With the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity (LTC) based on the Boltzmann transport theory becomes computationally feasible. However, steep computational costs in evaluating interatomic force constants limit the theoretical investigation of LTC to relatively simple crystals. Currently, machine learning potentials (MLPs) are garnering attention as an efficient surrogate model of DFT. However, the applicability of MLPs to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a variety of materials would be useful. Herein, we test a various methods of training set construction, and compute LTC of materials with diverse symmetries and a wide range of LTC using MLP. The current work will establish a robust framework for accurately computing LTC with MLPs. This work was published at Computational Materials Science 2022, 211, 111472
The machine learning potentials (MLPs) are garnering large attention, providing the accuracy of ab initio calculations at much lower costs. MLPs guarantee their reliability within the training domain. However, it requires expertise and enormous time to collect all necessary configurations by hand-picking. We suggest a sampling method via metadynamics accumulating on the local atomic environment space, which is called G-metaD. G-metaD is applied to H:Pt(111), GeTe, and Si systems. This work was published at npj Computational Materials 2021, 7, 131
Since there are many unknown stable materials with ternary or higher (multinary) composition, crystal structure prediction is necessary to accelerate the rate of material discovery. This demands fast and accurate evaluation of free energies in exploring a vast number of atomic configurations. The neural network potential (NNP) can meet this requirement but a scarcity of information on the crystal structure poses a challenge in choosing training sets. In this work, we propose a method of constructing training sets from density functional theory (DFT)–based dynamical trajectories of disordered structures, which does not require any preceding information on material structures except for the chemical composition. With this method, we find strong correlation of NNP and DFT energies, ensuring that the NNPs can properly rank energies among low-energy crystalline structures. We also find that the evolutionary search using the NNPs is more efficient than the DFT-based approach. This work was published at Physical Review B 2020, 102, 224104.
Ab initio calculations based on the density functional theory (DFT) become a vital tool in materials science for understanding and predicting material properties. However, it requires in-depth knowledge on underlying theories and enough experience to produce reliable data. Recently, several automation utilities have been developed to accelerate data production but they still assume that users are familiar with technical details. Here, we introduce a full-fledged automation code running a DFT program. The package requires only structure information from the user and provides a highly accurate band structure, band gap, effective mass, density of states and dielectric constant for the given structure. As a result, anyone can run DFT program without any background knowledge using the package. This work was published at Computer Physics Communications 2020, 256, 107450.
Despite the great potential of a-CuI:Sn as a new class of transparent p-type semiconductors, the fundamental understanding is still incomplete. To reveal the microscopic origin, structural and electrical properties of a-CuI are investigated. Despite the amorphous structure, states at valence band maximum are extended in linear ways due to the hybridization between I-5p state, explaining the high hole mobilities in the experiment. This work will help design new wide-band-gap p-type semiconductors. This work was published at Physica Status Solidi B, 257, 2000218.