scholarly journals Adjusting the descriptor for a crystal structure search using Bayesian optimization

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Nobuya Sato ◽  
Tomoki Yamashita ◽  
Tamio Oguchi ◽  
Koji Hukushima ◽  
Takashi Miyake
Keyword(s):  
Crystal Structure ◽  
Bayesian Optimization ◽  
Structure Search

scholarly journals Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

2020 ◽  
Author(s):  
Jari Järvi ◽  
Benjamin Alldritt ◽  
Ondřej Krejčí ◽  
Milica Todorovic ◽  
Peter Liljeroth ◽  
...  
Keyword(s):  
Image Features ◽  
Inorganic Materials ◽  
Bayesian Optimization ◽  
Molecular Adsorption ◽  
Detailed Knowledge ◽  
Automated Identification ◽  
Robust Identification ◽  
Force Microscopy ◽  
Structure Search ◽  
Experimental Image

Abstract Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In a fresh approach, we propose to integrate cross-disciplinary tools for a robust and automated identification of 3D adsorbate configurations. We employ Bayesian optimization with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow us to fingerprint adsorbate structures appearing in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, we found three matching AFM image contrasts, which allowed us to correlate experimental image features to distinct cases of molecular adsorption.

Exhaustive crystal structure search and crystal modeling of β-chitin

2007 ◽  
Vol 40 (4) ◽  
pp. 336-344 ◽  
Author(s):  
Toshifumi Yui ◽  
Naoto Taki ◽  
Junji Sugiyama ◽  
Sachio Hayashi
Keyword(s):  
Crystal Structure ◽  
Structure Search

scholarly journals Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

2021 ◽  
Author(s):  
Jari Järvi ◽  
Benjamin Alldritt ◽  
Ondřej Krejčí ◽  
Milica Todorović ◽  
Peter Liljeroth ◽  
...  
Keyword(s):  
Image Features ◽  
Inorganic Materials ◽  
Bayesian Optimization ◽  
Molecular Adsorption ◽  
Detailed Knowledge ◽  
Automated Identification ◽  
Robust Identification ◽  
Force Microscopy ◽  
Structure Search ◽  
Experimental Image

Abstract Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In a fresh approach, we propose to integrate cross-disciplinary tools for a robust and automated identification of 3D adsorbate configurations. We employ Bayesian optimization with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow us to fingerprint adsorbate structures appearing in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, we found three matching AFM image contrasts, which allowed us to correlate experimental image features to distinct cases of molecular adsorption.

scholarly journals Silicon clathrates for photovoltaics predicted by a two-step crystal structure search

2017 ◽  
Vol 111 (17) ◽  
pp. 173904 ◽  
Author(s):  
Juefei Wu ◽  
Hao Gao ◽  
Kang Xia ◽  
Dingyu Xing ◽  
Jian Sun
Keyword(s):  
Crystal Structure ◽  
Silicon Clathrates ◽  
Structure Search

scholarly journals Crystal structure prediction accelerated by Bayesian optimization

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Tomoki Yamashita ◽  
Nobuya Sato ◽  
Hiori Kino ◽  
Takashi Miyake ◽  
Koji Tsuda ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Bayesian Optimization ◽  
Crystal Structure Prediction

scholarly journals Crystal structure prediction by combining graph network and Bayesian optimization

2021 ◽  
Author(s):  
Guanjian Cheng ◽  
Xin-Gao Gong ◽  
Wan-Jian Yin
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Density Functional ◽  
Computational Cost ◽  
Formation Enthalpy ◽  
Bayesian Optimization ◽  
Chemical Compositions ◽  
Crystal Structure Prediction ◽  
Cell Shapes

Abstract We developed a density functional theory (DFT)-free approach for crystal structure prediction, in which a graph network (GN) is adopted to establish a correlation model between the crystal structure and formation enthalpies, and Bayesian optimization (BO) is used to accelerate the search for crystal structure with optimal formation enthalpy. The approach of combining GN and BO for crystal structure searching (GN-BOSS) can predict crystal structures at given chemical compositions with and without additional constraints on cell shapes and lattice symmetries. The applicability and efficiency of the GN-BOSS approach is then verified by solving the classical Ph-vV challenge. The approach can accurately predict the crystal structures with a computational cost that is three orders of magnitude less than that required for DFT-based approaches. The GN-BOSS approach may open new avenues for data-driven crystal structural predictions without using expensive DFT calculations.

scholarly journals Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

2020 ◽  
Vol 11 ◽  
pp. 1577-1589
Author(s):  
Jari Järvi ◽  
Patrick Rinke ◽  
Milica Todorović
Keyword(s):  
Density Functional ◽  
Complex Surface ◽  
Bayesian Optimization ◽  
Advanced Materials ◽  
Atomistic Simulations ◽  
Functional Theory ◽  
Molecular Adsorbates ◽  
Structure Search ◽  
Dimensional Phase Space ◽  
Microscopy Images

Identifying the atomic structure of organic–inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.

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