Machine-learning-assisted prediction of the size of microgels prepared by aqueous precipitation polymerization.

The size of soft colloids (microgels) is essential; however, control over their size has typically been established empirically. Herein, we report a linear-regression model that can predict microgel size using a machine learning method, sparse modeling for small data, which enables the determination of the synthesis conditions for target-sized microgels.

Complex energies of the coherent longitudinal optical phonon-plasmon coupled mode according to dynamic mode decomposition analysis.

In a dissipative quantum system, we report the dynamic mode decomposition (DMD) analysis of damped oscillation signals. We used a reflection-type pump-probe method to observe time-domain signals, including the coupled modes of long-lived longitudinal optical phonons and quickly damped plasmons (LOPC) at various pump powers. The Fourier transformed spectra of the observed damped oscillation signals show broad and asymmetric modes, making it difficult to evaluate their frequencies and damping rates.

Yield-prediction models for efficient exfoliation of soft layered materials into nanosheets.

Yield-prediction models were studied for efficient exfoliation of soft layered materials stacked via van der Waals interactions with the assistance of machine learning on small experimental data. High-yield exfoliation of graphite and layered organic polymer was achieved under the conditions guided by the models in a limited number of experiments.

Lateral-size control of exfoliated transition-metal-oxide 2D materials by machine learning on small data.

A wide variety of nanosheets including monolayers and few-layers have attracted much interest as two-dimensional (2D) materials for the unique anisotropic structures and properties. On the other hand, one of the significant remaining and challenging issues is the lateral-size control. Since 2D materials are generally synthesized by the exfoliation of layered materials, the lateral size is not easily controlled in the breaking-down processes.

Normal mode analysis of a relaxation process with Bayesian inference.

Measurements of relaxation processes are essential in many fields, including nonlinear optics. Relaxation processes provide many insights into atomic/molecular structures and the kinetics and mechanisms of chemical reactions. For the analysis of these processes, the extraction of modes that are specific to the phenomenon of interest (normal modes) is unavoidable.

Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents.

We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We performed quantum chemistry calculations on the coordination energy (Ecoord) of five alkali metal ions (Li, Na, K, Rb, and Cs) to electrolyte solvent, which is intimately related to ion transfer at the electrolyte/electrode interface. Three regression methods, namely, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), were employed to find the relationship between Ecoord and descriptors.

A longitudinal study of infant view-invariant face processing during the first 3-8 months of life.

View-invariant face processing emerges early in life. A previous study (Nakato et al., 2009) measured infant hemodynamic responses to faces from the frontal and profile views in the bilateral temporal areas, which have been reported to be involved in face processing using near-infrared spectroscopy.

Liquid electrolyte informatics using an exhaustive search with linear regression.

Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties.

Estimation of the reaction times in tasks of varying difficulty from the phase coherence of the auditory steady-state response using the least absolute shrinkage and selection operator analysis.

Quantitative estimation of the workload in the brain is an important factor for helping to predict the behavior of humans. The reaction time when performing a difficult task is longer than that when performing an easy task. Thus, the reaction time reflects the workload in the brain.

Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli.

We perceive our surrounding environment by using different sense organs. However, it is not clear how the brain estimates information from our surroundings from the multisensory stimuli it receives. While Bayesian inference provides a normative account of the computational principle at work in the brain, it does not provide information on how the nervous system actually implements the computation.

Theory of correlation in a network with synaptic depression.

Synaptic depression affects not only the mean responses of neurons but also the correlation of response variability in neural populations. Although previous studies have constructed a theory of correlation in a spiking neuron model by using the mean-field theory framework, synaptic depression has not been taken into consideration. We expanded the previous theoretical framework in this study to spiking neuron models with short-term synaptic depression.