Publications

Preprints

• K. Sakakibara, D. M. Packwood. Discrete Choice Models, Market Shares, and Density Functional Theory: Application to Monolayer Nanomaterials. arXiv:2211.09952

Papers since 2015

• C. Wechwithayakhlung, G. R. Weal, Y. Kaneko, P. A. Hume, J. M. Hodgkiss, D. M. Packwood. Exciton diffusion in amorphous organic semiconductors: reducing simulation overheads with machine learning. J. Chem. Phys. Accepted (2023)

• D. M. Packwood, Y. Kaneko, D. Ikeda, M. Ohno. An intelligent, user-inclusive pipeline for organic semiconductor design. Adv. Theory Simul. Accepted (2023).

• D. M. Packwood. Bi-Functional On-Surface Molecular Assemblies Predicted From a Multifaceted Computational Approach. Adv. Physics. Res. 1, 2022, 2200019.

• D. M. Packwood, L. T. H. Nguyen, P. Cesana, G. Zhang, A. Staykov, Y. Fukumoto, D. H. Nguyen. Machine Learning in Materials Chemistry: An Invitation. Machine Learning with Applications 8, 2022, 100265

• G. Ado, N. Noda, H. T. Vu, A. Perron, A. D. Mahapatra, K. P. Arista, H. Yoshimura, D. M. Packwood, F. Ishidate, S. Sato, T. Ozawa, M. Uesugi. Discovery of a phase-separating small molecule that selectively sequesters tubulin in cells. Chem. Sci. 13, 2022, 5760.

• C. Kaiyasuan, V. Somjit, B. Boekfa, D. M. Packwood, P. Chasing, T. Sudyoadsuk, K. Kongpatpanich, V. Promarak. Intrinsic hole mobility in luminescent metal-organic frameworks and its application in organic light-emitting diodes. Angew. Chem. Int. Ed. 134, 2022, e202117608.

• K. Kadota, Y. L. Hong, Y. Nishiyama, Y. Nishiyama, E. Sivaniah, D. M. Packwood. S. Horike. One-pot, room-temperature conversion of CO2 into porous metal-organic frameworks. J. Am. Chem. Soc. 40, 2021, 16750 

• M. Maruoka, P. Zhang, H. Mori, E. Imanishi, D. M. Packwood, H. Harada, H. Kosako, J. Suzuki. Caspase cleavage releases a nuclear protein fragment that stimulates phospholipid scrambling at the plasma membrane. Mol. Cell. 81, 2021, 1397.

• C. Wechwithayakhlung, D. M. Packwood, D. J. Harding, P. Pattanasattayavong. Structures, bonding, and electronic properties of metalthiocyanates. J. Phys. Chem. Solids. 154, 2021, 110085

• S. Jin, H. T. Vu, K. Hioki, N. Noda, H. Yoshida, T. Shimane, S. Ishizuka, I. Takashima, Y. Mizuhata, K. B. Pe, T. Ogawa, D. M. Packwood, N. Tokito, H. Kurata, S. Yamasaki, K. J. Ishii, M. Uesugi. Discovery of self-assembling small molecules as vaccine adjuvants. Angew. Chem. Int. Ed.  60, 2021, 961.

• D. M. Packwood and P. Pattanasattayavong. Disorder-robust bands from anisotropic orbitals in a coordination polymer semiconductor. J. Phys. Condens. Matter. 32, 2020, 275701.

• D. M. Packwood. Exploring the configuration spaces of surface materials using time-dependent diffraction patterns and unsupervised learning. Sci. Rep. 10, 2020, 5868.

• T. Higashino, Y. Kurumisawa, A.B. Alemayehu, R. F. Einrem, D. Sahu, D. M. Packwood, K. Kato, A. Yamakata, A. Ghosh, H. Imahori. Heavy metal effects on the photovoltaic properties of metallocorroles in dye-sensitized solar cells. ACS Appl. Energy. Mater. 3, 2020, 12460.

