Hiromasa Kaneko

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Journal: Journal of chemical information and modeling

Published: June 04, 2026

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Journal: Journal of chemical information and modeling

Published: June 04, 2026

Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: January 25, 2026

Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.

Journal: Molecular informatics

Published: January 22, 2026

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: January 14, 2026

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: January 13, 2026

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: January 08, 2026

Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: December 27, 2025

Adsorption Energy Prediction Model for CO2 Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Journal: Journal of chemical information and modeling

Published: December 19, 2025

Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.

Journal: Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

Published: December 15, 2025

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models.

Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models.

Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.

Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties.

Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties.

Adsorption Energy Prediction Model for CO2 Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Adsorption Energy Prediction Model for CO2 Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.

Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.

Design of a green ammonia production process by machine learning.

Design of a green ammonia production process by machine learning.

Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification.

Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification.