1
|
Vadaddi SM, Zhao Q, Savoie BM. Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability. J Phys Chem A 2024; 128:2543-2555. [PMID: 38517281 DOI: 10.1021/acs.jpca.3c07240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.
Collapse
Affiliation(s)
- Sai Mahit Vadaddi
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Qiyuan Zhao
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brett M Savoie
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| |
Collapse
|
2
|
Kirkland JK, Kumawat J, Shaban Tameh M, Tolman T, Lambert AC, Lief GR, Yang Q, Ess DH. Machine Learning Models for Predicting Zirconocene Properties and Barriers. J Chem Inf Model 2024; 64:775-784. [PMID: 38259142 DOI: 10.1021/acs.jcim.3c01575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.
Collapse
Affiliation(s)
- Justin K Kirkland
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Jugal Kumawat
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Maliheh Shaban Tameh
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Tyson Tolman
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Allison C Lambert
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Graham R Lief
- Research and Technology, Chevron Phillips Chemical Company, Highways 60 & 123, Bartlesville, Oklahoma 74003, United States
| | - Qing Yang
- Research and Technology, Chevron Phillips Chemical Company, Highways 60 & 123, Bartlesville, Oklahoma 74003, United States
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| |
Collapse
|
3
|
Escayola S, Bahri-Laleh N, Poater A. % VBur index and steric maps: from predictive catalysis to machine learning. Chem Soc Rev 2024; 53:853-882. [PMID: 38113051 DOI: 10.1039/d3cs00725a] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.
Collapse
Affiliation(s)
- Sílvia Escayola
- Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, c/Mª Aurèlia Capmany 69, 17003 Girona, Catalonia, Spain.
- Donostia International Physics Center (DIPC), 20018 Donostia, Euskadi, Spain
| | - Naeimeh Bahri-Laleh
- Iran Polymer and Petrochemical Institute (IPPI), P.O. Box 14965/115, Tehran, Iran
- Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM), Hiroshima University, Hiroshima, 739-8526, Japan
| | - Albert Poater
- Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, c/Mª Aurèlia Capmany 69, 17003 Girona, Catalonia, Spain.
| |
Collapse
|
4
|
Marques E, de Gendt S, Pourtois G, van Setten MJ. Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information. J Chem Inf Model 2023; 63:1454-1461. [PMID: 36864757 DOI: 10.1021/acs.jcim.2c01502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decrease the computational cost for these predictions compared to traditional methods, which require an optimal path search along a high-dimensional potential energy surface. To enable this new route, we need both large and accurate datasets and a compact yet complete description of the reactions. Although data for chemical reactions is becoming increasingly available, the key step of encoding the reaction as an efficient descriptor remains a big challenge. In this paper, we demonstrate that including electronic energy levels in the description of the reaction significantly improves the prediction accuracy and transferability. Feature importance analysis further demonstrates that electronic energy levels have a higher importance than some structural information and typically require less space in the reaction encoding vector. In general, we observe that the results of the feature importance analysis relate well to the domain knowledge of fundamental chemical principles. This work can help to build better chemical reaction encodings for machine learning and thus improve the predictions of machine learning models for reaction activation energies. These models could ultimately be used to recognize reaction limiting steps in large reaction systems, allowing to account for bottlenecks at the design stage.
Collapse
Affiliation(s)
- Esteban Marques
- Department of Chemistry, KU Leuven (University of Leuven), Celestijnenlaan 200 F, Heverlee 3001, Belgium.,IMEC, Kapeldreef 75, Leuven 3001, Belgium
| | - Stefan de Gendt
- Department of Chemistry, KU Leuven (University of Leuven), Celestijnenlaan 200 F, Heverlee 3001, Belgium.,IMEC, Kapeldreef 75, Leuven 3001, Belgium
| | - Geoffrey Pourtois
- IMEC, Kapeldreef 75, Leuven 3001, Belgium.,Department of Chemistry, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, Wilrijk 2610, Belgium
| | - Michiel J van Setten
- IMEC, Kapeldreef 75, Leuven 3001, Belgium.,ETSF European Theoretical Spectroscopy Facility, Institut de Physique, Université de Liège, Allée du 6 août 17, Liège 4000, Belgium
| |
Collapse
|
5
|
Wu FY, Yin J, Chen SC, Gao XQ, Zhou L, Lu Y, Lei J, Zhong GJ, Li ZM. Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding. POLYMER 2023. [DOI: 10.1016/j.polymer.2023.125736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
6
|
Ismail I, Chantreau Majerus R, Habershon S. Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities. J Phys Chem A 2022; 126:7051-7069. [PMID: 36190262 PMCID: PMC9574932 DOI: 10.1021/acs.jpca.2c06408] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.
Collapse
Affiliation(s)
- Idil Ismail
- Department of Chemistry, University of Warwick, CoventryCV4 7AL, United Kingdom
| | | | - Scott Habershon
- Department of Chemistry, University of Warwick, CoventryCV4 7AL, United Kingdom
| |
Collapse
|
7
|
Lewis‐Atwell T, Townsend PA, Grayson MN. Machine learning activation energies of chemical reactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1593] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Toby Lewis‐Atwell
- Department of Computer Science, Faculty of Science University of Bath Bath UK
| | - Piers A. Townsend
- Department of Chemistry, Faculty of Science University of Bath Bath UK
| | | |
Collapse
|
8
|
Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annu Rev Chem Biomol Eng 2022; 13:405-429. [PMID: 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , ,
| |
Collapse
|
9
|
Harper DR, Nandy A, Arunachalam N, Duan C, Janet JP, Kulik HJ. Representations and strategies for transferable machine learning Improve model performance in chemical discovery. J Chem Phys 2022; 156:074101. [DOI: 10.1063/5.0082964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Daniel R Harper
- Massachusetts Institute of Technology, United States of America
| | - Aditya Nandy
- Massachusetts Institute of Technology, United States of America
| | | | - Chenru Duan
- Massachusetts Institute of Technology, United States of America
| | | | - Heather J. Kulik
- Dept of Chemical Engineering, Massachusetts Institute of Technology, United States of America
| |
Collapse
|
10
|
Farrar EHE, Grayson MN. Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction. Chem Sci 2022; 13:7594-7603. [PMID: 35872815 PMCID: PMC9242013 DOI: 10.1039/d2sc02925a] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 11/21/2022] Open
Abstract
A synergistic approach that combines machine learning with semi-empirical methods enables the fast and accurate prediction of DFT-quality reaction barriers, with mechanistic insights available from semi-empirical transition state geometries.
Collapse
Affiliation(s)
- Elliot H. E. Farrar
- Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
| | - Matthew N. Grayson
- Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, UK
| |
Collapse
|
11
|
Taylor MG, Nandy A, Lu CC, Kulik HJ. Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning. J Phys Chem Lett 2021; 12:9812-9820. [PMID: 34597514 DOI: 10.1021/acs.jpclett.1c02852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.
Collapse
Affiliation(s)
- Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Connie C Lu
- Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
12
|
Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chem Rev 2021; 121:9927-10000. [PMID: 34260198 DOI: 10.1021/acs.chemrev.1c00347] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
Collapse
Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Fang Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Adam H Steeves
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
13
|
Xu Y, Ju CW, Li B, Ma QS, Chen Z, Zhang L, Chen J. Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors. ACS APPLIED MATERIALS & INTERFACES 2021; 13:34033-34042. [PMID: 34269560 DOI: 10.1021/acsami.1c05536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Hydrogen evolution by alternating conjugated copolymers has attracted much attention in recent years. To study alternating copolymers with data-driven strategies, two types of multidimension fragmentation descriptors (MDFD), structure-based MDFD (SMDFD), and electronic property-based MDFD (EPMDFD), have been developed with machine learning (ML) algorithms for the first time. The superiority of SMDFD-based models has been demonstrated by the highly accurate and universal predictions of electronic properties. Moreover, EPMDFD-based, experimental-parameter-free ML models were developed for the prediction of the hydrogen evolution reaction, displaying excellent accuracy (real-test accuracy = 0.91). The combination of explainable ML approaches and first-principles calculations was employed to explore photocatalytic dynamics, revealing the importance of electron delocalization in the excited state. Virtual designing of high-performance candidates can also be achieved. Our work illustrates the huge potential of ML-based material design in the field of polymeric photocatalysts toward high-performance photocatalysis.
Collapse
Affiliation(s)
- Yuzhi Xu
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, College of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Cheng-Wei Ju
- College of Chemistry, Nankai University, Tianjin 300071, China
| | - Bo Li
- Department of Chemistry, School of Science, Tianjin University, Tianjin 300072, China
| | - Qiu-Shi Ma
- School of Resource and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
| | - Zhenyu Chen
- School of Materials Science and Engineering, Nankai University, Tianjin 300350, China
| | - Lianjie Zhang
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, College of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Junwu Chen
- Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, College of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China
| |
Collapse
|