Encapsulated nanowires : boosting electronic transport in carbon nanotubes

The electrical conductivity of metallic carbon nanotubes (CNTs) quickly saturates with respect to bias voltage due to scattering from a large population of optical phonons. The decay of these dominant scatterers in pristine CNTs is too slow to offset an increased generation rate at high voltage bias. We demonstrate from first principles that encapsulation of one-dimensional atomic chains within a single-walled CNT can enhance the decay of “hot” phonons by providing additional channels for thermalization. Pacification of the phonon population growth reduces the electrical resistivity of metallic CNTs by 51% for an example system with encapsulated beryllium

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

At the high level, the fundamental differences between materials originate from the unique nature of the constituent chemical elements. Before specific differences emerge according to the precise ratios of elements (composition) in a given crystal structure (phase), the material can be represented by its phase field defined simply as the set of the constituent chemical elements. Classification of the materials at the level of their phase fields can accelerate materials discovery by selecting the elemental combinations that are likely to produce desirable functional properties in synthetically accessible materials. Here, we demonstrate that classification of the materials phase field with respect to the maximum expected value of a target functional property can be combined with the ranking of the materials synthetic accessibility. This end-to-end machine learning approach (PhaseSelect) first derives the atomic characteristics from the compositional environments in all computationally and experimentally explored materials and then employs these characteristics to classify the phase field by their merit. PhaseSelect can quantify the materials potential at the level of the periodic table, which we demonstrate with significant accuracy for three avenues of materials applications: high-temperature superconducting, high-temperature magnetic and targetted energy band gap materials

Superionic lithium transport via multiple coordination environments defined by two-anion packing

Fast cation transport in solids underpins energy storage. Materials design has focused on structures that can define transport pathways with minimal cation coordination change, restricting attention to a small part of chemical space. Motivated by the greater structural diversity of binary intermetallics than that of the metallic elements, we used two anions to build a pathway for three-dimensional superionic lithium ion conductivity that exploits multiple cation coordination environments. Li 7 Si 2 S 7 I is a pure lithium ion conductor created by an ordering of sulphide and iodide that combines elements of hexagonal and cubic close-packing analogously to the structure of NiZr. The resulting diverse network of lithium positions with distinct geometries and anion coordination chemistries affords low barriers to transport, opening a large structural space for high cation conductivity. </jats:p

Nonequilibrium statistical theory and ab anitio approach on model electron-ionic systems

