1
|
Góger S, Sandonas LM, Müller C, Tkatchenko A. Data-driven tailoring of molecular dipole polarizability and frontier orbital energies in chemical compound space. Phys Chem Chem Phys 2023; 25:22211-22222. [PMID: 37566426 PMCID: PMC10445328 DOI: 10.1039/d3cp02256k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/27/2023] [Indexed: 08/12/2023]
Abstract
Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and the dipole polarizability. Based on the recently introduced QM7-X dataset, augmented with accurate molecular polarizability calculations as well as analysis of functional group compositions, we show that polarizability and HOMO-LUMO gap are uncorrelated when considering sufficiently extended subsets of the chemical compound space. The relation between these two properties is further analyzed on specific examples of molecules with similar composition as well as homooligomers. Remarkably, the freedom brought by the lack of correlation between molecular polarizability and HOMO-LUMO gap enables the design of novel materials, as we demonstrate on the example of organic photodetector candidates.
Collapse
Affiliation(s)
- Szabolcs Góger
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Carolin Müller
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
| |
Collapse
|
2
|
McDonald SM, Augustine EK, Lanners Q, Rudin C, Catherine Brinson L, Becker ML. Applied machine learning as a driver for polymeric biomaterials design. Nat Commun 2023; 14:4838. [PMID: 37563117 PMCID: PMC10415291 DOI: 10.1038/s41467-023-40459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023] Open
Abstract
Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.
Collapse
Affiliation(s)
| | - Emily K Augustine
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Quinn Lanners
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Cynthia Rudin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - L Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Matthew L Becker
- Department of Chemistry, Duke University, Durham, NC, USA.
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.
| |
Collapse
|
3
|
Villot C, Huang T, Lao KU. Accurate prediction of global-density-dependent range-separation parameters based on machine learning. J Chem Phys 2023; 159:044103. [PMID: 37486048 DOI: 10.1063/5.0157340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023] Open
Abstract
In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0-1 and only five systems (0.07%) with an absolute error larger than 0.01 a0-1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.
Collapse
Affiliation(s)
- Corentin Villot
- Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA
| | - Tong Huang
- Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA
| | - Ka Un Lao
- Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA
| |
Collapse
|
4
|
Wang C, Li X, Liu L. Combining ab initio and ab initio molecular dynamics simulations to predict the complex refractive indices of organic polymers. Phys Chem Chem Phys 2023; 25:4950-4958. [PMID: 36722882 DOI: 10.1039/d2cp04768c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Organic polymers have attracted widespread interest in various fields ranging from optic and optoelectronic devices to optical system design owing to their light weight, high machinability, excellent thermal performance and reasonable costs. The complex refractive index is an inherent property of organic polymers and directly affects the accuracy of optical system simulation. This study introduces a theoretical protocol to accurately predict the complex refractive indices of organic polymers in the 0-5000 cm-1 region for guiding the discovery and design of high-refractive index materials. In the proposed protocol, we computed the refractive indices of polymers with different monomer units using ab initio calculated static polarizability and mass density obtained by classical isothermal-isobaric ensemble simulations based on the Lorentz-Lorenz equation; we proposed a "Polymer Polarizability Fragment Segmentation" method to extrapolate the polarizabilities of polymers with longer chain lengths; meanwhile, the imaginary part of the dielectric functions of the polymers was calculated using the ab initio molecular dynamics (AIMD) method, and the real part of the dielectric functions was obtained using the Kramers-Kronig relation. We calculated the complex refractive indices of four commonly used organic polymers, i.e. polyethylene, polyvinyl chloride, polyvinyl alcohol and polylactic acid, to demonstrate the performance of the theoretical protocol. The approach combining ab initio and AIMD simulations is effective and economical to predict the complex refractive indices of organic polymers and other organic materials.
Collapse
Affiliation(s)
- Chengchao Wang
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong, 250061, China. .,Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong, 266237, China
| | - Xiaoning Li
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong, 250061, China. .,Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong, 266237, China
| | - Linhua Liu
- School of Energy and Power Engineering, Shandong University, Jinan, Shandong, 250061, China. .,Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, Shandong, 266237, China
| |
Collapse
|
5
|
Rajulapati L, Chinta S, Shyamala B, Rengaswamy R. Integration of Machine Learning and First Principles Models. AIChE J 2022. [DOI: 10.1002/aic.17715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Lokesh Rajulapati
- Department of Chemical Engineering Indian Institute of Technology Madras Chennai India
| | | | - Bala Shyamala
- Department of Chemical Engineering Indian Institute of Technology Madras Chennai India
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering Indian Institute of Technology Madras Chennai India
- Robert Bosch Centre for Data Science and Artificial Intelligence Indian Institute of Technology Madras Chennai India
| |
Collapse
|
6
|
Liu K, Qin H, Tian M, Zhang L, Mi J. Towards a comprehensive optimization of dielectric and viscoelastic performance of poly(ethylene-co-methyl acrylate) through chain sequence regulation. POLYMER 2022. [DOI: 10.1016/j.polymer.2022.124657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
7
|
Vishwakarma G, Sonpal A, Hachmann J. Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry. TRENDS IN CHEMISTRY 2021. [DOI: 10.1016/j.trechm.2020.12.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
8
|
Filho EBDS, Su Y, Takashina M, Fowler G, Chien P, Stiegman AE. Additive and free‐volume effects on the refractive index of a thiol‐ene polymer network. J Appl Polym Sci 2020. [DOI: 10.1002/app.49544] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Edmundo B. D. S. Filho
- Department of Chemistry and Biochemistry Florida State University Tallahassee Florida USA
| | - Yue Su
- Department of Chemistry and Biochemistry Florida State University Tallahassee Florida USA
| | - Mamoru Takashina
- Department of Chemistry and Biochemistry Florida State University Tallahassee Florida USA
| | - Gary Fowler
- Department of Earth, Ocean and Atmospheric Science Florida State University Tallahassee Florida USA
| | - Po‐Hsiu Chien
- Department of Chemistry and Biochemistry Florida State University Tallahassee Florida USA
| | - Albert E. Stiegman
- Department of Chemistry and Biochemistry Florida State University Tallahassee Florida USA
| |
Collapse
|
9
|
Affiliation(s)
- Evan Komp
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Stéphanie Valleau
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
10
|
Braham EJ, Davidson RD, Al-Hashimi M, Arróyave R, Banerjee S. Navigating the design space of inorganic materials synthesis using statistical methods and machine learning. Dalton Trans 2020; 49:11480-11488. [PMID: 32743629 DOI: 10.1039/d0dt02028a] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Data-driven approaches have brought about a revolution in manufacturing; however, challenges persist in their applications to synthetic strategies. Their application to the deterministic navigation of reaction trajectories to stabilize crystalline solids with precise composition, atomic connectivity, microstructural dimensionality, and surface structure remains a frontier in inorganic materials research. The design of synthetic methodologies for the preparation of inorganic materials is often inefficient in terms of exploration of potentially vast design spaces spanning multiple process variables, reaction sequences, as well as structural parameters and reactivities of precursors and structure-directing agents. Reported synthetic methods are further limited in terms of the insight they provide into underlying chemical and physical principles. The recent surge in interest in accelerating the discovery of new materials can be considered as an opportunity to re-evaluate our approach to materials synthesis, and for considering new frameworks for exploration that are systematic and strategic in approach. Herein, we outline with the help of several illustrative examples, the challenges, opportunities, and limitations of data-driven synthesis design. The account collates discussion of design-of-experiments sampling methods, machine learning modeling, and active learning to develop experimental workflows that accelerate the experimental navigation of synthetic landscapes.
Collapse
Affiliation(s)
- Erick J Braham
- Department of Chemistry, Texas A&M University, College Station, TX 77843, USA. and Department of Material Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
| | - Rachel D Davidson
- Department of Chemistry, Texas A&M University, College Station, TX 77843, USA. and Department of Material Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
| | - Mohammed Al-Hashimi
- Department of Chemistry, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar
| | - Raymundo Arróyave
- Department of Material Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
| | - Sarbajit Banerjee
- Department of Chemistry, Texas A&M University, College Station, TX 77843, USA. and Department of Material Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
| |
Collapse
|
11
|
Mancuso JL, Mroz AM, Le KN, Hendon CH. Electronic Structure Modeling of Metal-Organic Frameworks. Chem Rev 2020; 120:8641-8715. [PMID: 32672939 DOI: 10.1021/acs.chemrev.0c00148] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Owing to their molecular building blocks, yet highly crystalline nature, metal-organic frameworks (MOFs) sit at the interface between molecule and material. Their diverse structures and compositions enable them to be useful materials as catalysts in heterogeneous reactions, electrical conductors in energy storage and transfer applications, chromophores in photoenabled chemical transformations, and beyond. In all cases, density functional theory (DFT) and higher-level methods for electronic structure determination provide valuable quantitative information about the electronic properties that underpin the functions of these frameworks. However, there are only two general modeling approaches in conventional electronic structure software packages: those that treat materials as extended, periodic solids, and those that treat materials as discrete molecules. Each approach has features and benefits; both have been widely employed to understand the emergent chemistry that arises from the formation of the metal-organic interface. This Review canvases these approaches to date, with emphasis placed on the application of electronic structure theory to explore reactivity and electron transfer using periodic, molecular, and embedded models. This includes (i) computational chemistry considerations such as how functional, k-grid, and other model variables are selected to enable insights into MOF properties, (ii) extended solid models that treat MOFs as materials rather than molecules, (iii) the mechanics of cluster extraction and subsequent chemistry enabled by these molecular models, (iv) catalytic studies using both solids and clusters thereof, and (v) embedded, mixed-method approaches, which simulate a fraction of the material using one level of theory and the remainder of the material using another dissimilar theoretical implementation.
Collapse
Affiliation(s)
- Jenna L Mancuso
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97405, United States
| | - Austin M Mroz
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97405, United States
| | - Khoa N Le
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97405, United States
| | - Christopher H Hendon
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97405, United States
| |
Collapse
|
12
|
Haghighatlari M, Vishwakarma G, Altarawy D, Subramanian R, Kota BU, Sonpal A, Setlur S, Hachmann J. ChemML
: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1458] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Mojtaba Haghighatlari
- Department of Chemical and Biological Engineering University at Buffalo, The State University of New York Buffalo New York
| | - Gaurav Vishwakarma
- Department of Chemical and Biological Engineering University at Buffalo, The State University of New York Buffalo New York
| | - Doaa Altarawy
- The Molecular Sciences Software Institute, Virginia Tech Blacksburg Virginia
- Computer and Systems Engineering Department Alexandria University Alexandria Egypt
| | - Ramachandran Subramanian
- Department of Computer Science and Engineering University at Buffalo, The State University of New York Buffalo New York
- Center for Unified Biometrics and Sensors University at Buffalo, The State University of New York Buffalo New York
| | - Bhargava U. Kota
- Department of Computer Science and Engineering University at Buffalo, The State University of New York Buffalo New York
- Center for Unified Biometrics and Sensors University at Buffalo, The State University of New York Buffalo New York
| | - Aditya Sonpal
- Department of Chemical and Biological Engineering University at Buffalo, The State University of New York Buffalo New York
| | - Srirangaraj Setlur
- Department of Computer Science and Engineering University at Buffalo, The State University of New York Buffalo New York
- Center for Unified Biometrics and Sensors University at Buffalo, The State University of New York Buffalo New York
- Center of Excellence for Document Analysis and Recognition, University at Buffalo The State University of New York Buffalo New York
| | - Johannes Hachmann
- Department of Chemical and Biological Engineering University at Buffalo, The State University of New York Buffalo New York
- Computational and Data‐Enabled Science and Engineering Graduate Program University at Buffalo, The State University of New York Buffalo New York
- New York State Center of Excellence in Materials Informatics Buffalo New York
| |
Collapse
|
13
|
Chen G, Shen Z, Iyer A, Ghumman UF, Tang S, Bi J, Chen W, Li Y. Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges. Polymers (Basel) 2020; 12:E163. [PMID: 31936321 PMCID: PMC7023065 DOI: 10.3390/polym12010163] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 12/27/2019] [Accepted: 01/02/2020] [Indexed: 12/18/2022] Open
Abstract
Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
Collapse
Affiliation(s)
- Guang Chen
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; (G.C.); (Z.S.)
| | - Zhiqiang Shen
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; (G.C.); (Z.S.)
| | - Akshay Iyer
- Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; (A.I.); (U.F.G.)
| | - Umar Farooq Ghumman
- Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; (A.I.); (U.F.G.)
| | - Shan Tang
- State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, and International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116023, China;
| | - Jinbo Bi
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA;
| | - Wei Chen
- Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA; (A.I.); (U.F.G.)
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA; (G.C.); (Z.S.)
- Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
| |
Collapse
|
14
|
Caldeweyher E, Mewes JM, Ehlert S, Grimme S. Extension and evaluation of the D4 London-dispersion model for periodic systems. Phys Chem Chem Phys 2020; 22:8499-8512. [PMID: 32292979 DOI: 10.1039/d0cp00502a] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We present an extension of the DFT-D4 model [J. Chem. Phys., 2019, 150, 154122] for periodic systems. The main new ingredients are additional reference polarizabilities for highly-coordinated group 1-5 elements derived from pseudo-periodic electrostatically-embedded cluster calculations. To illustrate the performance of the updated method, several test cases are considered, for which we compare D4 to its predecessor D3(BJ), as well as to a comprehensive set of other dispersion-corrected methods. The largest improvements are observed for solid-state polarizabilities of 16 inorganic salts, where the D4 model achieves an unprecedented accuracy, surpassing its predecessor as well as other, computationally much more demanding approaches. For cell volumes and lattice energies of two sets of chemically diverse molecular crystals, the accuracy gain is less pronounced compared to the already excellently performing D3(BJ) method. For the challenging adsorption energies of small organic molecules on metallic as well as on ionic surfaces, DFT-D4 provides values in good agreement with experimental and/or high-level references. These results suggest the application of the proposed D4 model as a physically improved yet computationally efficient dispersion correction for standard DFT calculations as well as low-cost approaches like semi-empirical or even force-field models.
Collapse
Affiliation(s)
| | | | | | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Bonn, Germany.
| |
Collapse
|
15
|
Su Y, Filho EBDS, Peek N, Chen B, Stiegman AE. High Refractive Index Polymers (n > 1.7), Based on Thiol–Ene Cross-Linking of Polarizable P═S and P═Se Organic/Inorganic Monomers. Macromolecules 2019. [DOI: 10.1021/acs.macromol.9b01671] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yue Su
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, United States
| | - Edmundo B. D. S. Filho
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, United States
| | - Nathan Peek
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, United States
| | - Banghao Chen
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, United States
| | - A. E. Stiegman
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, United States
| |
Collapse
|
16
|
Afzal MAF, Sonpal A, Haghighatlari M, Schultz AJ, Hachmann J. A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chem Sci 2019; 10:8374-8383. [PMID: 31762970 PMCID: PMC6855195 DOI: 10.1039/c9sc02677k] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Accepted: 07/08/2019] [Indexed: 01/23/2023] Open
Abstract
Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.
The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations.
Collapse
Affiliation(s)
- Mohammad Atif Faiz Afzal
- Department of Chemical and Biological Engineering , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA . ;
| | - Aditya Sonpal
- Department of Chemical and Biological Engineering , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA . ;
| | - Mojtaba Haghighatlari
- Department of Chemical and Biological Engineering , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA . ;
| | - Andrew J Schultz
- Department of Chemical and Biological Engineering , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA . ;
| | - Johannes Hachmann
- Department of Chemical and Biological Engineering , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA . ; .,Computational and Data-Enabled Science and Engineering Graduate Program , University at Buffalo , The State University of New York , Buffalo , NY 14260 , USA.,New York State Center of Excellence in Materials Informatics , Buffalo , NY 14203 , USA
| |
Collapse
|
17
|
|
18
|
Afzal MAF, Hachmann J. Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers. Phys Chem Chem Phys 2019; 21:4452-4460. [PMID: 30734777 DOI: 10.1039/c8cp05492d] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In a previous study, we introduced a new computational protocol to accurately predict the index of refraction (RI) of organic polymers using a combination of first-principles and data modeling. This protocol is based on the Lorentz-Lorenz equation and involves the calculation of static polarizabilities and number densities of oligomer sequences, which are extrapolated to the polymer limit. We chose to compute the polarizabilities within the density functional theory (DFT) framework using the PBE0/def2-TZVP-D3 model chemistry. While this ad hoc choice proved remarkably successful, it is also relatively expensive from a computational perspective. It represents the bottleneck step in the overall RI modeling protocol, thus limiting its utility for virtual high-throughput screening studies, in which efficiency is essential. For polymers that exhibit late-onset extensivity, the employed linear extrapolation scheme can require demanding calculations on long-oligomer sequences, thus becoming another bottleneck. In the work presented here, we benchmark DFT model chemistries to identify approaches that optimize the balance between accuracy and efficiency for this application domain. We compare results for conjugated and non-conjugated polymers, augment our original extrapolation approach with a non-linear option, analyze how the polarizability errors propagate into the RI predictions, and offer guidance for method selection.
