HiDeNN-FEM: A seamless machine learning approach to nonlinear finite element analysis.

The hierarchical deep-learning neural network (HiDeNN) (Zhang et al, Computational Mechanics, 67:207-230) provides a systematic approach to constructing numerical approximations that can be incorporated into a wide variety of Partial differential equations (PDE) and/or Ordinary differential equations (ODE) solvers. This paper presents a framework of the nonlinear finite element based on HiDeNN approximation (nonlinear HiDeNN-FEM). This is enabled by three basic building blocks employing structured deep neural networks: 1) A partial derivative operator block that performs the differentiation of the shape functions with respect to the element coordinates, 2) An r-adaptivity block that improves the local and global convergence properties and 3) A materials derivative block that evaluates the material derivatives of the shape function.

Data-driven discovery of dimensionless numbers and governing laws from scarce measurements.

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data.

Universal scaling laws of keyhole stability and porosity in 3D printing of metals.

Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines.

Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization.

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge.

Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification.

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method.

Data-driven characterization of thermal models for powder-bed-fusion additive manufacturing.

Computational modeling for additive manufacturing has proven to be a powerful tool to understand physical mechanisms, predict fabrication quality, and guide design and optimization. Varieties of models have been developed with different assumptions and purposes, and these models are sometimes difficult to choose from, especially for end-users, due to the lack of quantitative comparison and standardization. Thus, this study is focused on quantifying model uncertainty due to the modeling assumptions, and evaluating differences based on whether or not selected physical factors are incorporated.

Cell and nanoparticle transport in tumour microvasculature: the role of size, shape and surface functionality of nanoparticles.

Through nanomedicine, game-changing methods are emerging to deliver drug molecules directly to diseased areas. One of the most promising of these is the targeted delivery of drugs and imaging agents via drug carrier-based platforms. Such drug delivery systems can now be synthesized from a wide range of different materials, made in a number of different shapes, and coated with an array of different organic molecules, including ligands.

Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization.

In this paper, we present new reliable model order reduction strategies for computational micromechanics. The difficulties rely mainly upon the high dimensionality of the parameter space represented by any load path applied onto the representative volume element. We take special care of the challenge of selecting an exhaustive snapshot set.

Shape effect in cellular uptake of PEGylated nanoparticles: comparison between sphere, rod, cube and disk.

The size, shape, surface property and material composition of polymer-coated nanoparticles (NPs) are four important parameters in designing efficient NP-based carriers for targeted drug delivery. However, due to the complex interplay between size, shape and surface property, most studies lead to ambiguous descriptions of the relevance of shape. To clarify its influence on the cellular uptake of PEGylated NPs, large scale molecular simulations have been performed to study differently shaped convex NPs, such as sphere, rod, cube and disk.

Endocytosis of PEGylated nanoparticles accompanied by structural and free energy changes of the grafted polyethylene glycol.

Nanoparticles (NPs) are in use to efficiently deliver drug molecules into diseased cells. The surfaces of NPs are usually grafted with polyethylene glycol (PEG) polymers, during so-called PEGylation, to improve water solubility, avoid aggregation, and prevent opsonization during blood circulation. The interplay between grafting density σp and grafted PEG polymerization degree N makes cellular uptake of PEGylated NPs distinct from that of bare NPs.

USNCTAM perspectives on mechanics in medicine.

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges.

Dynamic structure of unentangled polymer chains in the vicinity of non-attractive nanoparticles.

Using coarse-grained molecular dynamics simulation, we study the motion of unentangled polymer chains dynamically confined by non-attractive nanoparticles (NPs). Both normal mode and dynamic structure factor S(q, t) analysis are adopted to analyze chain's dynamics. Relaxation behaviors of chains are found to be significantly slowed down by NPs.

Ternary and senary representations using DNA double-crossover tiles.

The information capacity of DNA double-crossover (DX) tiles was successfully increased beyond a binary representation to higher base representations. By controlling the length and the position of DNA hairpins on the DX tile, ternary and senary (base-3 and base-6) digit representations were realized and verified by atomic force microscopy. Also, normal mode analysis was carried out to study the mechanical characteristics of each structure.

Efficient prediction of protein conformational pathways based on the hybrid elastic network model.

Various computational models have gained immense attention by analyzing the dynamic characteristics of proteins. Several models have achieved recognition by fulfilling either theoretical or experimental predictions. Nonetheless, each method possesses limitations, mostly in computational outlay and physical reality.

Synthesis of nanodiamond-daunorubicin conjugates to overcome multidrug chemoresistance in leukemia.

Unlabelled: Nanodiamonds (NDs) are promising candidates in nanomedicine, demonstrating significant potential as gene/drug delivery platforms for cancer therapy. We have synthesized ND vectors capable of chemotherapeutic loading and delivery with applications towards chemoresistant leukemia. The loading of Daunorubicin (DNR) onto NDs was optimized by adjusting reaction parameters such as acidity and concentration.