Wydział FizykiProcesy nierównowagowe determinują właściwości nanorozmiarowych układów wieloelektronowych i stanowią przedmiot aktualnych badań podstawowych oraz ich aktywnej realizacji w technologii. Zrozumienie takich procesów nierównowagowych jak to adsorpcja, dyfuzja, procesy katalityczne, procesy transportu jest niezbędne dla rozwoju technologii katalitycznych, budowy supersieci, clasterów samoorganizujących, urządzeń opartych na nanostrukturach itp. Większość istniejących teorii dla układów wieloelektronowych rozpatruje procesy nierównowagowe w ramach zbliżenia jednorodnego gazu elektronowego. Także wiele podejść teoretycznych opisuje zachowanie układów wieloelektronowych w przybliżeniu liniowego potencjału elektrochemicznego, które zakazuje opis stanów silnie nierównowagowych. Ta praca doktorska adresuje wspomniane problemy współczesnej nierównowagowej statystycznej teorii dla niejednorodnych wieloelektronowych i jonowych struktur. Ważnym punktem badań jest opracowanie podejścia, które pozwala na opis słabych i silnych stanów nierównowagowych, biorąc pod uwagę niejednorodność podsystemu elektronowego oraz odrębność układów jonowych, zawierającego nieliniowość potencjału chemicznego i oddziaływania elektromagnetyczne. W dużej części przeprowadzone badania dotyczą sposobu budowy uogólnionych równań transportu, które uwzględniają zasadnicze procesy nierównowagowe: adsorpcji, desorpcji, dyfuzji elektronów, lepkich przepływów, jonizacji i polaryzacji jonów. Badania niektórych z rozpatrywanych układów są poparte obliczeniami numerycznymi wykonanymi za pomocą techniki ab initio na bazie teorii funkcjonału gęstości (DFT). Praca składa się z trzech części. Pierwszy rozdział dotyczy budowy nierównowagowej statystycznej teorii dla niejednorodnego gazu elektronowego w przybliżeniu modelu ``jellium''. Za pomocą nierównowagowego statystycznego operatora Zubareva (NSO) znaleziono łańcuch równań dla funkcji Greena. Uzyskano uogólnione równania transportu dla dyfuzji elektronów, lepko-sprężystego i lepko-ciepłowego przybliżenia dla modelu pół-nieskończonego metalu. Wyniki przeprowadzonych badań udowodniają, że podejście NSO wraz z przybliżeniem lepko-ciepłowym dla niejednorodnego gazu elektronowego uogólnia podejście teorii prądowej gęstości zależnej od czasu (TDCDT). Wyniki pierwszego rozdziału są opublikowane w czterech artykułach. W drugim rozdziale metoda NSO jest wykorzystana do opisu hydrodynamiki jonowych topi, biorąc pod uwagę polaryzację jonową. Zastosowanie teorii perturbacji w odniesieniu do korelacji daje widmo grupowych wzbudzeń układów wielojonowych. Uzyskane wyniki analityczne są jakościowo zgodne z wynikami podejścia uogólnionych trybów grupowych (generalized collective modes) opartym na ab initio (z uwzględnieniem polaryzacji) i symulacjami dynamiki molekularnej. Wyniki badań opublikowane w jednym artykule. W trzecim rozdziale rozwinięte nierównowagowe statystyczne podejście jest zastosowane do badania procesów adsorpcji na wieloelektronowych węglowych nanostrukturach. Uzyskany układ równań łączy procesy adsorpcji, desorpcji, jonizacji, polaryzacji atomów w polu elektromagnetycznym nanorurek węglowych. Obliczenia z wykorzystaniem techniki DFT zapewniają wartości dla energii adsorpcji He, NO, biorąc pod uwagę istnienie wakatu elektronowego w strukturze. Na podstawie obliczonych wartości energii adsorpcji grup COH, COOH na nanorurkach węglowych o różnych średnicach i chiralności zaprezentowano najbardziej prawdopodobny szlak powstania funkcjonalizacji nanorurek węglowych powyżej wymienionymi grupami funkcyjnymi. Wyniki zostały opublikowane w dwóch artykułach.Nonequilibrium processes that determine properties of the many-electrons nanoscale structures are the subject for the topical academic researches and for active implementation into technologies. Understanding of nonequilibrium processes such as adsorption, diffusion, catalytic processes, and pecularities of transport processes is essential for development of catalytic technologies, construction of superlattices, self-organizing clasters, nanostructure-based devices etc. Most of the existent theories for many-electrons systems consider nonequilibrium processes within approximation of homogeneous electron gas. Many of the theoretical approaches approximate the behaviour of the systems with linear electrochemical potential that prohibit description of strong nonequilibrium states. This doctoral thesis addresses the mentioned problems of modern nonequilibrium statistical theory of inhomogenious many-electrons and ionic structures. The stressed point of investigation is to develop an approach that allows for description of weak and strong nonequilibrium states, takes into account inhomogeneity of electron subsystem as well as discretness of ionic one, considers non-linearity of chemical potential and explicitely includes electro-magnetic interactions. In great part the conducted investigations concern the method of construction of the generalized transport equations that take into account the essential nonequilibrium processes: adsorption, desorption, electron diffusion, viscous fluxes, ionization and polarization of ions. Some researches of the considered systems are supported with the numerical calculations performed by means of the first-principles density functional theory (DFT)-based computational techniques. The work is presented with three sections. First section addresses the nonequilibrium statistical theory of inhomogeneous electron gas within ``jellium'' model. By means of the Zubarev nonequilibrium statistical operator (NSO) a chain of equations for the Green functions is derived. Generalized transport equations for electron-diffusion, viscous-elastic and viscous-heat approximations are obtained for model of semi-infinite metal. The results of the investigations demonstrate that NSO approach within viscous-heat approximation for inhomogeneous electron gas generalizes the approach of time-dependent current density theory (TDCDT). The results of the first section are published in four articles. In the second section the NSO method is used for description of molecular hydrodynamics of ionic melts, while taking into account polarization. The applied theory of perturbation with respect to correlations yields the spectrum of collective excitations of many-particles ionic systems. The obtained analytical results are in a qualitative agreement with the results of the generalized collective modes approach based on ab initio (considering the polarization effects) and the rigid-ion molecular dynamics simulations. The results are presented in one article. The third section demontrates the nonequilibrium statistical approach applied to adsorption processes on many-electrons nanostructure. The derived set of equations desribes processes of adsorption, desorption, ionization, polarization of gas atoms in the electro-magnetic field of carbon nanotubes. The calculations with use of density functional theory provide the values for adsorption energies of He, NO, while taking into account vacation effects; the most energetically preferable functionalization path is drown on the basis of chemisorption energies of COH, COOH groups calculated for various diameters and chiralities of carbon nanotubes. The results are published in two articles

