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Poly(3-alkylthiophenes): Controlled Manipulation of Microstructure and its Impact on Charge Transport
Published in John R. Reynolds, Barry C. Thompson, Terje A. Skotheim, Conjugated Polymers, 2019
Michael McBride, Guoyan Zhang, Martha Grover, Elsa Reichmanis
The vast amount of accumulated knowledge on the family of poly(3-alkylthiophene) polymers provides an unparalleled opportunity to elucidate informative process–structure–property relationships relevant to the behavior of conjugated polymers in general. Structural features and properties described above display a strong dependence on polymer molecular weight, polydispersity, regioregularity, solvent choice and processing history. Advances in ‘Big Data’ and materials informatics techniques are only just beginning to leverage existing knowledge to identify promising regions for future experimentation. Developing a complete understanding of P3AT polymers can aid in the rapid advancement of novel conjugated polymer systems.
Polymeric Biomaterials and Current Trends for Advanced Applications
Published in Anandhan Srinivasan, Selvakumar Murugesan, Arunjunai Raj Mahendran, Progress in Polymer Research for Biomedical, Energy and Specialty Applications, 2023
Vineeth M. Vijayan, Suja Mathai, Vinoy Thomas
Machine learning and artificial intelligence are two significant future directions in the development of polymeric biomaterials. In the future, it is likely to play a significant role. Machine learning and artificial intelligence will mostly be used to identify and create next-generation bioinspired polymeric materials. With the most efficient digital, high-throughput techniques, it is projected to increase the design space of new interesting polymeric biomaterials.119 The machine learning and artificial intelligence assisted material development contains a virtual space which is mainly comprised of materials informatics, artificial intelligence, and computational modeling and simulations.120 The valuable information's derived from this virtual space will be applied in to the real experimental material development space which comprises of different elements such as experimental synthesis, advanced manufacturing, and inspiration from nature. This synergistic combinatory approach of virtual space (offered by the machine learning and artificial intelligence) and real experimental space can greatly enhance the development of new exciting polymeric biomaterials.121 There are different computational modeling of soft biomaterials such as density functional theory, fully atomistic molecular dynamics methods, coarse-grained molecular dynamics methods, and macroscale modeling and simulations are currently employed for this purpose.119 These different approaches may revolutionize the development of soft polymeric biomaterials in the future. Even though it has enormous potential to accelerate the polymeric material synthesis space, certain challenges must be addressed, such as (i) taking into account the increased accuracy and efficiency of DFT functionals and MD forcefields, (ii) developing a common framework for easily accessible soft materials databases, and (iii) continuing to develop emerging techniques in artificial intelligence.119 The coming decade may witness many approaches that are capable of addressing these different challenges in machine learning and artificial intelligence towards the development of different polymeric biomaterials.
Sparse modeling of chemical bonding in binary compounds
Published in Science and Technology of Advanced Materials, 2019
Yosuke Kanda, Hitoshi Fujii, Tamio Oguchi
Recently, data-intensive scientific discovery and design have been the focus of great attention for the acceleration of research and development in materials science, being widely called materials informatics (MI). The major aims of MI are the exploration of new materials with desired properties, the optimization of existing materials for particular performances, and the understanding of underlying physical mechanisms for further development. Generally, if one demands high predictability for a model constructed by data-science machine-learning techniques, complicated methods using non-linear models such as kernel ridge regression [1], neural network [2], and random forest [3] are appropriate, though their interpretation becomes troublesome because of the non-linearity involved in the modeling. On the other hand, simple modeling such as linear regression with interpretable descriptors is suitable for extracting intuitive understanding from materials data at the sacrifice of its predictability to a certain degree. Sparse modeling [4] is the statistical learning technique to realize such a simple model by the selection and reduction of the descriptors assumed.
Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials
Published in Science and Technology of Advanced Materials, 2019
Yukari Katsura, Masaya Kumagai, Takushi Kodani, Mitsunori Kaneshige, Yuki Ando, Sakiko Gunji, Yoji Imai, Hideyasu Ouchi, Kazuki Tobita, Kaoru Kimura, Koji Tsuda
Recently, by combining first-principles calculations and experimental data, materials informatics has emerged. The leading example is Citrination thermoelectrics recommendation engine [11], which by machine learning assists the users in the selection of good parent compounds of thermoelectric materials. As calculation data, they used the TE Design Lab database [7], which contains electronic structure parameters of over 2300 parent compounds. As experimental data, they used several experimental databases including UCSB Thermoelectrics Data (MRL Datamining Chart/Energy Materials Datamining) [12], which contains experimental data of about 300 samples (over 1000 data points at 4 different T) of thermoelectric materials reported in over 100 publications.
Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials
Published in Science and Technology of Advanced Materials, 2019
Yukari Katsura, Masaya Kumagai, Takushi Kodani, Mitsunori Kaneshige, Yuki Ando, Sakiko Gunji, Yoji Imai, Hideyasu Ouchi, Kazuki Tobita, Kaoru Kimura, Koji Tsuda
Recently, by combining first-principles calculations and experimental data, materials informatics has emerged. The leading example is Citrination thermoelectrics recommendation engine[11], which by machine learning assists the users in the selection of good parent compounds of thermoelectric materials. As calculation data, they used the TE Design Lab database[7], which contains electronic structure parameters of over 2,300 parent compounds. As experimental data, they used several experimental databases including UCSB Thermoelectrics Data (MRL Datamining Chart/Energy Materials Datamining)[12], which contains experimental data of about 300 samples (over 1,000 data points at 4 different T) of thermoelectric materials reported in over 100 publications.