1
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Bhat V, Ganapathysubramanian B, Risko C. Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline. J Phys Chem Lett 2024; 15:7206-7213. [PMID: 38973725 DOI: 10.1021/acs.jpclett.4c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Organic semiconductors (OSC) offer tremendous potential across a wide range of (opto)electronic applications. OSC development, however, is often limited by trial-and-error design, with computational modeling approaches deployed to evaluate and screen candidates through a suite of molecular and materials descriptors that generally require hours to days of computational time to accumulate. Such bottlenecks slow the pace and limit the exploration of the vast chemical space comprising OSC. When considering charge-carrier transport in OSC, a key parameter of interest is the intermolecular electronic coupling. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic crystalline materials from their three-dimensional (3D) molecular geometries. The ML predictions take only a few seconds of computing time compared to hours by density functional theory (DFT) methods. To demonstrate the utility of the ML predictions, we deploy the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobility anisotropy for more than 60,000 molecular crystal structures and compare the ML predictions to DFT benchmarks.
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Affiliation(s)
- Vinayak Bhat
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering & Translational AI Center, Iowa State University, Ames, Iowa 50010, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506, United States
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2
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Wahab A, Gershoni-Poranne R. COMPAS-3: a dataset of peri-condensed polybenzenoid hydrocarbons. Phys Chem Chem Phys 2024; 26:15344-15357. [PMID: 38758092 DOI: 10.1039/d4cp01027b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
We introduce the third installment of the COMPAS Project - a COMputational database of Polycyclic Aromatic Systems, focused on peri-condensed polybenzenoid hydrocarbons. In this installment, we develop two datasets containing the optimized ground-state structures and a selection of molecular properties of ∼39k and ∼9k peri-condensed polybenzenoid hydrocarbons (at the GFN2-xTB and CAM-B3LYP-D3BJ/cc-pvdz//CAM-B3LYP-D3BJ/def2-SVP levels, respectively). The manuscript details the enumeration and data generation processes and describes the information available within the datasets. An in-depth comparison between the two types of computation is performed, and it is found that the geometrical disagreement is maximal for slightly-distorted molecules. In addition, a data-driven analysis of the structure-property trends of peri-condensed PBHs is performed, highlighting the effect of the size of peri-condensed islands and linearly annulated rings on the HOMO-LUMO gap. The insights described herein are important for rational design of novel functional aromatic molecules for use in, e.g., organic electronics. The generated datasets provide a basis for additional data-driven machine- and deep-learning studies in chemistry.
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Affiliation(s)
- Alexandra Wahab
- The Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Renana Gershoni-Poranne
- The Schulich Faculty of Chemistry and the Resnick Sustainability Center for Catalysis, Technion - Israel Institute of Technology, Haifa 32000, Israel.
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3
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Mayo Yanes E, Chakraborty S, Gershoni-Poranne R. COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems. Sci Data 2024; 11:97. [PMID: 38242917 PMCID: PMC10799083 DOI: 10.1038/s41597-024-02927-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/05/2024] [Indexed: 01/21/2024] Open
Abstract
Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date. Our dataset contains ~500k molecules comprising 11 types of aromatic and antiaromatic building blocks calculated at the GFN1-xTB level and is representative of a highly diverse chemical space. We detail the structure enumeration process and the methods used to provide various electronic properties (including HOMO-LUMO gap, adiabatic ionization potential, and adiabatic electron affinity). Additionally, we benchmark against a ~50k dataset calculated at the CAM-B3LYP-D3BJ/def2-SVP level and develop a fitting scheme to correct the xTB values to higher accuracy. These new datasets represent the second installment in the COMputational database of Polycyclic Aromatic Systems (COMPAS) Project.
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Affiliation(s)
- Eduardo Mayo Yanes
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Sabyasachi Chakraborty
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Renana Gershoni-Poranne
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel.
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4
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Blaskovits JT, Laplaza R, Vela S, Corminboeuf C. Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305602. [PMID: 37815223 DOI: 10.1002/adma.202305602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 09/05/2023] [Indexed: 10/11/2023]
Abstract
The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.
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Affiliation(s)
- J Terence Blaskovits
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Competence in Research "Sustainable chemical processes through catalysis (NCCR Catalysis)" École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
| | - Sergi Vela
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL),Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
| | - Clémence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Competence in Research "Sustainable chemical processes through catalysis (NCCR Catalysis)" École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL),Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
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5
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Zhugayevych A, Sun W, van der Heide T, Lien-Medrano CR, Frauenheim T, Tretiak S. Benchmark Data Set of Crystalline Organic Semiconductors. J Chem Theory Comput 2023; 19:8481-8490. [PMID: 37969072 PMCID: PMC10688188 DOI: 10.1021/acs.jctc.3c00861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 11/17/2023]
Abstract
This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r2SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r2SCAN-D3. Our results show that r2SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.
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Affiliation(s)
- Andriy Zhugayevych
- Max
Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
| | - Wenbo Sun
- Bremen
Center for Computational Materials Science, Am Fallturm 1, 28359 Bremen, Germany
| | - Tammo van der Heide
- Bremen
Center for Computational Materials Science, Am Fallturm 1, 28359 Bremen, Germany
| | | | - Thomas Frauenheim
- Bremen
Center for Computational Materials Science, Am Fallturm 1, 28359 Bremen, Germany
| | - Sergei Tretiak
- Los
Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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6
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Liu A, Lee M, Venkatesh R, Bonsu JA, Volkovinsky R, Meredith JC, Reichmanis E, Grover MA. Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:8816-8826. [PMID: 38027538 PMCID: PMC10653076 DOI: 10.1021/acs.chemmater.3c01842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/30/2023] [Accepted: 10/02/2023] [Indexed: 12/01/2023]
Abstract
Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community.
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Affiliation(s)
- Aaron
L. Liu
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Myeongyeon Lee
- Department
of Chemical & Biomolecular Engineering, Lehigh University, 124 East Morton Street, Bethlehem, Pennsylvania 18015, United States
| | - Rahul Venkatesh
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Jessica A. Bonsu
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Ron Volkovinsky
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - J. Carson Meredith
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Elsa Reichmanis
- Department
of Chemical & Biomolecular Engineering, Lehigh University, 124 East Morton Street, Bethlehem, Pennsylvania 18015, United States
| | - Martha A. Grover
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
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7
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Kobayashi Y, Miyake Y, Ishiwari F, Ishiwata S, Saeki A. Machine learning of atomic force microscopy images of organic solar cells. RSC Adv 2023; 13:15107-15113. [PMID: 37207099 PMCID: PMC10189247 DOI: 10.1039/d3ra02492j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/11/2023] [Indexed: 05/21/2023] Open
Abstract
The bulk heterojunction structures of organic photovoltaics (OPVs) have been overlooked in their machine learning (ML) approach despite their presumably significant impact on power conversion efficiency (PCE). In this study, we examined the use of atomic force microscopy (AFM) images to construct an ML model for predicting the PCE of polymer : non-fullerene molecular acceptor OPVs. We manually collected experimentally observed AFM images from the literature, applied data curing and performed image analyses (fast Fourier transform, FFT; gray-level co-occurrence matrix, GLCM; histogram analysis, HA) and ML linear regression. The accuracy of the model did not considerably improve even by including AFM data in addition to the chemical structure fingerprints, material properties and process parameters. However, we found that a specific spatial wavelength of FFT (40-65 nm) significantly affects PCE. The GLCM and HA methods, such as homogeneity, correlation and skewness expand the scope of image analysis and artificial intelligence in materials science research fields.
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Affiliation(s)
- Yasuhito Kobayashi
- Division of Materials Physics, Graduate School of Engineering Science, Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan
- Interactive Materials Science CADET, Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan
| | - Yuta Miyake
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
| | - Fumitaka Ishiwari
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University 1-1 Yamadaoka Suita Osaka 565-0871 Japan
- PRESTO, Japan Science and Technology Agency (JST) Kawaguchi Saitama 332-0012 Japan
| | - Shintaro Ishiwata
- Division of Materials Physics, Graduate School of Engineering Science, Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan
| | - Akinori Saeki
- Department of Applied Chemistry, Graduate School of Engineering, Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan
- Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University 1-1 Yamadaoka Suita Osaka 565-0871 Japan
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8
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Bhat V, Callaway CP, Risko C. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials. Chem Rev 2023. [PMID: 37141497 DOI: 10.1021/acs.chemrev.2c00704] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.
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Affiliation(s)
- Vinayak Bhat
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Connor P Callaway
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
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9
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Xie X, Troisi A. Identification via Virtual Screening of Emissive Molecules with a Small Exciton-Vibration Coupling for High Color Purity and Potential Large Exciton Delocalization. J Phys Chem Lett 2023; 14:4119-4126. [PMID: 37129191 PMCID: PMC10165648 DOI: 10.1021/acs.jpclett.3c00749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A sequence of quantum chemical computations of increasing accuracy was used in this work to identify molecules with small exciton reorganization energy (exciton-vibration coupling), of interest for light emitting devices and coherent exciton transport, starting from a set of ∼4500 known molecules. We validated an approximate computational approach based on single-point calculations of the force in the excited state, which was shown to be very efficient in identifying the most promising candidates. We showed that a simple descriptor based on the bond order could be used to find molecules with potentially small exciton reorganization energies without performing excited state calculations. A small set of chemically diverse molecules with a small exciton reorganization energy was analyzed in greater detail to identify common features leading to this property. Many such molecules display an A-B-A structure where the bonding/antibonding patterns in the fragments A are similar in HOMO and LUMO. Another group of molecules with small reorganization energy displays instead HOMO and LUMO with a strong nonbonding character.
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Affiliation(s)
- Xiaoyu Xie
- Department of Chemistry, University of Liverpool Liverpool L69 3BX, U.K
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX, U.K
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10
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Bhat V, Sornberger P, Pokuri BSS, Duke R, Ganapathysubramanian B, Risko C. Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches. Chem Sci 2022; 14:203-213. [PMID: 36605753 PMCID: PMC9769113 DOI: 10.1039/d2sc04676h] [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: 08/20/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022] Open
Abstract
Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed.
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Affiliation(s)
- Vinayak Bhat
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky Lexington Kentucky 40506 USA
| | - Parker Sornberger
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky Lexington Kentucky 40506 USA
| | - Balaji Sesha Sarath Pokuri
- Department of Mechanical Engineering and Translational AI Center, Iowa State University Ames Iowa 50010 USA
| | - Rebekah Duke
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky Lexington Kentucky 40506 USA
| | | | - Chad Risko
- Department of Chemistry and Center for Applied Energy Research, University of Kentucky Lexington Kentucky 40506 USA
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11
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Huang LY, Ai Q, Risko C. The Role of Crystal Packing on the Optical Response of Trialkyltetrelethynyl Acenes. J Chem Phys 2022; 157:084703. [DOI: 10.1063/5.0097421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The electronic and optical responses of an organic semiconductor (OSC) are dictated by the chemistries of the molecular or polymer building blocks and how these chromophores pack in the solid state. Understanding the physicochemical natures of these responses are not only critical for determining OSC performance for a particular application, but the UV/visible optical response may also be of potential use to determine aspects of the molecular-scale solid-state packing for crystal polymorphs or thin-film morphologies that are difficult to determine otherwise. To probe these relationships, we report the quantum-chemical investigation of a series of trialkyltetrelethynyl acenes (tetrel = silicon or germanium) that adopt the brickwork (BW), slip-stack (SS), or herringbone (HB) packing configurations; the π-conjugated backbones considered here are pentacene (PEN) and anthradithiophene (ADT). For comparison, HB-packed (unsubstituted) pentacene is also included. Density functional theory (DFT) and G0W0 (single-shot GW) electronic band structures, G0W0-BSE (Bethe-Salpeter Equation)-derived optical spectra, polarized ϵ2 spectra, and distributions of both singlet and triplet exciton wave functions are reported. Configurational disorder is also considered. Further, we evaluate the probability of singlet fission in these materials through energy conservation relationships.
