Experiment‐Oriented Materials Informatics for Efficient Exploration of Design Strategy and New Compounds for High‐Performance Organic Anode

Abstract

High-performance organic energy storage has attracted much interest as a future battery. Organic anode has been developed as an alternate of graphite in the past decade. However, the design strategies are not fully studied for further development. The present work shows experiment-oriented materials informatics (MI) for efficient exploration of design strategy and new compounds for an active material of high-performance organic anode. A few important factors to achieve high specific capacity are extracted from training dataset containing experimentally measured specific capacity, calculation results, and literature data of the model compounds using sparse modeling, an informatics approach. Although the prediction model is not sufficiently accurate, the model assists in exploration of new compounds in combination with experience and intuition of experimental scientists. New compounds with high specific capacity, such as 227 mA h g -1 at 100 mA g -1 for benzo[1,2-b:4,5-b′]dithiophene (BdiTp), are efficiently discovered in a minimum number of experiments. Furthermore, polymerization of BdiTp exhibits the enhanced performances, such as 933 mA h g -1 at 20 mA g -1 and cycle stability, and rate performance. MI combined with experiment, calculation, and data accelerates design new materials and functions by experimental scientists having their small data, experience, and intuition.

Nitroaromatics as High‐Energy Organic Cathode Materials for Rechargeable Alkali‐Ion (Li+, Na+, and K+) Batteries

“…Nitro was employed previously as an electron-withdrawing substituting group to modify electrochemical properties of organic electrode materials. [8,43,44] It was found electrochemically active with the ability to accept two electrons each group to form a dianion. [8,45,46] However, the nitro group in several aromatic compounds was revealed to undergo an irreversible electrochemical reduction in the first discharge, rendering redox-reversible azo groups responsible for subsequent redox reactions.…”

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

“…[51][52][53] Our group has focused on combination of sparse modeling and chemical perspective for utilization of small data. [41,43,44,[54][55][56] In sparse modeling, the whole of high-dimensional data are explained by a limited number of strongly correlated factors, namely descriptors, on the assumption of the sparseness. [57,58] In materials science, it means that the target materials and properties can be mainly predicted by a limited number of the descriptors, such as experimental parameters and structural factors.…”

“…Therefore, combination of sparse modeling and our perspective facilitates extraction of a limited number of interpretable descriptors for construction of a straightforward prediction model. [43,44,54] In the present work, the combination approach was applied to predict the lateral-size distribution of the exfoliated nanosheets (Figure 1). The descriptors of the lateral-size distribution were extracted by combination of machine learning and chemical perspective on the small experimental data (Figure 1c).…”

Efficient Syntheses of 2D Materials from Soft Layered Composites Guided by Yield Prediction Model: Potential of Experiment‐Oriented Materials Informatics

“…Our group has studied sparse modeling, a data-scientific method, for small-scale experimental data. [29,52] Sparse modeling focuses on sparseness of high-dimensional data. [53][54][55] It means that whole of the target behavior can be explained by a small number of strongly correlated factors, namely descriptors.…”

“…We have applied sparse modeling to materials design and synthesis by the following two steps. [29,52] The descriptors are initially explored in a large number of potential explanatory variables by sparse modeling using machine learning. Then, a small number of the important descriptors are extracted on the basis of chemical perspective by researchers to construct simple prediction model.…”