Collapse
Affiliation(s)
- Mohammad Atif Faiz Afzal
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA.
| | | |
Collapse
|
19
|
Jadoun S, Verma A, Riaz U. Luminol modified polycarbazole and poly(o-anisidine): Theoretical insights compared with experimental data. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 204:64-72. [PMID: 29902772 DOI: 10.1016/j.saa.2018.06.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 06/02/2018] [Accepted: 06/07/2018] [Indexed: 06/08/2023]
Abstract
With the aim to explore the effect of luminol as a multifunctional dopant for conjugated polymers, the present study reports the ultrasound-assisted doping of polycarbazole (PCz) and poly(o-anisidine) (PAnis) with luminol in basic, acidic and neutral media. The synthesized homopolymers and luminol doped polymers were characterized using FT-IR, UV-visible and XRD studies while the photo-physical properties were investigated via fluorescence spectroscopy. Density functional theory (DFT) calculations were performed to get insights into the structural, optical, and electronic properties of homopolymers of polycarbazole (PCz) and poly(o-anisidine) (PAnis). Vibrational bands B3LYP/6-311G (d,p) level, UV-vis spectral bands and electronic properties such as ionization potentials (IP), electron affinities (EA) and HOMO-LUMO band gap energies of the homopolymers and doped polymers were calculated and compared. Results revealed that luminol doped polymers showed different photo-physical characteristics in acidic, basic and neutral media which could be tuned to obtain near infrared (NIR) emitting polymers.
Collapse
Affiliation(s)
- Sapana Jadoun
- Materials Research Laboratory, Department of Chemistry, Jamia Millia Islamia, New Delhi 110025, India
| | - Anurakshee Verma
- Materials Research Laboratory, Department of Chemistry, Jamia Millia Islamia, New Delhi 110025, India
| | - Ufana Riaz
- Materials Research Laboratory, Department of Chemistry, Jamia Millia Islamia, New Delhi 110025, India.
| |
Collapse
|
20
|
Rupp M, von Lilienfeld OA, Burke K. Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry. J Chem Phys 2018; 148:241401. [DOI: 10.1063/1.5043213] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Affiliation(s)
- Matthias Rupp
- Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, 14195 Berlin, Germany
| | - O. Anatole von Lilienfeld
- Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel, 4056 Basel, Switzerland
| | - Kieron Burke
- Departments of Chemistry and Physics, University of California, Irvine, California 92697, USA
| |
Collapse
|
21
|
Hachmann J, Afzal MAF, Haghighatlari M, Pal Y. Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space. MOLECULAR SIMULATION 2018. [DOI: 10.1080/08927022.2018.1471692] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Johannes Hachmann
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York , Buffalo, NY, USA
- Computational and Data-Enabled Science and Engineering Graduate Program, University at Buffalo, The State University of New York , Buffalo, NY, USA
- New York State Center of Excellence in Materials Informatics , Buffalo, NY, USA
| | - Mohammad Atif Faiz Afzal
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York , Buffalo, NY, USA
| | - Mojtaba Haghighatlari
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York , Buffalo, NY, USA
| | - Yudhajit Pal
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York , Buffalo, NY, USA
| |
Collapse
|