1
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Sato K, Hattori K, Uehara F, Kitaguni T, Nishiura T, Yamagata T, Nomura K, Matsumoto N, Tanaka T, Aihara H. A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices. Sci Rep 2024; 14:4336. [PMID: 38383699 PMCID: PMC10881559 DOI: 10.1038/s41598-024-54473-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 02/13/2024] [Indexed: 02/23/2024] Open
Abstract
Materials informatics in the development of organic light-emitting diode (OLED) related materials have been performed and exhibited the effectiveness for finding promising compounds with a desired property. However, the molecular structure optimization of the promising compounds through the conventional approach, namely the fine-tuning of molecules, still involves a significant amount of trial and error. This is because it is challenging to endow a single molecule with all the properties required for practical applications. The present work focused on fine-tuning triazine-based electron-transport materials using machine learning (ML) techniques. The prediction models based on localized datasets containing only triazine derivatives showed high prediction accuracy. The descriptors from density functional theory calculations enhanced the prediction of the glass transition temperature. The proposed multistep virtual screening approach extracted the promising triazine derivatives with the coexistence of higher electron mobility and glass transition temperature. Nine selected triazine compounds from 3,670,000 of the initial search space were synthesized and used as the electron transport layer for practical OLED devices. Their observed properties matched the predicted properties, and they enhanced the current efficiency and lifetime of the device. This paper provides a successful model for the ML assisted fine-tuning that effectively accelerates the development of practical materials.
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Affiliation(s)
- Kosuke Sato
- Sagami Chemical Research Institute, Ayase, Kanagawa, 252-1193, Japan.
| | - Kazuki Hattori
- Tokyo Research Center, Organic Materials Research Laboratory, Tosoh Corporation, Ayase, Kanagawa, 252-1123, Japan
| | - Fuminari Uehara
- Tokyo Research Center, Organic Materials Research Laboratory, Tosoh Corporation, Ayase, Kanagawa, 252-1123, Japan
| | - Tomoko Kitaguni
- Sagami Chemical Research Institute, Ayase, Kanagawa, 252-1193, Japan
| | - Toshiki Nishiura
- Sagami Chemical Research Institute, Ayase, Kanagawa, 252-1193, Japan
| | - Takuya Yamagata
- Sagami Chemical Research Institute, Ayase, Kanagawa, 252-1193, Japan
| | - Keisuke Nomura
- Tokyo Research Center, Organic Materials Research Laboratory, Tosoh Corporation, Ayase, Kanagawa, 252-1123, Japan
| | - Naoki Matsumoto
- Tokyo Research Center, Organic Materials Research Laboratory, Tosoh Corporation, Ayase, Kanagawa, 252-1123, Japan
| | - Tsuyoshi Tanaka
- Tokyo Research Center, Organic Materials Research Laboratory, Tosoh Corporation, Ayase, Kanagawa, 252-1123, Japan
| | - Hidenori Aihara
- Sagami Chemical Research Institute, Ayase, Kanagawa, 252-1193, Japan
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2
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Beran GJO. Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials. Chem Sci 2023; 14:13290-13312. [PMID: 38033897 PMCID: PMC10685338 DOI: 10.1039/d3sc03903j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.
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Affiliation(s)
- Gregory J O Beran
- Department of Chemistry, University of California Riverside Riverside CA 92521 USA
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3
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Wang B, Hilleke KP, Hajinazar S, Frapper G, Zurek E. Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases. J Chem Theory Comput 2023; 19:7960-7971. [PMID: 37856841 DOI: 10.1021/acs.jctc.3c00594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure's fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features and interface this method with the XtalOpt evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g, the coordination number or chemical environment), and symmetry (e.g, Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN8, with a two-dimensional polymeric nitrogen sublattice, brookite TiO2, and a high pressure BaH4 phase, which was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1̅ WC3N6, was found to possess an energy that is lower than that of two phases proposed in a recent computational study. The method presented here could help in identifying the structures of compounds that have already been synthesized, and in developing new synthesis targets with desired properties.
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Affiliation(s)
- Busheng Wang
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Katerina P Hilleke
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Samad Hajinazar
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Gilles Frapper
- Applied Quantum Chemistry Group, E4 Team, IC2MP UMR 7285, Université de Poitiers, CNRS, Poitiers 86073, France
| | - Eva Zurek
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
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4
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Hashemi A, Bougueroua S, Gaigeot MP, Pidko EA. HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts. J Chem Inf Model 2023; 63:6081-6094. [PMID: 37738303 PMCID: PMC10565810 DOI: 10.1021/acs.jcim.3c00660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Indexed: 09/24/2023]
Abstract
A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.
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Affiliation(s)
- Ali Hashemi
- Inorganic
Systems Engineering, Department of Chemical Engineering, Faculty of
Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Sana Bougueroua
- Laboratoire
Analyse et Modélisation pour la Biologie et l’Environnement
(LAMBE) UMR8587, Paris-Saclay, Univ Evry,
CY Cergy Paris Université, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Marie-Pierre Gaigeot
- Laboratoire
Analyse et Modélisation pour la Biologie et l’Environnement
(LAMBE) UMR8587, Paris-Saclay, Univ Evry,
CY Cergy Paris Université, CNRS, LAMBE UMR8587, Evry-Courcouronnes 91025, France
| | - Evgeny A. Pidko
- Inorganic
Systems Engineering, Department of Chemical Engineering, Faculty of
Applied Sciences, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
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5
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Bayse CA. Stack bonding in polyaromatic hydrocarbons. Phys Chem Chem Phys 2023. [PMID: 37466927 DOI: 10.1039/d3cp02553e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Parallel displacement of π-stacked component molecules enhances the efficiency of organic semiconductors by maximizing interpenetration of the π-densities. Dimers of symmetric polyaromatic hydrocarbons coronene, hexabenzo[bc,de,gh,kl,no,qr]coronene, circumcoronene, kekulene, and circumcircumcoronene are examined using density functional theory from the stack bonding perspective which considers π-stacking interactions in terms of contributions of monomer π-orbital overlap to the character of dimer orbitals. Energetically favored parallel displaced and/or twisted dimer conformations are consistent with patterns of mixing of the monomer molecular orbitals (MOs) that maximize interpenetration of the π densities. The multiple minima found along parallel displacement (PD) coordinates coincide with the formation of dimer MOs formally antibonding between the monomers at the sandwich conformation to bonding at the PD minima. Minima identified with favorable stack bonding are consistent with polymorphs found in large polyaromatic hydrocarbons.
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Affiliation(s)
- Craig A Bayse
- Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, Virginia 23529, USA.
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6
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Shim E, Tewari A, Cernak T, Zimmerman PM. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J Chem Inf Model 2023; 63:3659-3668. [PMID: 37312524 PMCID: PMC11163943 DOI: 10.1021/acs.jcim.3c00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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7
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Tom R, Gao S, Yang Y, Zhao K, Bier I, Buchanan EA, Zaykov A, Havlas Z, Michl J, Marom N. Inverse Design of Tetracene Polymorphs with Enhanced Singlet Fission Performance by Property-Based Genetic Algorithm Optimization. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:1373-1386. [PMID: 36999121 PMCID: PMC10042130 DOI: 10.1021/acs.chemmater.2c03444] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/06/2023] [Indexed: 06/19/2023]
Abstract
The efficiency of solar cells may be improved by using singlet fission (SF), in which one singlet exciton splits into two triplet excitons. SF occurs in molecular crystals. A molecule may crystallize in more than one form, a phenomenon known as polymorphism. Crystal structure may affect SF performance. In the common form of tetracene, SF is experimentally known to be slightly endoergic. A second, metastable polymorph of tetracene has been found to exhibit better SF performance. Here, we conduct inverse design of the crystal packing of tetracene using a genetic algorithm (GA) with a fitness function tailored to simultaneously optimize the SF rate and the lattice energy. The property-based GA successfully generates more structures predicted to have higher SF rates and provides insight into packing motifs associated with improved SF performance. We find a putative polymorph predicted to have superior SF performance to the two forms of tetracene, whose structures have been determined experimentally. The putative structure has a lattice energy within 1.5 kJ/mol of the most stable common form of tetracene.
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Affiliation(s)
- Rithwik Tom
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Siyu Gao
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Yi Yang
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Kaiji Zhao
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Imanuel Bier
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Eric A. Buchanan
- Department
of Chemistry, University of Colorado, Boulder, Colorado80309, United States
| | - Alexandr Zaykov
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
- Department
of Physical Chemistry, University of Chemistry
and Technology, 166 28Prague 6, Czech Republic
| | - Zdeněk Havlas
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
| | - Josef Michl
- Department
of Chemistry, University of Colorado, Boulder, Colorado80309, United States
- Institute
of Organic Chemistry and Biochemistry, Czech
Academy of Sciences, 16610Prague 6, Czech
Republic
| | - Noa Marom
- Department
of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
- Department
of Materials Science and Engineering, Carnegie
Mellon University, Pittsburgh, Pennsylvania15213, United States
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
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8
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Cook CJ, Li W, Lui BF, Gately TJ, Al-Kaysi RO, Mueller LJ, Bardeen CJ, Beran GJO. A theoretical framework for the design of molecular crystal engines. Chem Sci 2023; 14:937-949. [PMID: 36755715 PMCID: PMC9890974 DOI: 10.1039/d2sc05549j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Photomechanical molecular crystals have garnered attention for their ability to transform light into mechanical work, but difficulties in characterizing the structural changes and mechanical responses experimentally have hindered the development of practical organic crystal engines. This study proposes a new computational framework for predicting the solid-state crystal-to-crystal photochemical transformations entirely from first principles, and it establishes a photomechanical engine cycle that quantifies the anisotropic mechanical performance resulting from the transformation. The approach relies on crystal structure prediction, solid-state topochemical principles, and high-quality electronic structure methods. After validating the framework on the well-studied [4 + 4] cycloadditions in 9-methyl anthracene and 9-tert-butyl anthracene ester, the experimentally-unknown solid-state transformation of 9-carboxylic acid anthracene is predicted for the first time. The results illustrate how the mechanical work is done by relaxation of the crystal lattice to accommodate the photoproduct, rather than by the photochemistry itself. The large ∼107 J m-3 work densities computed for all three systems highlight the promise of photomechanical crystal engines. This study demonstrates the importance of crystal packing in determining molecular crystal engine performance and provides tools and insights to design improved materials in silico.
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Affiliation(s)
- Cameron J. Cook
- Department of Chemistry, University of California RiversideRiverside CA 92521USA
| | - Wangxiang Li
- Department of Chemistry, University of California Riverside Riverside CA 92521 USA
| | - Brandon F. Lui
- Department of Chemistry, University of California RiversideRiverside CA 92521USA
| | - Thomas J. Gately
- Department of Chemistry, University of California RiversideRiverside CA 92521USA
| | - Rabih O. Al-Kaysi
- College of Science and Health Professions-3124, King Saud Bin Abdulaziz University for Health Sciences, and King Abdullah International Medical Research Center, Ministry of National Guard Health AffairsRiyadh 11426Kingdom of Saudi Arabia
| | - Leonard J. Mueller
- Department of Chemistry, University of California RiversideRiverside CA 92521USA
| | | | - Gregory J. O. Beran
- Department of Chemistry, University of California RiversideRiverside CA 92521USA
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9
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Global analysis of the energy landscapes of molecular crystal structures by applying the threshold algorithm. Commun Chem 2022; 5:86. [PMID: 36697680 PMCID: PMC9814927 DOI: 10.1038/s42004-022-00705-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/15/2022] [Indexed: 01/28/2023] Open
Abstract
Polymorphism in molecular crystals has important consequences for the control of materials properties and our understanding of crystallization. Computational methods, including crystal structure prediction, have provided important insight into polymorphism, but have usually been limited to assessing the relative energies of structures. We describe the implementation of the Monte Carlo threshold algorithm as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes and provide valuable information on the depth of the energy minima associated with crystal structures. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals.
