Robust cryogenic ab-initio quantum transport simulation for L\u3csub\u3eG\u3c/sub\u3e = 10 nm nanowire

In this paper, we propose a simulation methodology for robust and accurate ab-initio quantum transport simulation down to 3 K of an n-type Si nanowire. This is important to understand the subthreshold swing (SS) at cryogenic temperature. We show that for LG = 10 nm, the SS is fully dominated by direct tunneling at cryogenic temperature, which is the first time to be demonstrated using ab-initio simulation, to the best of our knowledge. We propose a method to achieve more than 2x speed up in the simulation time and achieve convergence at high gate biases. It is also shown that from the leakage perspective, there is no advantage in operating LG = 10 nm transistor below 77 K

Protein sequencing strategy in nanotechnology by classical and quantum atomistic models

Il mio lavoro di ricerca ha avuto l’obiettivo di studiare le proprietà principali dell’interazione tra materiale biologico e superfici inorganiche. Per tale scopo è stato utilizzato uno approccio basato sulla teoria del funzionale densità (DFT), lo studio è stato svolto nell’ambito del calcolo ad alte prestazioni utilizzando un approccio quanto-meccanico da principi primi, in modo da poter descrivere al meglio le interazioni chimico-fisiche a livello molecolare e sub-molecolare. Il tutto è stato applicato in dispositivi di ultima generazione per sequenziamento di catene biologiche, basate sulla tecnologia a nano-poro; in questi sensori nano-strutturati vi è un analisi detta a singola-molecola, il tipo e i modi d’interazioni tra dispositivo e target d’analizzare sono fondamentali e determinano la variazione del nostro segnale in uscita. Il funzionamento è relativamente semplice: si applica agli estremi della superfice con il poro una differenza di potenziale, e si misura la variazione della corrente quando il foro è occupato; le dimensioni del poro fanno si che si possa analizzare una molecola alla volta. Il materiale scelto per la realizzazione di questi dispositivi è stato il grafene per le sue proprietà elettroniche e la sua geometria; le catene biologiche scelte sono sequenze di amminoacidi; questa scelta si basa sulla possibile evoluzione di questi dispositivi, finora utilizzati per il sequenziamento di DNA (commercializzato dalla Oxford Nanopores Technologies), e sull’importanza dell’identificazione della sequenza e della struttura delle proteine, visto la connessione a patologie neurodegenerative come Parkinson e Alzheimer. Più in dettaglio il mio lavoro di ricerca è partito da studi precedenti dove venivano analizzate filamenti di DNA con la traslazione di basi nucleiche in nano-pori biologici per il sequenziamento; si sono studiati i cambiamenti caratteristici di corrente quando il target si avvicina alla superficie o attraversava il poro in modo da ottenere un'analisi rapida a singola-molecola. Si è provato, così, ad applicare lo stesso principio su sequenze di peptidi, per la loro importanza a livello medico-scientifico, con nanostrutture allo stato solido, visti i vantaggi di quest'ultimi rispetto a quelli biologici (miglior rapporto segnale rumore e una vita media più lunga). Sono state effettuate simulazioni Ab-Initio per caratterizzare sia le proprietà elettroniche superficiali, osservando la densità degli stati (DOS), e sia l'effetto quantistico del tunneling degli elettroni al variare della molecola interagente con la superficie. Per fare ciò si è studiata la corrente elettronica trasversale al piano del poro, su un ribbon di grafene, correlando le variazioni della nube elettronica con la molecola target. Per raggiungere questi obiettivi abbiamo: • definito modelli atomistici di interazione tra amminoacidi e bordi di un nano-poro di grafene; • utilizzato simulazioni atomistiche/molecolari per ottimizzare la morfologia (grandezza) e struttura (forma) più adeguata del poro; • studiato il funzionamento elettronico del nano-poro in fase di traslocazione degli amminoacidi attraverso esso. In particolare ci si è concentrati sul calcolo della conduttività trasversale attraverso la metodologia della Non-Equilibrium Green Function (NEGF) e l'approccio Landauer-Buttiker La ricerca è stata articolata in due fasi: I Fase: Design del nano-poro di grafene Nonostante diversi nano-pori siano già studiati con tecniche sperimentali, l’approccio teorico-modellistico basato su simulazioni molecolari atomistiche della struttura del nano-poro ha reso possibile avere una rigorosa caratterizzazione fisica e chimica del sistema; questa caratterizzazione è diventata la base per il successivo processo di ottimizzazione del dispositivo (passando da un nano-poro a un nano-gap). Dal punto di vista teorico-computazionale, si è confrontato il comportamento, strutturale del passaggio all’interno del sensore di diversi amminoacidi e si è progettato un nano-gap adatto alla valutazione degli effetti di traslocazione. II Fase: Caratterizzazione del segnale Si è studiata la variazione del “segnale” ottenuto, per caratterizzarlo al meglio e abbassare il rapporto segnale rumore. Attraverso varie analisi di post-processing si è andata a vedere la corrente elettronica elastica ed anelastica e si è aggiunta l’analisi della corrente ionica con simulazioni di dinamica molecolare classica

