npj Computational Materials
EISSN : 2057-3960
Published by: Springer Nature (10.1038)
Total articles ≅ 634
Latest articles in this journal
npj Computational Materials, Volume 7, pp 1-6; https://doi.org/10.1038/s41524-021-00608-3
The discovery of substrate materials has been dominated by trial and error, opening the opportunity for a systematic search. We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals. Successful cleavage reduces the bonding network to two periodic dimensions. We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals, 4626 of which have a maximum Miller index of one. We characterize the likelihood of cleavage by creating monolayers of these surfaces and calculating their thermodynamic stability using density functional theory to discover 3991 potential substrates. Following, we identify distinct trends in the work of cleavage and relate them to bonding in the three-dimensional precursor. We illustrate the potential impact of the substrate database by identifying several improved epitaxial substrates for the transparent conductor BaSnO3. The open-source databases of predicted and commercial substrates are available at MaterialsWeb.org.
npj Computational Materials, Volume 7, pp 1-10; https://doi.org/10.1038/s41524-021-00620-7
In this work, we demonstrate that damage mechanism identification from acoustic emission (AE) signals generated in minicomposites with elastically similar constituents is possible. AE waveforms were generated by SiC/SiC ceramic matrix minicomposites (CMCs) loaded under uniaxial tension and recorded by four sensors (two models with each model placed at two ends). Signals were encoded with a modified partial power scheme and subsequently partitioned through spectral clustering. Matrix cracking and fiber failure were identified based on the frequency information contained in the AE event they produced, despite the similar constituent elastic properties of the matrix and fiber. Importantly, the resultant identification of AE events closely followed CMC damage chronology, wherein early matrix cracking is later followed by fiber breaks, even though the approach is fully domain-knowledge agnostic. Additionally, the partitions were highly precise across both the model and location of the sensors, and the partitioning was repeatable. The presented approach is promising for CMCs and other composite systems with elastically similar constituents.
npj Computational Materials, Volume 7, pp 1-12; https://doi.org/10.1038/s41524-021-00619-0
The interactions between electrons and phonons play the key role in determining the carrier transport properties in semiconductors. In this work, comprehensive investigations on full electron–phonon (el–ph) couplings and their influences on carrier mobility and thermoelectric (TE) performances of 2D group IV and V elemental monolayers are performed, and we also analyze the selection rules on el–ph couplings using group theory. For shallow n/p-dopings in Si, Ge, and Sn, ZA/TA/LO phonon modes dominate the intervalley scatterings. Similarly strong intervalley scatterings via ZA/TO phonon modes can be identified for CBM electrons in P, As, and Sb, and for VBM holes, ZA/TA phonon modes dominate intervalley scatterings in P while LA phonons dominate intravalley scatterings in As and Sb. By considering full el–ph couplings, the TE performance for these two series of monolayers are predicted, which seriously downgrades the thermoelectric figures of merits compared with those predicted by the constant relaxation time approximation.
npj Computational Materials, Volume 7, pp 1-6; https://doi.org/10.1038/s41524-021-00610-9
In situ growth of pyrochlore iridate thin films has been a long-standing challenge due to the low reactivity of Ir at low temperatures and the vaporization of volatile gas species such as IrO3(g) and IrO2(g) at high temperatures and high P O2. To address this challenge, we combine thermodynamic analysis of the Pr-Ir-O2 system with experimental results from the conventional physical vapor deposition (PVD) technique of co-sputtering. Our results indicate that only high growth temperatures yield films with crystallinity sufficient for utilizing and tailoring the desired topological electronic properties and the in situ synthesis of Pr2Ir2O7 thin films is fettered by the inability to grow with P O2 on the order of 10 Torr at high temperatures, a limitation inherent to the PVD process. Thus, we suggest techniques capable of supplying high partial pressure of key species during deposition, in particular chemical vapor deposition (CVD), as a route to synthesis of Pr2Ir2O7.
npj Computational Materials, Volume 7, pp 1-21; https://doi.org/10.1038/s41524-021-00612-7
The complex degradation of metallic materials in aggressive environments can result in morphological and microstructural changes. The phase-field (PF) method is an effective computational approach to understanding and predicting the morphology, phase change and/or transformation of materials. PF models are based on conserved and non-conserved field variables that represent each phase as a function of space and time coupled with time-dependent equations that describe the mechanisms. This report summarizes progress in the PF modeling of degradation of metallic materials in aqueous corrosion, hydrogen-assisted cracking, high-temperature metal oxidation in the gas phase and porous structure evolution with insights to future applications.
npj Computational Materials, Volume 7, pp 1-11; https://doi.org/10.1038/s41524-021-00618-1
Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles (HANPs) are still lacking. A multiscale multisource dataset is presented, including both experimental data (TEM/SEM, XRD/crystallinity, ROS, anti-tumor effects, and zeta potential) and computation results (containing 41,976 data samples with up to 9768 atoms) of nanoparticles with different sizes and morphologies at density functional theory (DFT), semi-empirical DFTB, and force field, respectively. Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance. To avoid the pre-determination of features, we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability. Energies with DFT accuracy are achievable for large-sized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes. The simulated XRD spectra and crystallinity values are in good agreement with experiments.
npj Computational Materials, Volume 7, pp 1-8; https://doi.org/10.1038/s41524-021-00600-x
Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.
npj Computational Materials, Volume 7, pp 1-9; https://doi.org/10.1038/s41524-021-00606-5
The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.
npj Computational Materials, Volume 7, pp 1-10; https://doi.org/10.1038/s41524-021-00607-4
The compositional design of metallic glasses (MGs) is a long-standing issue in materials science and engineering. However, traditional experimental approaches based on empirical rules are time consuming with a low efficiency. In this work, we successfully developed a hybrid machine learning (ML) model to address this fundamental issue based on a database containing ~5000 different compositions of metallic glasses (either bulk or ribbon) reported since 1960s. Unlike the prior works relying on empirical parameters for featurization of data, we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML algorithms. Our hybrid ML modeling was validated both numerically and experimentally. Most importantly, it enabled the discovery of MGs (either bulk or ribbon) through the ML-aided deep search of a multitude of quaternary to scenery alloy compositions. The computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.
npj Computational Materials, Volume 7, pp 1-10; https://doi.org/10.1038/s41524-021-00605-6
To accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals.