26 results on '"Koda, D."'
Search Results
2. Low molecular weight ‘liquid’ polymer extended compounds, impact on free volume and crosslink density studied by positron lifetime spectroscopy and stress-strain analysis according to Mooney-Rivlin
- Author
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Gruendken, M., Koda, D., Dryzek, J., and Blume, A.
- Published
- 2021
- Full Text
- View/download PDF
3. Silane-modified low molecular weight ‘liquid’ polymers in sulfur cured mixtures of styrene-butadiene copolymers and silica
- Author
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Gruendken, M., Velencoso, M.M., Koda, D., and Blume, A.
- Published
- 2021
- Full Text
- View/download PDF
4. Performance improvement of the μ10 microwave discharge ion thruster by expansion of the plasma production volume
- Author
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Tani, Y., Tsukizaki, R., Koda, D., Nishiyama, K., and Kuninaka, H.
- Published
- 2019
- Full Text
- View/download PDF
5. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
- Author
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Peng, J, Schwalbe-Koda, D, Akkiraju, K, Xie, T, Giordano, L, Yu, Y, John Eom, C, Lunger, J, Zheng, D, Rao, R, Muy, S, Grossman, J, Reuter, K, Gómez-Bombarelli &, R, Shao-Horn, Y, Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez-Bombarelli &, Yang Shao-Horn, Peng, J, Schwalbe-Koda, D, Akkiraju, K, Xie, T, Giordano, L, Yu, Y, John Eom, C, Lunger, J, Zheng, D, Rao, R, Muy, S, Grossman, J, Reuter, K, Gómez-Bombarelli &, R, Shao-Horn, Y, Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez-Bombarelli &, and Yang Shao-Horn
- Abstract
Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.
- Published
- 2022
6. Generative Models for Automatic Chemical Design
- Author
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Schwalbe-Koda, D, Gómez-Bombarelli, R, Schwalbe-Koda, D, and Gómez-Bombarelli, R
- Abstract
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care, and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.
- Published
- 2022
7. Study on charge neutralization effect by electron cyclotron resonance plasma source in high vacuum
- Author
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Morishita, T, primary, Koda, D, additional, Hosoda, S, additional, Mogami, T, additional, Minemura, K, additional, Nomura, N, additional, and Kuninaka, H, additional
- Published
- 2019
- Full Text
- View/download PDF
8. Numerical investigation of plasma properties for the microwave discharge ion thruster μ10 using PIC-MCC simulation
- Author
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Yamashita, Y., primary, Tani, Y., additional, Tsukizaki, R., additional, Koda, D., additional, and Nishiyama, K., additional
- Published
- 2019
- Full Text
- View/download PDF
9. Approximate quasiparticle correction for calculations of the energy gap in two-dimensional materials
- Author
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Guilhon, I., primary, Koda, D. S., additional, Ferreira, L. G., additional, Marques, M., additional, and Teles, L. K., additional
- Published
- 2018
- Full Text
- View/download PDF
10. Liquid rubber uitility in tire formulation
- Author
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Motoda S., IRC 2016, The Society of Rubber Science and Technology, Japan, Kitakyushu, Japan, 24-28 Oct. 2016, Hirata K., Koda D., Kuwahara S., Moriguchi N., Sasaki H., Motoda S., IRC 2016, The Society of Rubber Science and Technology, Japan, Kitakyushu, Japan, 24-28 Oct. 2016, Hirata K., Koda D., Kuwahara S., Moriguchi N., and Sasaki H.
- Abstract
Kuraray has developed a series of liquid rubber products with molecular weights ranging from a few thousands to a hundred thousand. These polymers, which consist of isoprene, butadiene, styrene and a new, bio-based monomer “Farnesene”, can be used by rubber manufacturers to achieve improvements in processing and physical properties. The most valuable physical property enhancement liquid butadiene rubber offers is a performance improvement in wet and ice grip, as well as plasticizing effect. Moreover, liquid Farnesene rubber contributes to advantaged vehicle fuel economy through reduction in tire rolling resistance. The application of each liquid rubber product in tire formulations is explained., Kuraray has developed a series of liquid rubber products with molecular weights ranging from a few thousands to a hundred thousand. These polymers, which consist of isoprene, butadiene, styrene and a new, bio-based monomer “Farnesene”, can be used by rubber manufacturers to achieve improvements in processing and physical properties. The most valuable physical property enhancement liquid butadiene rubber offers is a performance improvement in wet and ice grip, as well as plasticizing effect. Moreover, liquid Farnesene rubber contributes to advantaged vehicle fuel economy through reduction in tire rolling resistance. The application of each liquid rubber product in tire formulations is explained.
- Published
- 2016
11. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
- Author
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Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez-Bombarelli, Yang Shao-Horn, Peng, J, Schwalbe-Koda, D, Akkiraju, K, Xie, T, Giordano, L, Yu, Y, John Eom, C, Lunger, J, Zheng, D, Rao, R, Muy, S, Grossman, J, Reuter, K, Gómez-Bombarelli &, R, and Shao-Horn, Y
- Subjects
transition-metal oxides ,quantum-chemistry ,catalytic-activity ,surface science ,reduction activity ,Surfaces, Coatings and Films ,Electronic, Optical and Magnetic Materials ,Biomaterials ,oxygen evolution reaction ,Machine learning, materials, catalysis ,Materials Chemistry ,organic photovoltaics ,inorganic crystals ,ammonia-synthesis ,scaling relations ,Energy (miscellaneous) - Abstract
Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design., Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.
- Published
- 2022
12. One-Pot Synthesis of CHA/ERI-Type Zeolite Intergrowth from a Single Multiselective Organic Structure-Directing Agent.
- Author
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Kwon S, Bello-Jurado E, Ikonnikova E, Lee H, Schwalbe-Koda D, Corma A, Willhammar T, Olivetti EA, Gomez-Bombarelli R, Moliner M, and Román-Leshkov Y
- Abstract
We report the one-pot synthesis of a chabazite (CHA)/erionite (ERI)-type zeolite intergrowth structure characterized by adjustable extents of intergrowth enrichment and Si/Al molar ratios. This method utilizes readily synthesizable 6-azaspiro[5.6]dodecan-6-ium as the exclusive organic structure-directing agent (OSDA) within a potassium-dominant environment. High-throughput simulations were used to accurately determine the templating energy and molecular shape, facilitating the selection of an optimally biselective OSDA from among thousands of prospective candidates. The coexistence of the crystal phases, forming a distinct structure comprising disk-like CHA regions bridged by ERI-rich pillars, was corroborated via rigorous powder X-ray diffraction and integrated differential-phase contrast scanning transmission electron microscopy (iDPC S/TEM) analyses. iDPC S/TEM imaging further revealed the presence of single offretite layers dispersed within the ERI phase. The ratio of crystal phases between CHA and ERI in this type of intergrowth could be varied systematically by changing both the OSDA/Si and K/Si ratios. Two intergrown zeolite samples with different Si/Al molar ratios were tested for the selective catalytic reduction (SCR) of NO
x with NH3 , showing competitive catalytic performance and hydrothermal stability compared to that of the industry-standard commercial NH3 -SCR catalyst, Cu-SSZ-13, prevalent in automotive applications. Collectively, this work underscores the potential of our approach for the synthesis and optimization of adjustable intergrown zeolite structures, offering competitive alternatives for key industrial processes.- Published
- 2024
- Full Text
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13. Specific inhibitory effects of guanosine on breast cancer cell proliferation.
- Author
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Takizawa Y, Kizawa M, Niwa N, Komura Y, Takahashi M, Koda D, Kurita T, and Nakajima T
- Subjects
- Humans, Female, Guanosine pharmacology, Mycophenolic Acid pharmacology, Cell Proliferation, MCF-7 Cells, Guanosine Triphosphate metabolism, Adenosine Triphosphate pharmacology, Breast Neoplasms drug therapy, Antineoplastic Agents pharmacology
- Abstract
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Drug therapy for breast cancer is currently selected based on the subtype classification; however, many anticancer drugs are highly cytotoxic. Since intracellular levels of GTP are elevated in many cancer cells that undergo a specific cell proliferation cycle, GTP has potential as a target for cancer therapy. The present study focused on nucleosides and nucleotides and examined intracellular GTP-dependent changes in cell proliferation rates in normal (MCF-12A) and cancer (MCF-7) breast cell lines. Decreased cell proliferation due to a reduction in intracellular GTP levels by mycophenolic acid (MPA), an inosine monophosphate dehydrogenase inhibitor, was observed in both cell lines. The inhibitory effects of MPA on cell proliferation were suppressed when it was applied in combination with Guanosine (Guo), a substrate for GTP salvage synthesis, while the single exposure to Guo suppressed the proliferation of MCF-7 cells only. Although the underlying mechanisms remain unclear, since the inhibitory effects of Guo on cell proliferation did not correlate with GTP or ATP intracellular levels or the GTP/ATP ratio, there may be another cause besides GTP metabolism. Guo inhibited the proliferation of MCF-7, a human breast cancer cell line, but not MCF-12A, a human normal breast cell line. Further studies are needed to investigate the potential of applying Guo as a target for the development of a novel cancer treatment system., Competing Interests: Declaration of competing interest The authors state that they have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)
- Published
- 2023
- Full Text
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14. Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others.
- Author
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Ferri P, Li C, Schwalbe-Koda D, Xie M, Moliner M, Gómez-Bombarelli R, Boronat M, and Corma A
- Abstract
Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl intermediates for the two competing reactions only differ in the number of ethyl substituents in the aromatic rings, and therefore finding a selective zeolite able to recognize this subtle difference requires an accurate balance of the stabilization of reaction intermediates and transition states inside the zeolite microporous voids. In this work we present a computational methodology that, by combining a fast high-throughput screeening of all zeolite structures able to stabilize the key intermediates with a more computationally demanding mechanistic study only on the most promising candidates, guides the selection of the zeolite structures to be synthesized. The methodology presented is validated experimentally and allows to go beyond the conventional criteria of zeolite shape-selectivity., (© 2023. The Author(s).)
- Published
- 2023
- Full Text
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15. Mechanistic Insights on Permeation of Water over Iron Cations in Nanoporous Silicon Oxide Films for Selective H 2 and O 2 Evolution.
- Author
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Aydin F, Andrade MFC, Stinson RS, Zagalskaya A, Schwalbe-Koda D, Chen Z, Sharma S, Maiti A, Esposito DV, Ardo S, Pham TA, and Ogitsu T
- Abstract
Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO
2 films. It is shown that even for SiO2 pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2 pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2 coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.- Published
- 2023
- Full Text
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16. Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx.
- Author
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Bello-Jurado E, Schwalbe-Koda D, Nero M, Paris C, Uusimäki T, Román-Leshkov Y, Corma A, Willhammar T, Gómez-Bombarelli R, and Moliner M
- Abstract
A novel ab initio methodology based on high-throughput simulations has permitted designing unique biselective organic structure-directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well-established commercial CHA-type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties., (© 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)
- Published
- 2022
- Full Text
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17. Data-Driven Design of Biselective Templates for Intergrowth Zeolites.
- Author
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Schwalbe-Koda D, Corma A, Román-Leshkov Y, Moliner M, and Gómez-Bombarelli R
- Abstract
Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective toward both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity toward the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.
- Published
- 2021
- Full Text
- View/download PDF
18. A priori control of zeolite phase competition and intergrowth with high-throughput simulations.
- Author
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Schwalbe-Koda D, Kwon S, Paris C, Bello-Jurado E, Jensen Z, Olivetti E, Willhammar T, Corma A, Román-Leshkov Y, Moliner M, and Gómez-Bombarelli R
- Abstract
Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this work, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from more than 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalized framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed us to realize an intergrowth zeolite through a single bi-selective template. The computation-first approach enables control of both zeolite synthesis and structure composition using a priori theoretical descriptors.
- Published
- 2021
- Full Text
- View/download PDF
19. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks.
- Author
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Schwalbe-Koda D, Tan AR, and Gómez-Bombarelli R
- Abstract
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system., (© 2021. The Author(s).)
- Published
- 2021
- Full Text
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20. Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.
- Author
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Jensen Z, Kwon S, Schwalbe-Koda D, Paris C, Gómez-Bombarelli R, Román-Leshkov Y, Corma A, Moliner M, and Olivetti EA
- Abstract
Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)
- Published
- 2021
- Full Text
- View/download PDF
21. Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites.
- Author
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Schwalbe-Koda D and Gómez-Bombarelli R
- Abstract
Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding toward a given zeolite framework. Over the last few decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and Dreiding force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that Dreiding binding energies from the frozen pose method correlate best with DFT energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive than their time-averaged counterparts. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over the frozen pose method despite being orders of magnitude more expensive. The code and benchmark data are open-sourced and provide robust and computationally efficient guidelines to calculating binding energies in zeolite-OSDA pairs.
- Published
- 2021
- Full Text
- View/download PDF
22. Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks.
- Author
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Ruza J, Wang W, Schwalbe-Koda D, Axelrod S, Harris WH, and Gómez-Bombarelli R
- Abstract
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.
