scholarly journals Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yangyang Wan ◽  
Fernando Ramirez ◽  
Xu Zhang ◽  
Thuc-Quyen Nguyen ◽  
Guillermo C. Bazan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structural Features ◽  
Hole Transport ◽  
Molecular Structures ◽  
Trial And Error ◽  
First Principles Calculations ◽  
Structure Property ◽  
Conjugated Polyelectrolytes ◽  
Structure Property Relationships ◽  
Photocatalytic Applications

AbstractConjugated polyelectrolytes (CPEs), comprised of conjugated backbones and pendant ionic functionalities, are versatile organic materials with diverse applications. However, the myriad of possible molecular structures of CPEs render traditional, trial-and-error materials discovery strategy impractical. Here, we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles calculations. We systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are established. By means of machine learning, we uncover structural features crucial to the CPE properties, and these features are then used as descriptors in the machine learning to predict the properties of unknown CPEs. Lastly, we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting. Our work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

scholarly journals 1,3-Diketone Calix[4]arene Derivatives—A New Type of Versatile Ligands for Metal Complexes and Nanoparticles

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1214
Author(s):  
Sergey N. Podyachev ◽  
Rustem R. Zairov ◽  
Asiya R. Mustafina
Keyword(s):  
Structural Diversity ◽  
Building Blocks ◽  
Structural Features ◽  
Structure Property ◽  
One Pot ◽  
Arene Ligands ◽  
Structure Property Relationships ◽  
New Type ◽  
Synthetic Routes ◽  
Magnetic Resonance Imaging Mri

The present review is aimed at highlighting outlooks for cyclophanic 1,3-diketones as a new type of versatile ligands and building blocks of the nanomaterial for sensing and bioimaging. Thus, the main synthetic routes for achieving the structural diversity of cyclophanic 1,3-diketones are discussed. The structural diversity is demonstrated by variation of both cyclophanic backbones (calix[4]arene, calix[4]resorcinarene and thiacalix[4]arene) and embedding of different substituents onto lower or upper macrocyclic rims. The structural features of the cyclophanic 1,3-diketones are correlated with their ability to form lanthanide complexes exhibiting both lanthanide-centered luminescence and magnetic relaxivity parameters convenient for contrast effect in magnetic resonance imaging (MRI). The revealed structure–property relationships and the applicability of facile one-pot transformation of the complexes to hydrophilic nanoparticles demonstrates the advantages of 1,3-diketone calix[4]arene ligands and their complexes in developing of nanomaterials for sensing and bioimaging.

MOLECULAR MODELING OF THERMAL AND MECHANICAL PROPERTIES OF ELASTOMERS: A REVIEW

2013 ◽  
Vol 86 (3) ◽  
pp. 401-422 ◽  
Author(s):  
Kshitij C. Jha ◽  
Mesfin Tsige
Keyword(s):  
Microphase Separation ◽  
Material Design ◽  
Trial And Error ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Network Building ◽  
High Structural Stability ◽  
Micro Level ◽  
Architecture Modeling ◽  
Work Done

ABSTRACT Elastomers have varied applications from adhesives, sealants, encapsulants, and coatings to specialty usage in electronics, aviation, optical, and communications industries due to their high structural stability. In addition, more and more biological applications of elastomeric compounds are gaining ground, particularly in mimetic architecture. Modeling and simulation provide tools by which the interactions leading to various structure–property relationships can be explored at the micro level. An understanding of these processes could cut down on the extensive and expensive trial-and-error experiments as well as provide a benchmark for material design. This review article explores the work done by different groups, especially at the molecular level, to model the properties of both thermoplastic and thermoset elastomers. Each presents its own challenges and solutions: from microphase separation to network building and force field parameterization. The results of these modeling efforts along with the challenges are presented in this review work.

scholarly journals Classification of Platinum Nanoparticle Catalysts using Machine Learning

2020 ◽  
Author(s):  
Amanda J. Parker ◽  
George Opletal ◽  
Amanda Barnard
Keyword(s):  
Machine Learning ◽  
Hydrogen Oxidation ◽  
Pt Nanoparticles ◽  
Structure Property ◽  
Metallic Nanoparticle ◽  
Platinum Nanoparticle ◽  
Nanoparticle Catalysts ◽  
Structure Property Relationships ◽  
Multi Stage

Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab.

Thermotropic Chiral Nematic Polymers as Optical Materials

1989 ◽  
Vol 175 ◽  
Author(s):  
S.H. Chen ◽  
M.L. Tsai ◽  
S.D. Jacobs
Keyword(s):  
Optical Materials ◽  
Helical Structure ◽  
Structural Features ◽  
Optically Active ◽  
Structure Property ◽  
Chiral Compound ◽  
Structure Property Relationships ◽  
Chiral Nematic ◽  
Chiral Structures ◽  
Nematic Polymers

AbstractChiral nematic copolymers based on optically active cholesterol, dihydrocholesterol, (R)-(+)- and (S)-(−)-1-phenylethylamine, and (+)- and (−)- isopinocampheol were synthesized and characterized for the investigations of thermotropic and optical properties. Although helical sense does not appear to correlate with the sign of [α]D of the precursor chiral compound as suggested by the observations of cholesteryl and dihydrocholesteryl copolymers, the inversion of chirality in the pendant group, (R)-(+)- vs (S)-(−)-1-phenylethylamine, does lead to the opposite handedness in the resultant helical structure. To better understand the structure-property relationships involving helical sense and twisting power, systematic studies of the roles played by both nematogenic and chiral structures as well as other structural features of the comonomers should be conducted.

Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design

2012 ◽  
Vol 1425 ◽  
Author(s):  
Michael P. Krein ◽  
Bharath Natarajan ◽  
Linda S. Schadler ◽  
L. C. Brinson ◽  
Hua Deng ◽  
...  
Keyword(s):  
A Priori ◽  
Molecular Structures ◽  
Work Of Adhesion ◽  
Materials Informatics ◽  
Structure Property ◽  
Surface Tensions ◽  
Virtual Experiments ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Informatics Tools

ABSTRACTPolymer nanocomposites (PNC) are complex material systems in which the dominant length scales converge. Our approach to understanding nanocomposite tradespace uses Materials Quantitative Structure-Property Relationships (MQSPRs) to relate molecular structures to the polar and dispersive components of corresponding surface tensions. If the polar and dispersive components of surface tensions in the nanofiller and polymer could be determined a priori, then the propensity to aggregate and the change in polymer mobility near the particle could be predicted. Derived energetic parameters such as work of adhesion, work of spreading and the equilibrium wetting angle may then used as input to continuum mechanics approaches that have been shown able to predict the thermomechanical response of nanocomposites and that have been validated by experiment. The informatics approach developed in this work thus enables future in silico nanocomposite design by enabling virtual experiments to be performed on proposed nanocomposite compositions prior to fabrication and testing.

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