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10
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Kalikadien AV, Pidko EA, Sinha V. ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold. DIGITAL DISCOVERY 2022; 1:8-25. [PMID: 35340336 PMCID: PMC8887922 DOI: 10.1039/d1dd00017a] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO–LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery. This work introduces ChemSpaX, an open-source Python-based tool for automated exploration of chemical space of molecular scaffolds with a special focus on transition-metal complexes.![]()
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Affiliation(s)
- Adarsh V Kalikadien
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
| | - Vivek Sinha
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9 2629 HZ Delft The Netherlands
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11
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Omar ÖH, Del Cueto M, Nematiaram T, Troisi A. High-throughput virtual screening for organic electronics: a comparative study of alternative strategies. JOURNAL OF MATERIALS CHEMISTRY. C 2021; 9:13557-13583. [PMID: 34745630 PMCID: PMC8515942 DOI: 10.1039/d1tc03256a] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/13/2021] [Indexed: 06/01/2023]
Abstract
We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.
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Affiliation(s)
- Ömer H Omar
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | - Marcos Del Cueto
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
| | | | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
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12
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Ai Q, Bhat V, Ryno SM, Jarolimek K, Sornberger P, Smith A, Haley MM, Anthony JE, Risko C. OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors. J Chem Phys 2021; 154:174705. [PMID: 34241085 DOI: 10.1063/5.0048714] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu.
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Affiliation(s)
- Qianxiang Ai
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Vinayak Bhat
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Sean M Ryno
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Karol Jarolimek
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Parker Sornberger
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Andrew Smith
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Michael M Haley
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403-1253, USA
| | - John E Anthony
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
| | - Chad Risko
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, USA
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13
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Kunkel C, Margraf JT, Chen K, Oberhofer H, Reuter K. Active discovery of organic semiconductors. Nat Commun 2021; 12:2422. [PMID: 33893287 PMCID: PMC8065160 DOI: 10.1038/s41467-021-22611-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 03/15/2021] [Indexed: 01/16/2023] Open
Abstract
The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.
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Affiliation(s)
- Christian Kunkel
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Johannes T Margraf
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Ke Chen
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Harald Oberhofer
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Karsten Reuter
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany.
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany.
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14
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Zhao C, Chen L, Che Y, Pang Z, Wu X, Lu Y, Liu H, Day GM, Cooper AI. Digital navigation of energy-structure-function maps for hydrogen-bonded porous molecular crystals. Nat Commun 2021; 12:817. [PMID: 33547307 PMCID: PMC7865007 DOI: 10.1038/s41467-021-21091-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/12/2021] [Indexed: 11/24/2022] Open
Abstract
Energy-structure-function (ESF) maps can aid the targeted discovery of porous molecular crystals by predicting the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of rigid molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. We show that the positioning of the hydrogen-bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps, revealing promising structure-function spaces for future experiments. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map, enabling an efficient navigation of the ESF data to identify 'landmark' structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, an important goal for closed-loop, autonomous searches for molecular crystals with useful functions.
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Affiliation(s)
- Chengxi Zhao
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Linjiang Chen
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China.
| | - Yu Che
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Zhongfu Pang
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
| | - Xiaofeng Wu
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Yunxiang Lu
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Honglai Liu
- Key Laboratory for Advanced Materials and School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, UK.
| | - Andrew I Cooper
- Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China.
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15
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Woodley SM, Day GM, Catlow R. Structure prediction of crystals, surfaces and nanoparticles. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190600. [PMID: 33100162 DOI: 10.1098/rsta.2019.0600] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'.
