Showing papers in "Computational Materials Science in 2019"

TL;DR: The Dusseldorf Advanced Material Simulation Kit (DAMASK) as mentioned in this paper is an open, flexible, and easy to use implementation to the scientific community that allows the use and straightforward implementation of different types of constitutive laws and numerical solvers.

TL;DR: In this paper, the authors proposed an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset.

TL;DR: In this paper, the effects of oxygen vacancies and typical impurity elements on ferroelectric phase transition and polarization performance of HfO2-based film were systematically investigated by first-principle calculation.

TL;DR: In this article, a machine learning-based model was proposed to predict the lattice thermal conductivity of inorganic materials, using a benchmark data set of experimentally measured κ L of about 100 inorganic material.

TL;DR: In this paper, four new A-D-A type fused ring electron acceptor molecules (M1, M2, M3 and M4) are evaluated for their opto-electronic properties for transparent organic solar cells.

TL;DR: The capability of the PRISMS-Plasticity software to efficiently solve crystal plasticity boundary value problems, in addition to integration with preprocessing and postprocessing tools is presented.

TL;DR: In this paper, the authors employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals using a neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field.

TL;DR: In this article, a recently proposed class of machine-learning interatomic potentials, Moment tensor potentials (MTPs), are used to parametrize the potential on-the-fly.

TL;DR: This approach combines molecular dynamics simulations with topological constraint theory with a simple, yet powerful framework that can be used to predict the compositional dependence of glass properties or pinpoint promising compositions with tailored functionalities.

TL;DR: The AFLOW Library of Crystallographic Prototypes is developed, a collection of crystal prototypes that can be rapidly decorated using the AFLOW software, which consists of 590 unique crystallographic prototypes covering all 230 space groups.

TL;DR: This work developed an automatic convergence procedure for k-points and plane wave cut-off in density functional (DFT) calculations and applied it to more than 30000 materials and identified some material species that would require more careful convergence than others.

TL;DR: In these examples it is shown how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented.

TL;DR: A machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail, using deep learning and simulations from high-fidelity models.

TL;DR: In this paper, the impact of various point defects on the structural, magnetic and electronic properties of graphene-like boron phosphide (h-BP) monolayer was studied using density functional theory.

TL;DR: This work proposes the use of machine learning technique, namely Gaussian process regression, with a small number of full-field simulation results to construct structure-property linkages that are accurate over a wide range of microstructures.

TL;DR: The efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys are reviewed.

TL;DR: This paper proposes a machine-learning-based method using nonlinear programming for multiple properties of the materials, and solves the problem by using the Interior Point Algorithm, capable of processing the restrictions of these properties.

TL;DR: The present study demonstrates that using a support vector machine in combination with pixel-based and morphology-based parameters allows a reliable classification based on microstructural images.

TL;DR: It is believed that the combinations of high-throughput multi-scale computations and fast experiments/manufacturing will build the advanced algorithms in the development of a promising digital fabricating approach to overcome the present and future challenges, illuminating the way toward the digital-twin intelligent manufacturing era.

TL;DR: In this paper, the authors proposed a transition-metal dichalcogenide (TMD) monolayer ZrSSe by ab initio calculations, where the generalized gradient approximation (GGA) plus spin-orbit coupling (SOC) is used as exchange-correlation potential, while GGA for lattice part.

TL;DR: In this article, a series of novel D-A-π-A organic dyes with different π-spacers for dye-sensitized solar cells (DSSCs) are designed based on hetero-tri-arylamine donor-based dye DP2.

TL;DR: In this article, the authors introduce the computational methods based on density functional theory (DFT) for investigating the mechanism of ORR, and for a specific case, the applications of DFT calculations on the researches of Pt-based alloys catalyzing ORR are summarized.

TL;DR: In this article, the authors conduct density functional theory (DFT) simulations to study the applicability of two-dimensional Vanadium dichalcogenides (VS2 and VSe2) as anode materials for Li-, Mg-, Ca-, Na- or Li-ion batteries.

TL;DR: In this paper, nanoindentation of the Tin+1CnTx MXenes was studied via atomistic simulations utilizing a parametrization of ReaxFF interatomic potential, to understand the influence of point defects.

TL;DR: In this paper, the weak coupling of a Finite Element (FE) model with a cellular automaton (CA) model was used to predict the microstructure evolution during SEBM.

TL;DR: In this article, the branching and deflecting behavior of a macro (main) crack in the presence of multiple number of micro-cracks at the vicinity of the crack tip is investigated.

TL;DR: In this article, a machine-learning-driven interatomic potential for MoS2-MoSe2 system based on the spectral neighbor analysis approach is parameterized by learning from a large amount of data generated from first-principles.

TL;DR: In this paper, the density functional theory was used to show that the SiGe sheet provides moderate/low migration energy barriers for the alkali metal atoms (0.14-0.35eV), suggesting fast charge/discharge rates.

TL;DR: In this paper, molecular dynamics simulations are employed to study mechanistic characteristics of Cu-Zr metallic glasses films during the imprinting process, and the results show that imprinting force and the shear transformation zones (STZs) of the Cu50Zr50 MGs films increase, while the residual stress has slightly changed with increasing angle of the punch.

TL;DR: This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology that provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods.