Atomistic modeling of interfacial and thermal transport properties of engineered graphene structures

The immense amount of research on graphene in the last decade has led to advancement in techniques to control the thermo-physical properties of graphene by making use of engineered graphene structures. Carefully designed defective graphene, van der Waals heterostructures, functionalized graphene and engineered graphene surfaces have potential applications in photoelectronics and thermoelectrics devices, as electrode materials, phase-change materials for thermal energy storage, sensors, coatings etc. In this study, we focus on the interfacial and thermal transport phenomena within these engineered graphene nano-structures. The main objective of this dissertation is to understand the mechanism of heat transfer across the nano-structures and analyze the molecular interactions at the interface which governs the surface properties like interfacial thermal resistance, viscosity and wettability. We use atomistic modelling to evaluate these materials and compare the results with experiments. The degradation of thermal conductivity with concentration of isotope and vacancy defects in graphene was analyzed using molecular dynamics (MD). The results qualitatively match with collaborative experimental and theoretical efforts based on Boltzmann transport equation (BTE). We next investigate contrasting behavior of thermal conductivity and sheet conductance in graphene/MoS2 van der Waals heterostructure. The phonon relaxation times and thermal conductivity in graphene are suppressed due to the weak van der Waals interaction with the adjacent layers. We also experimentally and computationally characterize thermo-physical properties of a mixture of n-eicosane phase change material and graphite particles (GP). Our results show a large enhancement in k (450%) and µ (1200%) for a 3.5% vol. concentration of GP fillers. The surprising reduction in interfacial thermal resistance (Reff) with increasing filler concentration, together with the high thermal conductivity of GP contributes to the large enhancement in k. While the viscosity of the n-eicosane around GP increases, we explore graphene’s potential as a solid lubricant when used with nanodiamond. The frictional force at the atomic scale in nanodiamond wrapped with graphene and graphitized nanodiamond is studied using molecular dynamics. Our results show that the rotational degree of freedom of nanodiamond leads to a lower friction in graphene wrapped nanodiamond. Finally, we show that hydrophobicity of graphene surfaces can be tuned by changing the orientation of graphene flakes. Our results show that the hydrophobicity due to the graphene flake orientation determines the Kapitza resistance and evaporation rates of water over the surface. The faster heated evaporation over hydrophilic surfaces is attributed to the efficient heat transfer from the substrate to water. Our results matches with collaborative experimental observations.</p

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

Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron

Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computational studies have shed light on some of the stable boron allotropes. However, the demand for multifunctionality necessitates the need to go beyond the stable phases into the realm of metastability and explore the potentially vast but elusive metastable phases of boron. Traditional search for stable phases of materials has focused on identifying materials with the lowest enthalpy. Here, we introduce a workflow that uses reinforcement learning coupled with decision trees, such as Monte Carlo tree search, to search for stable and metastable boron phases, with enthalpy as the objective. We discover new boron metastable phases and construct a phase diagram that locates their phase space (T, P) at different levels of metastability (ΔG) from the ground state and provides useful information on the domains of relative stability of the various stable and metastable boron phases

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. Computational docking simulations have traditionally been used for in silico ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional in silico ligand design. Here, we present a de novo molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100\u27s) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski\u27s rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality

A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences

A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences