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Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-7 (2020)
Cu3SbSe3 that exhibits distinct liquid-like sublattice due to the heterogeneous bonding environment has emerged as a promising low cost superionic semiconductor with intrinsic ultralow thermal conductivity. However, the relationship between atomic dy
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-9 (2020)
Creation of a partially filled intermediate band in a photovoltaic absorber material is an appealing concept for increasing the quantum efficiency of solar cells. Recently, we showed that formation of a partially filled intermediate band through dopi
Autor:
Pedro Borlido, Fabien Tran, Miguel A. L. Marques, Silvana Botti, Ahmad W. Huran, Jonathan Schmidt
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-17 (2020)
We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids. First, we use the large materials data set that we have recently proposed to benc
Publikováno v:
npj Computational Materials, Vol 5, Iss 1, Pp 1-11 (2019)
The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine-learning algorithms to develop fast and
Publikováno v:
npj Computational Materials, Vol 5, Iss 1, Pp 1-8 (2019)
Multifunctional polarization controlling plays an important role in modern photonics, but their designs toward broad bandwidths and high efficiencies are still rather challenging. Here, by applying the inverse design method of model-based theoretical
Publikováno v:
npj Computational Materials, Vol 5, Iss 1, Pp 1-8 (2019)
Various factors affect the interfacial thermal resistance (ITR) between two materials, making ITR prediction a high-dimensional mathematical problem. Machine learning is a cost-effective method to address this. Here, we report ITR predictive models b
Publikováno v:
npj Computational Materials, Vol 4, Iss 1, Pp 1-7 (2018)
Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing p
Autor:
Pengcheng Zhai, Sergey I. Morozov, Qingjie Zhang, Qi An, Guodong Li, William A. Goddard, G. Jeffrey Snyder, Bo Duan
Publikováno v:
npj Computational Materials, Vol 4, Iss 1, Pp 1-6 (2018)
Inorganic semiconductor α-Ag2S exhibits a metal-like ductile behavior at room temperature, but the origin of this high ductility has not been fully explored yet. Based on density function theory simulations on the intrinsic mechanical properties of
Autor:
Ying Zhang, Chen Ling
Publikováno v:
npj Computational Materials, Vol 4, Iss 1, Pp 1-8 (2018)
There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influenc
Autor:
Marco Fornari, Mark Asta, Demet Usanmaz, Jose J. Plata, Stefano Curtarolo, Maarten de Jong, Cormac Toher, Jesús Carrete, Marco Buongiorno Nardelli, Pinku Nath
Publikováno v:
npj Computational Materials, Vol 3, Iss 1, Pp 1-10 (2017)
One of the most accurate approaches for calculating lattice thermal conductivity, $$\kappa _\ell$$ DMPSID=1 , is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximatio