Friction and wear remain as the primary modes of mechanical energy dissipation in moving mechanical assemblies; thus, it is desirable to minimize friction in a number of applications. We demonstrate ...that superlubricity can be realized at engineering scale when graphene is used in combination with nanodiamond particles and diamondlike carbon (DLC). Macroscopic superlubricity originates because graphene patches at a sliding interface wrap around nanodiamonds to form nanoscrolls with reduced contact area that slide against the DLC surface, achieving an incommensurate contact and substantially reduced coefficient of friction (∼0.004). Atomistic simulations elucidate the overall mechanism and mesoscopic link bridging the nanoscale mechanics and macroscopic experimental observations.
Four different machine learning (ML) regression models: artificial neural network,
k
-nearest neighbors, Gaussian process regression and random forest were built to backmap coarse-grained models to ...all-atom models. The ML models showed better predictions than the existing backmapping approaches for selected structures, suggesting the applications of the ML models for backmapping.
Four different machine learning (ML) regression models: artificial neural network,
k
-nearest neighbors, Gaussian process regression and random forest were built to backmap coarse-grained models to all-atom models.
Over the last few years, coarse-grained molecular dynamics has emerged as a way to model large and complex systems in an efficient and inexpensive manner due to its lowered resolution, faster ...dynamics, and larger time steps. However, developing coarse-grained models and subsequently, the accurate interaction potentials (force-field parameters) is a challenging task. Traditional parameterisation techniques, although tedious, have been used extensively to develop CG models for a variety of solvent, soft-matter and biological systems. With the advent of sophisticated optimisation methods, machine learning, and hybrid approaches for force-field parameterisation, models with a higher degree of transferability and accuracy can be developed in a shorter period of time. We review here, some of these traditional and advanced parameterisation techniques while also shedding light on several transferable CG models developed in our group over the years using such an advanced method developed by us. These models, including solvents, polymers and biomolecules have helped us study important solute-solvent interactions and complex polymer architectures, thus paving a way to make experimentally verifiable observations.
During the last few years, graphene's unusual friction and wear properties have been demonstrated at nano to micro scales but its industrial tribological potential has not been fully realized. The ...macroscopic wear resistance of one atom thick graphene coating is reported by subjecting it to pin‐on‐disc type wear testing against most commonly used steel against steel tribo‐pair. It is shown that when tested in hydrogen, a single layer of graphene on steel can last for 6400 sliding cycles, while few‐layer graphene (3–4 layers) lasts for 47 000 cycles. Furthermore, these graphene layers are shown to completely cease wear despite the severe sliding conditions including high contact pressures (≈0.5 GPa) observed typically in macroscale wear tests. The computational simulations show that the extraordinary wear performance originates from hydrogen passivation of the dangling bonds in a ruptured graphene, leading to significant stability and longer lifetime of the graphene protection layer. Also, the electronic properties of these graphene sheets are theoretically evaluated and the improved wear resistance is demonstrated to preserve the electronic properties of graphene and to have significant potential for flexible electronics. The findings demonstrate that tuning the atomistic scale chemical interactions holds the promise of realizing extraordinary tribological properties of monolayer graphene coatings.
The mechanism of extraordinary wear resistance of just one atom thick graphene layer on steel is revealed. A single layer of graphene is able to reduce steel wear by 3–4 orders of magnitude. The wear‐life of graphene significantly increases when tested in hydrogen environment. Hydrogen plays a crucial role in preventing graphene from wear‐induced damage by passivating carbon dangling bonds.
In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials ...have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.
This review summarises recent advances in the use of machine learning for predicting friction and wear in tribological systems, material discovery, lubricant design and composite formulation. Potential future applications and areas for further research are also discussed.
Machine-Learned Coarse-Grained Models Bejagam, Karteek K; Singh, Samrendra; An, Yaxin ...
