challenges, perspectives and outlook of this field are presented. We hope this review will inspire more fundamental and applied research in the polymer thermal transport field to advance scientific understanding and engineering applications.

cond-mat.mtrl-sci↗

arXiv2019

The Role of Ionization in Thermal Transport of Solid Polyelectrolytes

Amorphous polymers are known as thermal insulators, increasing their thermal conductivities have not been guided by fully understood physics. In this work, we use molecular dynamics simulations to study the thermal transport mechanism of solid polyelectrolytes, poly(acrylic acid) (PAA) and its ionized forms. The thermal conductivity of PAA increases monotonically with the ionization strength. Although stronger ionization induces larger Coulombic interactions, the Coulombic interaction does not directly contribute to the thermal conductivity enhancement. Instead, it enhances thermal transport through the Lennard-Jones (LJ) interaction. The strong Coulombic force between the counterion and the ionized carboxylic group shifts the LJ force to the stronger LJ repulsive regime, which is mainly responsible for the improved thermal conductivity. Applying a high pressure can further reduce the inter-atomic distance and trigger the thermal transport through the LJ interaction. A thermal conductivity of 1.09 W/m.K can be achieved at 11.2 GPa in an ionized PAA.

cond-mat.soft↗