Slippery and Wear-Resistant Surfaces Enabled by Interface Engineered Graphene.

Friction and wear remain the primary cause of mechanical energy dissipation and system failure. Recent studies reveal graphene as a powerful solid lubricant to combat friction and wear. Most of these studies have focused on nanoscale tribology and have been limited to a few specific surfaces. Here, we uncover many unknown aspects of graphene's contact-sliding at micro- and macroscopic tribo-scales over a broader range of surfaces. We discover that graphene's performance reduces for surfaces with increasing roughness. To overcome this, we introduce a new type of graphene/silicon nitride (SiNx, 3 nm) bilayer overcoats that exhibit superior performance compared to native graphene sheets (mono and bilayer), that is, display the lowest microscale friction and wear on a range of tribologically poor flat surfaces. More importantly, two-layer graphene/SiNx bilayer lubricant (<4 nm in total thickness) shows the highest macroscale wear durability on tape-head (topologically variant surface) that exceeds most previous thicker (∼7-100 nm) overcoats. Detailed nanoscale characterization and atomistic simulations explain the origin of the reduced friction and wear arising from these nanoscale coatings. Overall, this study demonstrates that engineered graphene-based coatings can outperform conventional coatings in a number of technologies.

Reduced Thermal Transport in the Graphene/MoS2/Graphene Heterostructure: A Comparison with Freestanding Monolayers.

The thermal conductivity of the graphene-encapsulated MoS2 (graphene/MoS2/graphene) van der Waals heterostructure is determined along the armchair and zigzag directions with different twist angles between the layers using molecular dynamics (MD) simulations. The differences in the predictions relative to those of the monolayers are analyzed using the phonon power spectrum and phonon lifetimes obtained by spectral energy density analysis. The thermal conductivity of the heterostructure is predominantly isotropic. The out-of-plane phonons of graphene are suppressed because of the interaction between the adjacent layers that results in the reduced phonon lifetime and thermal conductivity relative to monolayer graphene. The occurrence of an additional nonzero phonon branch at the Γ point in the phonon dispersion curves of the heterostructure corresponds to the breathing modes resulting from stacking of the layers in the heterostructure. The thermal sheet conductance of the heterostructure being an order of magnitude larger than that of monolayer MoS2, this van der Waals material is potentially suitable for efficient thermal packaging of photoelectronic devices. The interfacial thermal conductance of the graphene/MoS2 bilayer as a function of the heat flow direction shows weak thermal rectification.

Artificial Graphene Nanoribbons: A Test Bed for Topology and Low-Dimensional Dirac Physics.

We synthesize artificial graphene nanoribbons by positioning carbon monoxide molecules on a copper surface to confine its surface state electrons into artificial atoms positioned to emulate the low-energy electronic structure of graphene derivatives. We demonstrate that the dimensionality of artificial graphene can be reduced to one dimension with proper "edge" passivation, with the emergence of an effectively gapped one-dimensional nanoribbon structure. These one-dimensional structures show evidence of topological effects analogous to graphene nanoribbons. Guided by first-principles calculations, we spatially explore robust, zero-dimensional topological states by altering the topological invariants of quasi-one-dimensional artificial graphene nanostructures. The robustness and flexibility of our platform allow us to toggle the topological invariants between trivial and nontrivial on the same nanostructure. Ultimately, we spatially manipulate the states to understand fundamental coupling between adjacent topological states that are finely engineered and simulate complex Hamiltonians.

Machine learning the metastable phase diagram of covalently bonded carbon.

Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase.

Machine learning overcomes human bias in the discovery of self-assembling peptides.

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19.

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the in silico ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a de novo molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.

Machine learning a bond order potential model to study thermal transport in WSe2 nanostructures.

Nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon-phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications.

Superhydrophobic inkjet printed flexible graphene circuits via direct-pulsed laser writing.

Solution-phase printing of exfoliated graphene flakes is emerging as a low-cost means to create flexible electronics for numerous applications. The electrical conductivity and electrochemical reactivity of printed graphene has been shown to improve with post-print processing methods such as thermal, photonic, and laser annealing. However, to date no reports have shown the manipulation of surface wettability via post-print processing of printed graphene. Herein, we demonstrate how the energy density of a direct-pulsed laser writing (DPLW) technique can be varied to tune the hydrophobicity and electrical conductivity of the inkjet-printed graphene (IPG). Experimental results demonstrate that the DPLW process can convert the IPG surface from one that is initially hydrophilic (contact angle ∼47.7°) and electrically resistive (sheet resistance ∼21 MΩ â–¡-1) to one that is superhydrophobic (CA ∼157.2°) and electrically conductive (sheet resistance ∼1.1 kΩ â–¡-1). Molecular dynamic (MD) simulations reveal that both the nanoscale graphene flake orientation and surface chemistry of the IPG after DPLW processing induce these changes in surface wettability. Moreover, DPLW can be performed with IPG printed on thermally and chemically sensitive substrates such as flexible paper and polymers. Hence, the developed, flexible IPG electrodes treated with DPLW could be useful for a wide range of applications such as self-cleaning, wearable, or washable electronics.

Thermal conductivity of graphene with defects induced by electron beam irradiation.

We investigate the thermal conductivity of suspended graphene as a function of the density of defects, ND, introduced in a controllable way. High-quality graphene layers are synthesized using chemical vapor deposition, transferred onto a transmission electron microscopy grid, and suspended over ∼7.5 µm size square holes. Defects are induced by irradiation of graphene with the low-energy electron beam (20 keV) and quantified by the Raman D-to-G peak intensity ratio. As the defect density changes from 2.0 × 10(10) cm(-2) to 1.8 × 10(11) cm(-2) the thermal conductivity decreases from ∼(1.8 ± 0.2) × 10(3) W mK(-1) to ∼(4.0 ± 0.2) × 10(2) W mK(-1) near room temperature. At higher defect densities, the thermal conductivity reveals an intriguing saturation-type behavior at a relatively high value of ∼400 W mK(-1). The thermal conductivity dependence on the defect density is analyzed using the Boltzmann transport equation and molecular dynamics simulations. The results are important for understanding phonon - point defect scattering in two-dimensional systems and for practical applications of graphene in thermal management.