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Terayama K, Osaki Y, Fujita T, Tamura R, Naito M, Tsuda K, Matsui T, Sumita M. Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules. J Chem Theory Comput 2023; 19:6770-6781. [PMID: 37729470 DOI: 10.1021/acs.jctc.3c00764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans' theorem. Our results demonstrate the effectiveness of the BO in optimizing functional parameters. Particularly, Koopmans' theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to the experimental UV-absorption values. Furthermore, we prepared an optimized parameter dataset of KTLC-BLYP for over 3000 molecules through BO for satisfying Koopmans' theorem. We have developed a machine learning model on this dataset to predict the parameters of the LC-BLYP functional for a given molecule. The prediction model automatically predicts the appropriate parameters for a given molecule and calculates the corresponding values. The approach in this paper would be useful to develop new functionals and to update the previously developed functionals.
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Affiliation(s)
- Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku Kanagawa 230-0045, Japan
- Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- MDX Research Center for Element Strategy, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
| | - Yamato Osaki
- Department of Chemistry, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Takehiro Fujita
- Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
| | - Ryo Tamura
- Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Masanobu Naito
- Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
| | - Koji Tsuda
- Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
| | - Toru Matsui
- Department of Chemistry, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Masato Sumita
- Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Yoshizawa T, Ishida S, Sato T, Ohta M, Honma T, Terayama K. Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. J Chem Inf Model 2022; 62:5351-5360. [DOI: 10.1021/acs.jcim.2c00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Tatsuya Yoshizawa
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
| | - Tomohiro Sato
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama230-0045, Japan
| | - Teruki Honma
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama230-0045, Japan
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Sumita M, Terayama K, Tamura R, Tsuda K. QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization. J Chem Inf Model 2022; 62:4427-4434. [PMID: 36074116 PMCID: PMC9518232 DOI: 10.1021/acs.jcim.2c00812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Indexed: 11/29/2022]
Abstract
To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation.
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Affiliation(s)
- Masato Sumita
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba 305-0044, Japan
| | - Kei Terayama
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Ryo Tamura
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba 305-0044, Japan
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba 305-0047, Japan
| | - Koji Tsuda
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba 305-0047, Japan
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