npj Computational Materials

http://www.nature.com/npjcompumats

List of Papers (Total 104)

Orbitally driven giant thermal conductance associated with abnormal strain dependence in hydrogenated graphene-like borophene

Heat energy in solids is carried by phonons and electrons. However, in most two-dimensional (2D) materials, the contribution from electrons to total thermal conduction is much lower than that for phonons. In this work, through first-principles calculations combined with non-equilibrium Green’s function theory, we studied electron and phonon thermal conductance in recently...

New frontiers for the materials genome initiative

The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by...

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard...

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via...

Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy

The rapid development of spectral-imaging methods in scanning probe, electron, and optical microscopy in the last decade have given rise for large multidimensional datasets. In many cases, the reduction of hyperspectral data to the lower-dimension materials-specific parameters is based on functional fitting, where an approximate form of the fitting function is known, but the...

Solving the electronic structure problem with machine learning

Simulations based on solving the Kohn-Sham (KS) equation of density functional theory (DFT) have become a vital component of modern materials and chemical sciences research and development portfolios. Despite its versatility, routine DFT calculations are usually limited to a few hundred atoms due to the computational bottleneck posed by the KS equation. Here we introduce a...

Excitation to defect-bound band edge states in two-dimensional semiconductors and its effect on carrier transport

The ionization of dopants is a crucial process for electronics, yet it can be unexpectedly difficult in two-dimensional materials due to reduced screening and dimensionality. Using first-principles calculations, here we propose a dopant ionization process for two-dimensional semiconductors where charge carriers are only excited to a set of defect-bound band edge states, rather...

Empirical modeling of dopability in diamond-like semiconductors

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits...

Predicting accurate cathode properties of layered oxide materials using the SCAN meta-GGA density functional

Layered lithium intercalating transition metal oxides are promising cathode materials for Li-ion batteries. Here, we scrutinize the recently developed strongly constrained and appropriately normed (SCAN) density functional method to study structural, magnetic, and electrochemical properties of prototype cathode materials LiNiO2, LiCoO2, and LiMnO2 at different Li-intercalation...

Quantum effects on dislocation motion from ring-polymer molecular dynamics

Quantum motion of atoms known as zero-point vibration was recently proposed to explain a long-standing discrepancy between theoretically computed and experimentally measured low-temperature plastic strength of iron and possibly other metals with high atomic masses. This finding challenges the traditional notion that quantum motion of atoms is relatively unimportant in solids...

Microstructure design using graphs

Thin films with tailored microstructures are an emerging class of materials with applications such as battery electrodes, organic electronics, and biosensors. Such thin film devices typically exhibit a multi-phase microstructure that is confined, and show large anisotropy. Current approaches to microstructure design focus on optimizing bulk properties, by tuning features that are...

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...

Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses

The hidden order of atomic packing in amorphous structures and how this may provide the origin of plastic events have long been a goal in the understanding of plastic deformation in metallic glasses. To pursue this issue, we employ here molecular dynamic simulations to create three-dimensional models for a few metallic glasses where, based on the geometrical frustration of the...

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...

Phase-field model of pitting corrosion kinetics in metallic materials

Pitting corrosion is one of the most destructive forms of corrosion that can lead to catastrophic failure of structures. This study presents a thermodynamically consistent phase field model for the quantitative prediction of the pitting corrosion kinetics in metallic materials. An order parameter is introduced to represent the local physical state of the metal within a metal...

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...

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented...

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...