Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware

Physics-Based Modeling of Material Behavior and Damage Initiation in Nanoengineered Composites

abstract: Materials with unprecedented properties are necessary to make dramatic changes in current and future aerospace platforms. Hybrid materials and composites are increasingly being used in aircraft and spacecraft frames; however, future platforms will require an optimal design of novel materials that enable operation in a variety of environments and produce known/predicted damage mechanisms. Nanocomposites and nanoengineered composites with CNTs have the potential to make significant improvements in strength, stiffness, fracture toughness, flame retardancy and resistance to corrosion. Therefore, these materials have generated tremendous scientific and technical interest over the past decade and various architectures are being explored for applications to light-weight airframe structures. However, the success of such materials with significantly improved performance metrics requires careful control of the parameters during synthesis and processing. Their implementation is also limited due to the lack of complete understanding of the effects the nanoparticles impart to the bulk properties of composites. It is common for computational methods to be applied to explain phenomena measured or observed experimentally. Frequently, a given phenomenon or material property is only considered to be fully understood when the associated physics has been identified through accompanying calculations or simulations. The computationally and experimentally integrated research presented in this dissertation provides improved understanding of the mechanical behavior and response including damage and failure in CNT nanocomposites, enhancing confidence in their applications. The computations at the atomistic level helps to understand the underlying mechanochemistry and allow a systematic investigation of the complex CNT architectures and the material performance across a wide range of parameters. Simulation of the bond breakage phenomena and development of the interface to continuum scale damage captures the effects of applied loading and damage precursor and provides insight into the safety of nanoengineered composites under service loads. The validated modeling methodology is expected to be a step in the direction of computationally-assisted design and certification of novel materials, thus liberating the pace of their implementation in future applications.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Challenges in Ceramic Science: A Report from the Workshop on Emerging Research Areas in Ceramic Science

In March 2012, a group of researchers met to discuss emerging topics in ceramic science and to identify grand challenges in the field. By the end of the workshop, the group reached a consensus on eight challenges for the future:—understanding rare events in ceramic microstructures, understanding the phase-like behavior of interfaces, predicting and controlling heterogeneous microstructures with unprecedented functionalities, controlling the properties of oxide electronics, understanding defects in the vicinity of interfaces, controlling ceramics far from equilibrium, accelerating the development of new ceramic materials, and harnessing order within disorder in glasses. This paper reports the outcomes of the workshop and provides descriptions of these challenges

First principles-based multiparadigm, multiscale strategy for simulating complex materials processes with applications to amorphous SiC films

Progress has recently been made in developing reactive force fields to describe chemical reactions in systems too large for quantum mechanical (QM) methods. In particular, ReaxFF, a force field with parameters that are obtained solely from fitting QM reaction data, has been used to predict structures and properties of many materials. Important applications require, however, determination of the final structures produced by such complex processes as chemical vapor deposition, atomic layer deposition, and formation of ceramic films by pyrolysis of polymers. This requires the force field to properly describe the formation of other products of the process, in addition to yielding the final structure of the material. We describe a strategy for accomplishing this and present an example of its use for forming amorphous SiC films that have a wide variety of applications. Extensive reactive molecular dynamics (MD) simulations have been carried out to simulate the pyrolysis of hydridopolycarbosilane. The reaction products all agree with the experimental data. After removing the reaction products, the system is cooled down to room temperature at which it produces amorphous SiC film, for which the computed radial distribution function, x-ray diffraction pattern, and the equation of state describing the three main SiC polytypes agree with the data and with the QM calculations. Extensive MD simulations have also been carried out to compute other structural properties, as well the effective diffusivities of light gases in the amorphous SiC film

Molecular dynamics simulations of nanoclusters in neuromorphic systems

Neuromorphic computing is a new computing paradigm that deals with computing tasks using inter-connected artificial neurons inspired by the natural neurons in the human brain. This computational architecture is more efficient in performing many complex tasks such a pattern recognition and has promise at overcoming some of the limitations of conventional computers. Among the emerging types of artificial neurons, a cluster-based neuromorphic device is a promising system with good costefficiency because of a simple fabrication process. This device functions using the formation and breakage of the connections (“synapses”) between clusters, driven by the bias voltage applied to the clusters. The mechanisms of the formation and breakage of these connections are thus of the utmost interest. In this thesis, the molecular dynamics simulation method is used to explore the mechanisms of the formation and breakage of the connections (“filaments”) between the clusters in a model of neuromorphic device. First, the Joule heating mechanism of filament breakage is explored using a model consisting of Au nanowire that connects two Au1415 clusters. Upon heating, the atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of the wire to graduallythin and then suddenly break. Most of the system remains crystalline during this process, but the centre becomes molten. The terminal clusters increase the melting point of the nanowires by fixing them and act as recrystallisation regions. A strong dependence of the breaking temperature is found not only on the width of the nanowires but also their length and atomic structure. Secondly, the bridge formation and thermal breaking processes between Au1415 clusters on a graphite substrate are also simulated. The bridging process , which can heal a broken filament, is driven by diffusion of gold along the graphite substrate. The characteristic times of bridge formation are explored at elevated simulation temperatures to estimate the longer characteristic times of formation at room-temperature conditions. The width of the bridge formed has a power-law dependence on the simulation time, and the mechanism is a combination of diffusion and viscous flow. Simulations of bridgebreaking are also conducted and reveal the existence of a voltage threshold that must be reached to activate the breakage. The role of the substrate in the bridge formation and breakage processes is revealed as a medium of diffusion. Lastly, to explore future potential cluster materials, the thermal behaviour of Pb-Al core-shell clusters is studied. The core and shell are found to melt separately. In fact, the core atoms of nanoclusters tend to escape their shells and partially cover them, leading to a preference for a segregated state. The melting point of the core can either be depressed or elevated, depending on the thickness of the shell due to different mechanisms

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and it is of great relevance for practical applications as well. The manufacturing of window glass, the performance degradation of fiber-optics and the scalability of next-generation phase- change memories all depend on the thermal properties of amorphous solids. While macroscopic properties such as the thermal conductivity are usually well-characterised experimentally, their microscopic origin is often largely unknown. This is because the thermal properties of amorphous solids are determined by their vibrational (and possibly electronic) properties, which in turn depend upon the atomic-level structure. Hence there is a pressing need for atomistic simulations, which can in principle unravel the connection between microscopic structure and functional properties such as thermal conductivity. However, the large (long) length (time) scales involved are usually well beyond the reach of ab initio calculations. On the other hand, many interesting amorphous materials are characterised by a very complex structure. This often prevents the construction of classical interatomic potentials which would enable simulations on much larger (longer) length (time) scales – if compared to those achievable by first-principles simulations. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields for molecular dynamics simulations while retaining much of the accuracy of first-principles calculations. Here, we discuss the contribution of these ML-based potentials to our understanding of the thermal properties of amorphous solids. We focus on neural-network potentials (NNPs) and Gaussian approximation potentials (GAPs), two of the most widespread theoretical frameworks available to date. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, a class of systems widely used in the context of non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

© 2018 Informa UK Limited, trading as Taylor & Francis Group. Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and for practical applications as well. While quantities such as the thermal conductivity are usually well characterised experimentally, their microscopic origin is often largely unknown - hence the pressing need for molecular simulations. However, the time and length scales involved with thermal transport phenomena are typically well beyond the reach of ab initio calculations. On the other hand, many amorphous materials are characterised by a complex structure, which prevents the construction of classical interatomic potentials. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields while retaining much of the accuracy of first-principles calculations. Here, we discuss neural network potentials (NNPs) and Gaussian approximation potentials (GAPs), two popular ML frameworks. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, systems widely used in non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids

Neuromorphic nanocluster networks: Critical role of the substrate in nano-link formation

Atomic cluster-based networks represent a promising architecture for the realization of neuromorphic computing systems, which may overcome some of the limitations of the current computing paradigm. The formation and breakage of synapses between the clusters are of utmost importance for the functioning of these computing systems. This paper reports the results of molecular dynamics simulations of synapse (bridge) formation at elevated temperatures and thermal breaking processes between 2.8 nanometer-sized Au$_{1415}$ clusters deposited on a carbon substrate, a model system. Crucially, we find that the bridge formation process is driven by the diffusion of gold atoms along the substrate, however small the gap between the clusters themselves. The complementary simulations of the bridge-breaking process reveal the existence of a threshold bias voltage to activate bridge rupture via Joule heating. These results provide an atomistic-level understanding of the fundamental dynamical processes occurring in neuromorphic cluster arrays