- Published
- 2020
- Full Text
- View/download PDF
23. Graph similarity drives zeolite diffusionless transformations and intergrowth.
- Author
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Schwalbe-Koda D, Jensen Z, Olivetti E, and Gómez-Bombarelli R
- Abstract
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis
1-3 . Interzeolite transformations enable selective crystallization4-7 , but are often too complex to be designed by comparing crystal structures. Here, computational and theoretical tools are combined to both exhaustively data mine polymorphic transformations reported in the literature and analyse and explain interzeolite relations. It was found that crystallographic building units are weak predictors of topology interconversion and insufficient to explain intergrowth. By introducing a supercell-invariant metric that compares crystal structures using graph theory, we show that diffusionless (topotactic and reconstructive) transformations occur only between graph-similar pairs. Furthermore, all the known instances of intergrowth occur between either structurally similar or graph similar frameworks. We identify promising pairs to realize diffusionless transformations and intergrowth, with hundreds of low-distance pairs identified among known zeolites, and thousands of hypothetical frameworks connected to known zeolite counterparts. The theory may enable the understanding and control of zeolite polymorphism.- Published
- 2019
- Full Text
- View/download PDF
24. Cancer cell death induced by the intracellular self-assembly of an enzyme-responsive supramolecular gelator.
- Author
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Tanaka A, Fukuoka Y, Morimoto Y, Honjo T, Koda D, Goto M, and Maruyama T
- Subjects
- Cell Death drug effects, Cell Line, Tumor, Gels, Humans, Hydrolysis, Antineoplastic Agents chemistry, Antineoplastic Agents pharmacology, Intracellular Space metabolism, Matrix Metalloproteinase 7 metabolism
- Abstract
We report cancer cell death initiated by the intracellular molecular self-assembly of a peptide lipid, which was derived from a gelator precursor. The gelator precursor was designed to form nanofibers via molecular self-assembly, after cleavage by a cancer-related enzyme (matrix metalloproteinase-7, MMP-7), leading to hydrogelation. The gelator precursor exhibited remarkable cytotoxicity to five different cancer cell lines, while the precursor exhibited low cytotoxicity to normal cells. Cancer cells secrete excessive amounts of MMP-7, which converted the precursor into a supramolecular gelator prior to its uptake by the cells. Once inside the cells, the supramolecular gelator formed a gel via molecular self-assembly, exerting vital stress on the cancer cells. The present study thus describes a new drug where molecular self-assembly acts as the mechanism of cytotoxicity.
- Published
- 2015
- Full Text
- View/download PDF
25. Proteinase-mediated drastic morphological change of peptide-amphiphile to induce supramolecular hydrogelation.
- Author
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Koda D, Maruyama T, Minakuchi N, Nakashima K, and Goto M
- Subjects
- Amino Acid Sequence, Matrix Metalloproteinase 7 metabolism, Micelles, Nanofibers chemistry, Palmitates chemistry, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Water chemistry, Matrix Metalloproteinase 7 chemistry, Peptides chemistry
- Abstract
We report a novel peptide-amphiphile having a simple molecular structure that can gelate an aqueous solution at a remarkably low concentration and can be designed to be responsive to a disease-related enzyme by undergoing a drastic morphological change.
- Published
- 2010
- Full Text
- View/download PDF
26. Enzyme encapsulation in microparticles composed of polymerized ionic liquids for highly active and reusable biocatalysts.
- Author
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Nakashima K, Kamiya N, Koda D, Maruyama T, and Goto M
- Subjects
- Acrylamides, Cross-Linking Reagents, Oxidation-Reduction, Polyvinyls chemistry, Emulsions chemistry, Enzymes, Immobilized chemistry, Enzymes, Immobilized metabolism, Horseradish Peroxidase chemistry, Horseradish Peroxidase metabolism, Ionic Liquids chemistry
- Abstract
Horseradish peroxidase (HRP) is encapsulated in polymerized ionic liquid microparticles (pIL-MP), which are prepared by polymerization of 1-vinyl-3-ethylimidazolium bromide in the presence of the crosslinker N,N'-methylenebis(acrylamide) in a concentrated water-in-oil (W/O) emulsion. pIL-MP encapsulating HRP chemically-modified with comb-shaped polyethylene glycol (PM(13)-HRP) exhibit excellent activity for guaiacol oxidation in an aqueous solution. The PM(13)-HRP in pIL-MP shows more than 2-fold higher activity than that of the enzyme encapsulated in a polyacrylamide microparticle. The catalytic activity declines with an increase in the crosslinker concentration of the pIL-MP, probably due to suppression of substrate diffusion. The activity of PM(13)-HRP in pIL-MP depends on the external environment of the gel (i.e. pH and temperature). The pIL-MP are easily recovered from the reaction mixture by centrifugation, which makes it possible to recycle the biocatalyst for repeated oxidation reactions.
- Published
- 2009
- Full Text
- View/download PDF
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