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Affiliation(s)
| | - Qianxiang Ai
- Chemistry, Fordham University - Rose Hill Campus, United States of America
| | - Chad Risko
- Chemistry, University of Kentucky, United States of America
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12
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Affiliation(s)
- Josiah Roberts
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
| | - Eva Zurek
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
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13
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Yanes-Rodríguez R, Cabrera-Ramírez A, Prosmiti R. Delving into guest-free and He-filled sI and sII clathrate hydrates: a first-principles computational study. Phys Chem Chem Phys 2022; 24:13119-13129. [PMID: 35587105 DOI: 10.1039/d2cp00701k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The dynamics of the formation of a specific clathrate hydrate as well as its thermodynamic transitions depend on the interactions between the trapped molecules and the host water lattice. The molecular-level understanding of the different underlying processes benefits not only the description of the properties of the system, but also allows the development of multiple technological applications such as gas storage, gas separation, energy transport, etc. In this work we investigate the stability of periodic crystalline structures, such as He@sI and He@sII clathrate hydrates by first-principles computations. We consider such host water networks interacting with a guest He atom using selected density functional theory approaches, in order to explore the effects on the encapsulation of a light atom in the sI/sII crystals, by deriving all energy components (guest-water, water-water, guest-guest). Structural properties and energies were first computed by structural relaxations of the He-filled and empty sI/sII unit cells, yielding lattice and compressibility parameters comparable to experimental and theoretical values available for those hydrates. According to the results obtained, the He enclathration in the sI/sII unit cells is a stabilizing process, and both He@sI and He@sII clathrates, considering single cage occupancy, are predicted to be stable whatever the XDM or D4 dispersion correction applied. Our results further reveal that despite the weak underlying interactions the He encapsulation has a rather notable effect on both lattice parameters and energetics, with the He@sII being the most energetically favorable in accord with recent experimental observations.
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Affiliation(s)
- Raquel Yanes-Rodríguez
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain. .,Doctoral Programme in Theoretical Chemistry and Computational Modelling, Doctoral School, Universidad Autónoma de Madrid, Spain
| | - Adriana Cabrera-Ramírez
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain. .,Doctoral Programme in Theoretical Chemistry and Computational Modelling, Doctoral School, Universidad Autónoma de Madrid, Spain
| | - Rita Prosmiti
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain.
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14
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Callaway CP, Liu AL, Venkatesh R, Zheng Y, Lee M, Meredith JC, Grover M, Risko C, Reichmanis E. The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors. ACS APPLIED MATERIALS & INTERFACES 2022; 14:3613-3620. [PMID: 35037454 DOI: 10.1021/acsami.1c20994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The advent of data analytics techniques and materials informatics provides opportunities to accelerate the discovery and development of organic semiconductors for electronic devices. However, the development of engineering solutions is limited by the ability to control thin-film morphology in an immense parameter space. The combination of high-throughput experimentation (HTE) laboratory techniques and data analytics offers tremendous avenues to traverse the expansive domains of tunable variables offered by organic semiconductor thin films. This Perspective outlines the steps required to incorporate a comprehensive informatics methodology into the experimental development of polymer-based organic semiconductor technologies. The translation of solution processing and property metrics to thin-film behavior is crucial to inform efficient HTE for data collection and application of data-centric tools to construct new process-structure-property relationships. We argue that detailed investigation of the solution state prior to deposition in conjunction with thin-film characterization will yield a deeper understanding of the physicochemical mechanisms influencing performance in π-conjugated polymer electronics, with data-driven approaches offering predictive capabilities previously unattainable via traditional experimental means.
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Affiliation(s)
- Connor P Callaway
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Aaron L Liu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Rahul Venkatesh
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Yulong Zheng
- School of Chemistry & Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, Georgia 30332, United States
| | - Myeongyeon Lee
- Department of Chemical & Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - J Carson Meredith
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Martha Grover
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Elsa Reichmanis
- Department of Chemical & Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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