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Affiliation(s)
- Scott M Woodley
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton SO17 1BJ, UK
| | - R Catlow
- Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
- School of Chemistry, Cardiff University, Park Place, Cardiff CF10 3AT, UK
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16
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Chalek KR, Dong X, Tong F, Kudla RA, Zhu L, Gill AD, Xu W, Yang C, Hartman JD, Magalhães A, Al-Kaysi RO, Hayward RC, Hooley RJ, Beran GJO, Bardeen CJ, Mueller LJ. Bridging photochemistry and photomechanics with NMR crystallography: the molecular basis for the macroscopic expansion of an anthracene ester nanorod. Chem Sci 2020; 12:453-463. [PMID: 34163608 PMCID: PMC8178812 DOI: 10.1039/d0sc05118g] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 10/29/2020] [Indexed: 12/28/2022] Open
Abstract
Crystals composed of photoreactive molecules represent a new class of photomechanical materials with the potential to generate large forces on fast timescales. An example is the photodimerization of 9-tert-butyl-anthracene ester (9TBAE) in molecular crystal nanorods that leads to an average elongation of 8%. Previous work showed that this expansion results from the formation of a metastable crystalline product. In this article, it is shown how a novel combination of ensemble oriented-crystal solid-state NMR, X-ray diffraction, and first principles computational modeling can be used to establish the absolute unit cell orientations relative to the shape change, revealing the atomic-resolution mechanism for the photomechanical response and enabling the construction of a model that predicts an elongation of 7.4%, in good agreement with the experimental value. According to this model, the nanorod expansion does not result from an overall change in the volume of the unit cell, but rather from an anisotropic rearrangement of the molecular contents. The ability to understand quantitatively how molecular-level photochemistry generates mechanical displacements allows us to predict that the expansion could be tuned from +9% to -9.5% by controlling the initial orientation of the unit cell with respect to the nanorod axis. This application of NMR-assisted crystallography provides a new tool capable of tying the atomic-level structural rearrangement of the reacting molecular species to the mechanical response of a nanostructured sample.
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Affiliation(s)
- Kevin R Chalek
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Xinning Dong
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Fei Tong
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Ryan A Kudla
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Lingyan Zhu
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Adam D Gill
- Department of Biochemistry, University of California-Riverside Riverside CA 92521 USA
| | - Wenwen Xu
- Department of Chemical and Biological Engineering, University of Colorado Boulder 3415 Colorado Ave. Boulder CO 80303 USA
| | - Chen Yang
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Joshua D Hartman
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Alviclér Magalhães
- Department of Organic Chemistry, Institute of Chemistry, Federal University of Rio de Janeiro Rio de Janeiro RJ 21941-909 Brazil
| | - Rabih O Al-Kaysi
- College of Science and Health Professions-3124, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs Riyadh 11426 Kingdom of Saudi Arabia
| | - Ryan C Hayward
- Department of Chemical and Biological Engineering, University of Colorado Boulder 3415 Colorado Ave. Boulder CO 80303 USA
| | - Richard J Hooley
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | - Gregory J O Beran
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
| | | | - Leonard J Mueller
- Department of Chemistry, University of California-Riverside Riverside CA 92521 USA
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17
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Egorova O, Hafizi R, Woods DC, Day GM. Multifidelity Statistical Machine Learning for Molecular Crystal Structure Prediction. J Phys Chem A 2020; 124:8065-8078. [PMID: 32881496 DOI: 10.1021/acs.jpca.0c05006] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The prediction of crystal structures from first-principles requires highly accurate energies for large numbers of putative crystal structures. High accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multifidelity approach to re-evaluate the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen-bonded organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ mol-1 with between 4.2 and 6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact the assessment of the energetic ranking of crystal structures.
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Affiliation(s)
- Olga Egorova
- Statistical Sciences Research Institute, University of Southampton, Southampton, SO17 1BJ, U.K
| | - Roohollah Hafizi
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, SO17 1BJ, U.K
| | - David C Woods
- Statistical Sciences Research Institute, University of Southampton, Southampton, SO17 1BJ, U.K
| | - Graeme M Day
- Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton, SO17 1BJ, U.K
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18
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Giannini S, Ziogos OG, Carof A, Ellis M, Blumberger J. Flickering Polarons Extending over Ten Nanometres Mediate Charge Transport in High‐Mobility Organic Crystals. ADVANCED THEORY AND SIMULATIONS 2020. [DOI: 10.1002/adts.202000093] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Samuele Giannini
- Department of Physics and Astronomy and Thomas Young Centre University College London London WC1E 6BT UK
| | - Orestis George Ziogos
- Department of Physics and Astronomy and Thomas Young Centre University College London London WC1E 6BT UK
| | - Antoine Carof
- Laboratoire de Physique et Chimie Théoriques, CNRS, UMR No. 7019 Université de Lorraine BP 239 Vandœuvre‐lès‐Nancy Cedex 54506 France
| | - Matthew Ellis
- Department of Physics and Astronomy and Thomas Young Centre University College London London WC1E 6BT UK
| | - Jochen Blumberger
- Department of Physics and Astronomy and Thomas Young Centre University College London London WC1E 6BT UK
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