The journal of physical chemistry letters,
08/2018, Letnik:
9, Številka:
16
Journal Article
Recenzirano
Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD ...simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for D2O and DMF as a proof of concept. The PSO algorithm was used to generate the set of input FF parameters for the MD simulations of the CG models of these solvents, which were optimized to reproduce their experimental properties. Herein, for the first time, a reverse approach was employed for on-the-fly training of the ANN model, where results (solvent properties) obtained from the MD simulations and their corresponding FF parameters were used as inputs and outputs, respectively. The ANN model was then required to predict a set of new FF parameters, which were tested for their ability to predict the desired experimental properties. This new framework can be extended to integrate any optimization algorithm with ANN and MD simulations to accelerate the FF development.
The degradation of intrinsic properties of graphene during the transfer process constitutes a major challenge in graphene device fabrication, stimulating the need for direct growth of graphene on ...dielectric substrates. Previous attempts of metal-induced transformation of diamond and silicon carbide into graphene suffers from metal contamination and inability to scale graphene growth over large area. Here, we introduce a direct approach to transform polycrystalline diamond into high-quality graphene layers on wafer scale (4 inch in diameter) using a rapid thermal annealing process facilitated by a nickel, Ni thin film catalyst on top. We show that the process can be tuned to grow single or multilayer graphene with good electronic properties. Molecular dynamics simulations elucidate the mechanism of graphene growth on polycrystalline diamond. In addition, we demonstrate the lateral growth of free-standing graphene over micron-sized pre-fabricated holes, opening exciting opportunities for future graphene/diamond-based electronics.
We present a computational framework that integrates coarse-grained (CG) molecular dynamics (MD) simulations and a data-driven machine-learning (ML) method to gain insights into the conformations of ...polymers in solutions. We employ this framework to study conformational transition of a model thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM). Here, we have developed the first of its kind, a temperature-independent CG model of PNIPAM that can accurately predict its experimental lower critical solution temperature (LCST) while retaining the tacticity in the presence of an explicit water model. The CG model was extensively validated by performing CG MD simulations with different initial conformations, varying the radius of gyration of chain, the chain length, and the angle between the adjacent monomers of the initial configuration of PNIPAM (total simulation time = 90 μs). Moreover, for the first time, we utilize the nonmetric multidimensional scaling (NMDS) method, a data-driven ML approach, to gain further insights into the mechanisms and pathways of this coil-to-globule transition by analyzing CG MD simulation trajectories. NMDS analysis provides entirely new insights and shows multiple metastable states of PNIPAM during its coil-to-globule transition above the LCST.
Understanding the effect of solvent on the polymer conformations is a fundamental problem in materials science and engineering. Here, we have developed, the first of its kind, a coarse-grained (CG) ...model of poly(acrylic acid) (PAA) that can reproduce its experimental glass transition temperature (T g) and conformation of a single chain in the presence of explicit solvents along with capturing the structure of solvents at the PAA–solvent interface. The PAA model was based on a CG model of propionic acid, an analogue of the PAA monomer. The accuracy of both the propionic acid and PAA models was validated by employing uncertainty quantifications. The cross-interaction parameters between CG PAA and one-site water model and between CG PAA and DMF models were optimized to reproduce the radius of gyration (R g) of an all-atom 30-monomer (30-mer) PAA chain in pure solvents. These interaction parameters were further used to explore the PAA conformation in the presence of binary mixtures of water and DMF with different compositions. A PAA chain was in a globule-like and a coil-like state in binary solvents with low and high mass fractions of DMF, respectively. Moreover, the local structure of solvent suggests that even at a low mass fraction of DMF in a binary solvent, there is an enhanced ordering of DMF molecules at the polymer–solvent interface. Furthermore, an increase in the coordination number of DMF molecules within the first solvation shell of PAA suggests that DMF molecules form a shielding layer and protect PAA from water molecules. These results are in excellent agreement with the results of all-atom MD simulations.
Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their ...function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12–6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes.
PDF