• P. Worakajit, F. Hamada, D. Sahu, P. Kidkhunthod, P. T. Sudyoadsuk, V. Promarak, D. J. Harding, D. M. Packwood, A. Saeki, P. Pattanasattayavong. Elucidating the coordination of diethyl sulfide molecules in copper(I) thiocyanate (CuSCN) thin films and improving hole transport by antisolvent treatment. Adv. Funct. Mater. 30, 2020, 2002355

• Y. Tokuda, M. Fujisawa, D. M. Packwood, M. Kambayashi, Y. Ueda. Data-driven design of glasses with desirable optical properties using statistical regression. AIP Adv. 10, 2020, 105110

• Y. Miyazaki, R. Nakayama, N. Yasuo, Y. Watanabe, R. Shimizu, D. M. Packwood, K. Nishio, Y. Ando, M. Sekijima, T. Hitosugi. Bayesian statistics-based analysis of AC impedance spectra. AIP Adv. 10, 2020, 045231

• P. Pattanasattayavong, D. M. Packwood, and D. J. Harding. Structural versatility and electronic structures of copper(I) thiocyanate (CuSCN)-ligand complexes. J. Mater. Chem. C. 7, 2019, 12907

• C. Wechwithayakhlung, D. M. Packwood, J. Chaopaknam, P. Worakajit, S. Ittisanronnachai, N. Chanlek, V. Promarak, K. Kongpatpanich, D. J. Harding, and P. Pattanasattayavong. Tin(II) Thiocyanate Sn(NSC)2 - a Wide Band Gap Coordination Polymer Semiconductor with 2D Structure. J. Mater. Chem. C. 7, 2019, 3452.

• D. M. Packwood and T. Hitosugi. Material informatics for self-assembly of functionalized organic precursors on metal surfaces. Nat. Commun. 9, 2018, 2469.

• X. Li and D. M. Packwood. Substrate-molecule decoupling induced by molecular self-assembly - implications for graphene nanoribbon fabrication. AIP Adv. 8, 2018, 045117.

• G. Zhang, M. Tsujimoto, D. M. Packwood, N. T. Duong, Y. Nishiyama, K. Kadota, S. Kitagawa, and S. Horike. Construction of a Hierarchical Architecture of Covalent Organic Frameworks via a Postsynthetic Approach. J. Am. Chem. Soc. 140, 2018, 2602.

• D. M. Packwood and T. Hitosugi. Rapid prediction of molecule arrangements on metal surfaces via Bayesian optimization. Appl. Phys. Express. 10, 2017, 065502

• D. M. Packwood, P. Han, and T. Hitosugi. Chemical and Entropic Control of the Molecular Self-Assembly Process. Nat .Commum. 8, 2017, 14463

• T. Higashino, Y. Kurumisawa, N. Cai, Y. Fujimori, Y. Tsuji, S. Nimura, D. M. Packwood, J. Park, and H. Imahori. A hydroxamic acid anchoring group for durable dye-sensitized solar cells incorporating a cobalt redox shuttle. ChemSusChem 10, 2017, 3347

• D. M. Packwood, P. Han, and T. Hitosugi. State Space Reduction and Equivalence Class Sampling of a Molecular Self-Assembly Model. Roy. Soc. Open. Sci. 3, 2016, 150681

• D. M. Packwood, H. G. Katzgraber, and W. Teizer. Stochastic Boltzmann Equation for Magnetic Relaxation in High-Spin Molecules. Proc. Roy. Soc. A. 472, 2016, 20150699

• D. M. Packwood, K. Akagi, and M. Umetsu. Identification of Peptide Adsorbates for Strong Nanoparticle-Nanoparticle Binding by Lattice Protein Simulations. Materials Discovery. 1, 2015, 2

• D. M. Packwood, K. Oniwa, T. Jin, and N. Asao. Charge Transport in Organic Crystals: Crucial Role of Correlated Fluctuations Unveiled by Analysis of Feynman Diagrams. J. Chem. Phys. 142, 2015, 144503

Book chapter

• D. M. Packwood. Machine Learning and Monte Carlo Methods for Surface-Assisted Molecular Self-Assembly. In Cell-Inspired Materials and Engineering (Eds. D. O. Wang and D. M. Packwood). Springer Fundamental Biomedical Technologies Series. Springer (2021)

Books

• D. O. Wang and D. M. Packwood (Editors). Cell-Inspired Materials and Engineering. Springer Fundamental Biomedical Technologies Series. Springer (2021)

• D. M. Packwood. Bayesian Optimization for Materials Science. SpringerBriefs in the Mathematics of Materials (volume 3).Springer (2017)

Magazine articles

• D. M. Packwood. Nanomaterial design platform based on computation and machine learning. Kagakukougyou 71, 2020, 46 (in Japanese)

• D. M. Packwood. Kernelized machine learning for a molecular self-assembly model. Bull. Jpn. Soc. Coord. Chem. 74, 2019, 62

• D. M. Packwood. Structure prediction for bottom-up graphene nanoribbon assembly. Chem. NZ. 84, 2018, 182

• D. M. Packwood and T. Hitosugi. Prediction of the graphene nanoribbon formation process with a mathematical model - the unintuitive effect of entropy. Kagaku 72, 2017, 29 (in Japanese)