TSUKUBA, Japan, December 21, 2018
TSUKUBA, Japan, December 21, 2018 /PRNewswire/ --
International Center for Materials Nanoarchitectonics (MANA), Japan, publishes the December 2018 issue of the MANA E-Bulletin with a featuring article on 'Careers paths at MANA and research on chirality' and research highlights on 'Artificial intelligence learns to predict light-activated molecules'; 'New material that is both a thermoelectric and a superconductor identified by high-throughput materials discovery'; and 'Ionic devices learn how to make decisions'.
December 2018 issue of MANA eBulletin
Careers paths at MANA: Focus on chirality
Independent Scientist, MANA
"My career as a research scientist in Japan started in November 2008 when I joined the Supramolecules Group at MANA for a two JSPS postdoctoral fellowship," says Labuta. "I was really impressed and inspired by the highly focused and passionate approach to research at MANA. I learnt a lot and was able to contribute to the research activities of the group. It was a very fruitful two years for me and the experience led me to pursue a long term career at MANA. I highly recommend this career path to other young researchers overseas."
Caption: Visualization of in vivo chirality mapping of chiral analyte using porphyrintype molecule as a detector agent.
Ionic devices learn how to make decisions
Decision-making processes require examining complex data to adapt to dynamic changes in the environment and make decisions about the most appropriate behavior. Emulating these processes with computers requires enormous resources, so new avenues need to be explored.
Now, Takashi Tsuchiya and colleagues from the National Institute for Materials Science in Tsukuba, Japan, writing in Science Advances propose to use ionic devices to perform decision-making operations. They apply their devices to solve dynamic multiarmed bandit problems, mathematical problems in which a fixed set of resources has to be allocated between alternative choices to maximize the gain, with the properties of each choice only partially known at the beginning. The scenario investigated by the authors is that of a user of busy communication channels who needs to select a channel to transmit information with maximum efficiency.
Concept illustration of making decisions with nanomaterials.
Takashi Tsuchiya, Tohru Tsuruoka, Song-Ju Kim, Kazuya Terabe and Masakazu Aono, Ionic decision-maker created as novel, solid-state devices. Science Advances, 4, eaau2057 (2018).
Artificial intelligence learns to predict light-activated molecules
Artificial intelligence can be used to design new molecules; it is becoming a popular tool because of its potential for discovering molecules in unexplored chemical spaces, its ability to screen a huge number of potential molecules in a short amount of time and its tendency to find unconventional ways of solving problems. However, whether such molecules can be actually synthesized and whether they display the desired functionalities in the real world is an open question.
Masato Sumita, Koji Tsuda and colleagues at different institutions in Japan report in ACS Central Science a proof-of-concept study in which they use a platform based on artificial intelligence to discover light-activated organic molecules (which are relevant in green chemistry and molecular sensing) that can be synthesized and that have specific functional properties. The platform combines a molecule generator powered by artificial intelligence and a calculator based on density functional theory that performs quantum chemical calculations. The generator suggests molecules with different structures, the calculator predicts their properties.
Concept of platform based on artificial intelligence to discover photo-functional organic molecules.
Masato Sumita, Xiufeng Yang, Shinsuke Ishihara, Ryo Tamura, Koji Tsuda, Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies. ACS Cent. Sci. 2018, 4, 1126−1133.
New material that is both a thermoelectric and a superconductor identified by high-throughput materials discovery
Data-driven materials science aims at mining information contained in databases collecting the properties of existing materials to identify entirely new compounds with desired properties. This approach is proving successful, but its application is not straightforward for certain classes of materials, such as thermoelectrics - materials that convert temperature differences into electricity and thus hold promise for energy applications - or superconductors, which can transport currents without dissipation and could be used for power transmission.
Ryo Matsumoto from the Center for Materials Nanoarchitectonics in Tsukuba, Japan, and colleagues used a specific criterion to identify candidate thermoelectric and superconducting materials with high-throughput calculations. Specifically, they looked for materials that in the electronic band structure have bands that are flat near the Fermi level, which separates the occupied and unoccupied electronic states. Indeed, flat bands near the Fermi level are predicted to enhance the thermoelectric properties of materials, and if the flat band crosses the fermi level a superconducting state is realized.
Electronic band structure of materials studied.
Ryo Matsumoto, Zhufeng Hou, Hiroshi Hara, Shintaro Adachi, Hiroyuki Takeya, Tetsuo Irifune, Kiyoyuki Terakura and Yoshihiko Takano, Two pressure-induced superconducting transitions in SnBi2Se4 explored by data-driven materials search: new approach to developing novel functional materials including thermoelectric and superconducting materials. Appl. Phys. Express 11, 093101 (2018)
The International Center for Materials Nanoarchitectonics
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