npj Computational Materials

http://www.nature.com/npjcompumats

List of Papers (Total 22)

Entropy contributions to phase stability in binary random solid solutions

High entropy alloys contain multiple elements in large proportions that make them prone to phase separation. These alloys generally have shallow enthalpy of mixing which makes the entropy contributions of similar magnitude. As a result, the phase stability of these alloys is equally dependent on enthalpy and entropy of mixing and understanding the individual contribution of...

Online search tool for graphical patterns in electronic band structures

Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a “Mexican hat” pattern or an effectively free electron gas, characterized by a parabolic dispersion. To find material realizations of these features, manual...

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets...

Multiobjective genetic training and uncertainty quantification of reactive force fields

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex...

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state (EOS) for crystalline solids describe material behaviors under changes in pressure, volume, entropy and temperature, making them fundamental to scientific research in a wide range of fields including geophysics, energy storage and development of novel materials. Despite over a century of theoretical development and experimental testing of energy...

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions...

Automated defect analysis in electron microscopic images

Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude...

Fine-grained optimization method for crystal structure prediction

Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures. Relaxing a structure requires multiple optimization steps. It is time consuming to fully relax all the initial structures, but it is difficult to figure out which initial structure leads to the optimal solution in advance. In this...

Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence

We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition, with the voltage spectroscopy data being used as a proxy for the (unknown) microscopic state or thermodynamic parameters of materials. Given the discrete nature of the measurement points, we use Gaussian processes...

Efficient first-principles prediction of solid stability: Towards chemical accuracy

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of...

Displacement Current in Domain Walls of Bismuth Ferrite

In 1861, Maxwell conceived the idea of the displacement current, which then made laws of electrodynamics more complete and also resulted in the realization of devices exploiting such displacement current. Interestingly, it is presently unknown if such displacement current can result in large intrinsic ac current in ferroic systems possessing domains, despite the flurry of recent...

First-principles screening of structural properties of intermetallic compounds on martensitic transformation

Martensitic transformation with good structural compatibility between parent and martensitic phases are required for shape memory alloys (SMAs) in terms of functional stability. In this study, first-principles-based materials screening is systematically performed to investigate the intermetallic compounds with the martensitic phases by focusing on energetic and dynamical...

First-principles prediction of high-entropy-alloy stability

High entropy alloys (HEAs) are multicomponent compounds whose high configurational entropy allows them to solidify into a single phase, with a simple crystal lattice structure. Some HEAs exhibit desirable properties, such as high specific strength, ductility, and corrosion resistance, while challenging the scientist to make confident predictions in the face of multiple competing...

Empirical interatomic potentials optimized for phonon properties

Molecular dynamics simulations have been extensively used to study phonons and gain insight, but direct comparisons to experimental data are often difficult, due to a lack of accurate empirical interatomic potentials for different systems. As a result, this issue has become a major barrier to realizing the promise associated with advanced atomistic-level modeling techniques. Here...

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

Advances in solar cell technology require designing of new organic dye sensitizers for dye-sensitized solar cells with high power conversion efficiency to circumvent the disadvantages of silicon-based solar cells. In silico studies including quantitative structure-property relationship analysis combined with quantum chemical analysis were employed to understand the primary...

Computationally predicted energies and properties of defects in GaN

Recent developments in theoretical techniques have significantly improved the predictive power of density-functional-based calculations. In this review, we discuss how such advancements have enabled improved understanding of native point defects in GaN. We review the methodologies for the calculation of point defects, and discuss how techniques for overcoming the band-gap problem...

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation, fabrication, and characterization. In particular, machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition–phase maps...

Reentrant equilibrium disordering in nanoparticle–polymer mixtures

A large body of experimental work has established that athermal colloid/polymer mixtures undergo a sequence of transitions from a disordered fluid state to a colloidal crystal to a second disordered phase with increasing polymer concentration. These transitions are driven by polymer-mediated interparticle attraction, which is a function of both the polymer density and size. It...

Quantum–continuum simulation of underpotential deposition at electrified metal–solution interfaces

The underpotential deposition of transition metal ions is a critical step in many electrosynthetic approaches. While underpotential deposition has been intensively studied at the atomic level, first-principles calculations in vacuum can strongly underestimate the stability of underpotentially deposited metals. It has been shown recently that the consideration of co-adsorbed...

Predictive modelling of ferroelectric tunnel junctions

Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From...

On the tuning of electrical and thermal transport in thermoelectrics: an integrated theory–experiment perspective

During the last two decades, we have witnessed great progress in research on thermoelectrics. There are two primary focuses. One is the fundamental understanding of electrical and thermal transport, enabled by the interplay of theory and experiment; the other is the substantial enhancement of the performance of various thermoelectric materials, through synergistic optimisation of...

Cybermaterials: materials by design and accelerated insertion of materials

Cybermaterials innovation entails an integration of Materials by Design and accelerated insertion of materials (AIM), which transfers studio ideation into industrial manufacturing. By assembling a hierarchical architecture of integrated computational materials design (ICMD) based on materials genomic fundamental databases, the ICMD mechanistic design models accelerate innovation...