256 research outputs found
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First-principles study of alkali-metal intercalation in disordered carbon anode materials
The intercalation of alkali metals in disordered carbon anode materials is studied by a combination of first-principles and machine-learning methods.</p
Lattice thermal expansion and anisotropic displacements in {\alpha}-sulfur from diffraction experiments and first-principles theory
Thermal properties of solid-state materials are a fundamental topic of study
with important practical implications. For example, anisotropic displacement
parameters (ADPs) are routinely used in physics, chemistry, and crystallography
to quantify the thermal motion of atoms in crystals. ADPs are commonly derived
from diffraction experiments, but recent developments have also enabled their
first-principles prediction using periodic density functional theory (DFT).
Here, we combine experiments and dispersion-corrected DFT to quantify lattice
thermal expansion and ADPs in crystalline {\alpha}-sulfur (S8), a prototypical
elemental solid that is controlled by the interplay of covalent and van der
Waals interactions. We first report on single-crystal and powder X-ray
diffraction (XRD) measurements that provide new and improved reference data
from 10 K up to room temperature. We then use several popular
dispersion-corrected DFT methods to predict vibrational and thermal properties
of {\alpha}-sulfur, including the anisotropic lattice thermal expansion.
Hereafter, ADPs are derived in the commonly used harmonic approximation (in the
computed zero-Kelvin structure) and also in the quasi-harmonic approximation
(QHA) which takes the predicted lattice thermal expansion into account. At the
PBE+D3(BJ) level, the latter leads to excellent agreement with experiments.
Finally, more general implications of this study for realistic materials
modeling at finite temperature are discussed
Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics
Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials
Data-Driven Learning of Total and Local Energies in Elemental Boron.
The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery
Materials Screening for Disorder-Controlled Chalcogenide Crystals for Phase-Change Memory Applications
Tailoring the degree of disorder in chalcogenide phase-change materials (PCMs) plays an essential role in nonvolatile memory devices and neuro-inspired computing. Upon rapid crystallization from the amorphous phase, the flagship Ge–Sb–Te PCMs form metastable rocksalt-like structures with an unconventionally high concentration of vacancies, which results in disordered crystals exhibiting Anderson-insulating transport behavior. Here, ab initio simulations and transport experiments are combined to extend these concepts to the parent compound of Ge–Sb–Te alloys, viz., binary Sb2Te3, in the metastable rocksalt-type modification. Then a systematic computational screening over a wide range of homologous, binary and ternary chalcogenides, elucidating the critical factors that affect the stability of the rocksalt structure is carried out. The findings vastly expand the family of disorder-controlled main-group chalcogenides toward many more compositions with a tunable bandgap size for demanding phase-change applications, as well as a varying strength of spin–orbit interaction for the exploration of potential topological Anderson insulators
Evaluation of Coach-Based Technical Assistance: An Evolving Focus on Coachability and Goal Setting
In 2013, the National Institute of Food and Agriculture supported the creation of a professional development and technical assistance center to promote strong implementation and evaluation of University-led, community-based projects serving low-resource populations. Within this center, a coaching cadre was established to provide proactive and responsive technical assistance. Formative evaluation involving coaches and their primary contacts was used for refinement of coaching practices. Initially, coaches were encouraged to build strong interpersonal rapport. This set the stage for trusting, reciprocal interactions, but coaches recognized a need for targeted support and more tools for quality programming, evaluation, and sustainability. Greater emphasis was placed on goal-focused collaboration. Coaches received training and resources on topics such as goal setting, program quality, reduction of barriers (e.g., participant recruitment), and sustainability strategies. To assess coaching model enhancements, a survey of projects was expanded to gauge logic model usage, goal setting, strength of coaching relationships, and project implementation and sustainability progress. Overall, coaching was rated more favorably and effective when contact was consistent, inclusive of face-to-face interaction, met technical needs, and involved collaborative brainstorming and planning. Findings indicate coaching relationships strengthen over time and demand a collaborative, action-orientation to set goals, reduce barriers, and drive stronger outcomes
Machine-learning of atomic-scale properties based on physical principles
We briefly summarize the kernel regression approach, as used recently in
materials modelling, to fitting functions, particularly potential energy
surfaces, and highlight how the linear algebra framework can be used to both
predict and train from linear functionals of the potential energy, such as the
total energy and atomic forces. We then give a detailed account of the Smooth
Overlap of Atomic Positions (SOAP) representation and kernel, showing how it
arises from an abstract representation of smooth atomic densities, and how it
is related to several popular density-based representations of atomic
structure. We also discuss recent generalisations that allow fine control of
correlations between different atomic species, prediction and fitting of
tensorial properties, and also how to construct structural kernels---applicable
to comparing entire molecules or periodic systems---that go beyond an additive
combination of local environments
Building nonparametric -body force fields using Gaussian process regression
Constructing a classical potential suited to simulate a given atomic system
is a remarkably difficult task. This chapter presents a framework under which
this problem can be tackled, based on the Bayesian construction of
nonparametric force fields of a given order using Gaussian process (GP) priors.
The formalism of GP regression is first reviewed, particularly in relation to
its application in learning local atomic energies and forces. For accurate
regression it is fundamental to incorporate prior knowledge into the GP kernel
function. To this end, this chapter details how properties of smoothness,
invariance and interaction order of a force field can be encoded into
corresponding kernel properties. A range of kernels is then proposed,
possessing all the required properties and an adjustable parameter
governing the interaction order modelled. The order best suited to describe
a given system can be found automatically within the Bayesian framework by
maximisation of the marginal likelihood. The procedure is first tested on a toy
model of known interaction and later applied to two real materials described at
the DFT level of accuracy. The models automatically selected for the two
materials were found to be in agreement with physical intuition. More in
general, it was found that lower order (simpler) models should be chosen when
the data are not sufficient to resolve more complex interactions. Low GPs
can be further sped up by orders of magnitude by constructing the corresponding
tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte
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Development of a Model Specification for Performance MonitoringSystems for Commercial Buildings
The paper describes the development of a model specification for performance monitoring systems for commercial buildings. The specification focuses on four key aspects of performance monitoring: (1) performance metrics; (2) measurement system requirements; (3) data acquisition and archiving; and (4) data visualization and reporting. The aim is to assist building owners in specifying the extensions to their control systems that are required to provide building operators with the information needed to operate their buildings more efficiently and to provide automated diagnostic tools with the information required to detect and diagnose faults and problems that degrade energy performance. The paper reviews the potential benefits of performance monitoring, describes the specification guide and discusses briefly the ways in which it could be implemented. A prototype advanced visualization tool is also described, along with its application to performance monitoring. The paper concludes with a description of the ways in which the specification and the visualization tool are being disseminated and deployed
Machine-learned acceleration for molecular dynamics in CASTEP
Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research
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