Quantifying uncertainties in the microvascular transport of nanoparticles.

The character of nanoparticle dispersion in the microvasculature is a driving factor in nanoparticle-based therapeutics and bio-sensing. It is difficult, with current experimental and engineering capability, to understand dispersion of nanoparticles because their vascular system is more complex than mouse models and because nanoparticle dispersion is so sensitive to in vivo environments. Furthermore, uncertainty cannot be ignored due to the high variation of location-specific vessel characteristics as well as variation across patients.

On the near-wall accumulation of injectable particles in the microcirculation: smaller is not better.

Although most nanofabrication techniques can control nano/micro particle (NMP) size over a wide range, the majority of NMPs for biomedical applications exhibits a diameter of ~100 nm. Here, the vascular distribution of spherical particles, from 10 to 1,000 nm in diameter, is studied using intravital microscopy and computational modeling. Small NMPs (≤100 nm) are observed to move with Red Blood Cells (RBCs), presenting an uniform radial distribution and limited near-wall accumulation.

A mass weighted chemical elastic network model elucidates closed form domain motions in proteins.

An elastic network model (ENM), usually Cα coarse-grained one, has been widely used to study protein dynamics as an alternative to classical molecular dynamics simulation. This simple approach dramatically saves the computational cost, but sometimes fails to describe a feasible conformational change due to unrealistically excessive spring connections. To overcome this limitation, we propose a mass-weighted chemical elastic network model (MWCENM) in which the total mass of each residue is assumed to be concentrated on the representative alpha carbon atom and various stiffness values are precisely assigned according to the types of chemical interactions.

Multiscale Simulation as a Framework for the Enhanced Design of Nanodiamond-Polyethylenimine-based Gene Delivery.

Nanodiamonds (NDs) are emerging carbon platforms with promise as gene/drug delivery vectors for cancer therapy. Specifically, NDs functionalized with the polymer polyethylenimine (PEI) can transfect small interfering RNAs (siRNA) in vitro with high efficiency and low cytotoxicity. Here we present a modeling framework to accurately guide the design of ND-PEI gene platforms and elucidate binding mechanisms between ND, PEI, and siRNA.

Dielectrophoretic concentration of low-abundance nanoparticles using a nanostructured tip.

Electric field-induced concentration has the potential for application in highly sensitive detection of nanoparticles (NPs) for disease diagnosis and drug discovery. Conventional two-dimensional planar electrodes, however, have shown limited sensitivity in NP concentration. In this paper, the dielectrophoretic (DEP) concentration of low-abundance NPs is studied using a nanostructured tip where a high electric field of 3 × 10(7) V m(-1) is generated.

Nanoparticle effect on the dynamics of polymer chains and their entanglement network.

We explore the dynamics of entangled polymer chains embedded into nanocomposites. From primitive path analysis, highly entangled polymer chains are found to be significantly disentangled during increment of the volume fraction of spherical nonattractive nanoparticles (NPs) from 0 to 42%. A critical volume fraction, ϕ(c)=31%, is found to control the crossover from polymer chain entanglements to "NP entanglements.

Hydrodynamics of capillary imbibition under nanoconfinement.

Understanding fluid flow in nanoconfined geometries is crucial for a broad range of scientific problems relevant to the behavior of porous materials in biology, nanotechnology, and the built environment. Because of the dominant importance of surface effects at the nanoscale, long-standing assumptions that are valid for macroscopic systems must be revisited when modeling nanoconfined fluids, because boundary conditions and the confined behavior of liquids are challenging to discern from experiments. To address this issue, here we present a novel coarse-grained model that combines parameters calibrated for water with a dissipative particle dynamics thermostat for the purpose of investigating hydrodynamics under confinement at scales exceeding current capabilities with all-atomistic simulations.

Nanoscale sensor analysis using the immersed molecular electrokinetic finite element method.

The concentration and detection of molecular biomarkers remain as a challenge to develop point-of-care diagnostic devices. An electric field induced concentration has been studied for such purposes but with limited success due to limited efficacy. This paper presents a computational study for investigating the molecular concentration and retention efficacy of single nanowire (SNW) and dendritic nanotip (DNT) sensors.

Electric field guided assembly of one-dimensional nanostructures for high performance sensors.

Various nanowire or nanotube-based devices have been demonstrated to fulfill the anticipated future demands on sensors. To fabricate such devices, electric field-based methods have demonstrated a great potential to integrate one-dimensional nanostructures into various forms. This review paper discusses theoretical and experimental aspects of the working principles, the assembled structures, and the unique functions associated with electric field-based assembly.