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties

Abstract The unique nature of constituent chemical elements gives rise to fundamental differences in materials. Assessing materials based on their phase fields, defined as sets of constituent elements, before specific differences emerge due to composition and structure can reduce combinatorial complexity and accelerate screening, exploiting the distinction from composition-level approaches. Discrimination and evaluation of novelty of materials classes align with the experimental challenge of identifying new areas of chemistry. To address this, we present PhaseSelect, an end-to-end machine learning model that combines representation, classification, regression and novelty ranking of phase fields. PhaseSelect leverages elemental characteristics derived from computational and experimental materials data and employs attention mechanisms to reflect the individual element contributions when evaluating functional performance of phase fields. We demonstrate this approach for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy applications, showcasing its versatility and potential for accelerating materials exploration

Li4.3AlS3.3Cl0.7: A Sulfide-Chloride Lithium Ion Conductor with a Highly Disordered Structure

Mixed anion materials and anion doping are very promising strategies to improve solid-state electrolyte properties by enabling an optimized balance between good electrochemical stability and high ionic conductivity. In this work, we present the discovery of a novel lithium aluminum sulfide-chloride phase. The structure is strongly affected by the presence of chloride anions on the sulfur site, as this stabilizes a higher symmetry phase presenting a large degree of cationic site disorder, as well as disordered octahedral lithium vacancies, in comparison with Li-Al-S ternaries. The effect of disorder on the lithium conductivity properties was assessed by a combined experimental-theoretical approach. In particular, the conductivity is increased by a factor 103 compared to the pure sulfide phases. Although it remains moderate (10−6 S·cm-1), Ab Initio Molecular Dynamics and Maximum Entropy (applied to neutron diffraction data) methods show that disorder leads to a 3D diffusion pathway, where Li atoms move thanks to a concerted mechanism. An understanding of the structure-property relationships is developed to determine the limiting factor governing lithium ion conductivity. This analysis, added to the strong step forward obtained in the determination of the dimensionality of diffusion paves the way for accessing even higher conductivity in materials comprising an hcp anion arrangement

Statistically derived proxy potentials accelerate geometry optimization of crystal structures.

The crystal structures of known materials contain the information about the interatomic interactions that produced these stable compounds. Similar to the use of reported protein structures to extract effective interactions between amino acids, that has been a useful tool in protein structure prediction, we demonstrate how to use this statistical paradigm to learn the effective inter-atomic interactions in crystalline inorganic solids. By analyzing the reported crystallographic data for inorganic materials, we have constructed statistically derived proxy potentials (SPPs) that can be used to assess how realistic or unusual a computer-generated structure is compared to the reported experimental structures. The SPPs can be directly used for structure optimization to improve this similarity metric, that we refer to as the SPP score. We apply such optimization step to markedly improve the quality of the input crystal structures for DFT calculations and demonstrate that the SPPs accelerate geometry optimization for three systems relevant to battery materials. As this approach is chemistry-agnostic and can be used at scale, we produced a database of all possible pair potentials in a tabulated form ready to use