Li+ solvation in pure, binary and ternary mixtures of organic carbonate electrolytes

Classical molecular dynamics (MD) simulations and quantum chemical density functional theory (DFT) calculations have been employed in the present study to investigate the solvation of lithium cations in pure organic carbonate solvents (ethylene carbonate (EC), propylene carbonate (PC) and dimethyl carbonate (DMC)) and their binary (EC-DMC, 1:1 molar composition) and ternary (EC-DMC-PC, 1:1:3 molar composition) mixtures. The results obtained by both methods indicate that the formation of complexes with four solvent molecules around Li+, exhibiting a strong local tetrahedral order, is the most favorable. However, the molecular dynamics simulations have revealed the existence of significant structural heterogeneities, extending up to a length scale which is more than five times the size of the first coordination shell radius. Due to these significant structural fluctuations in the bulk liquid phases, the use of larger size clusters in DFT calculations has been suggested. Contrary to the findings of the DFT calculations on small isolated clusters, the MD simulations have predicted a preference of Li+ to interact with DMC molecules within its first solvation shell and not with the highly polar EC and PC ones, in the binary and ternary mixtures. This behavior has been attributed to the local tetrahedral packing of the solvent molecules in the first solvation shell of Li+, which causes a cancellation of the individual molecular dipole vectors, and this effect seems to be more important in the cases where molecules of the same type are present. Due to these cancellation effects, the total dipole in the first solvation shell of Li+ increases when the local mole fraction of DMC is high

Quantum properties of atomic-sized conductors

Using remarkably simple experimental techniques it is possible to gently break a metallic contact and thus form conducting nanowires. During the last stages of the pulling a neck-shaped wire connects the two electrodes, the diameter of which is reduced to single atom upon further stretching. For some metals it is even possible to form a chain of individual atoms in this fashion. Although the atomic structure of contacts can be quite complicated, as soon as the weakest point is reduced to just a single atom the complexity is removed. The properties of the contact are then dominantly determined by the nature of this atom. This has allowed for quantitative comparison of theory and experiment for many properties, and atomic contacts have proven to form a rich test-bed for concepts from mesoscopic physics. Properties investigated include multiple Andreev reflection, shot noise, conductance quantization, conductance fluctuations, and dynamical Coulomb blockade. In addition, pronounced quantum effects show up in the mechanical properties of the contacts, as seen in the force and cohesion energy of the nanowires. We review this reseach, which has been performed mainly during the past decade, and we discuss the results in the context of related developments.Comment: Review, 120 pages, 98 figures. In view of the file size figures have been compressed. A higher-resolution version can be found at: http://lions1.leidenuniv.nl/wwwhome/ruitenbe/review/QPASC-hr-ps-v2.zip (5.6MB zip PostScript

Computational methods for 2D materials modelling

Materials with thickness ranging from a few nanometers to a single atomic layer present unprecedented opportunities to investigate new phases of matter constrained to the two-dimensional plane.Particle-particle Coulomb interaction is dramatically affected and shaped by the dimensionality reduction, driving well-established solid state theoretical approaches to their limit of applicability. Methodological developments in theoretical modelling and computational algorithms, in close interaction with experiments, led to the discovery of the extraordinary properties of two-dimensional materials, such as high carrier mobility, Dirac cone dispersion and bright exciton luminescence, and inspired new device design paradigms. This review aims to describe the computational techniques used to simulate and predict the optical, electronic and mechanical properties of two-dimensional materials, and to interpret experimental observations. In particular, we discuss in detail the particular challenges arising in the simulation of two-dimensional constrained fermions, and we offer our perspective on the future directions in this field.Comment: This submission does not include the third party cited figure

Ab initio structure prediction methods for battery materials a review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the Ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.(1) Gates Cambridge Trust, University of Cambridge, UK (2) EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, UK, Grant No. EP/L015552/1. (3) Winton Programme for the Physics of Sustainability, University of Cambridge, UK (4) Sims Fund, University of Cambridge, UK (5) EPSRC Grant No. EP/P003532/1 (6) EPSRC Collaborative Computational Projects on the Electronic Structure of Condensed Matter (CCP9), Grant No. EP/M022595/1, and NMR crystallography, Grant No. EP/M022501/1 (7) Computing resources on the Tier 1 resource ARCHER were provided through the UKCP EPSRC High-End computational consortium (EP/P022561/1) and on the Tier 2 resources HPC Midlands+ (EP/P020232/1) and CSD3 (EP/P020259/1)

The 2019 materials by design roadmap

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design