Area of research
Materials Chemistry · Electrical and Electronic Engineering
Research interest
Research interests include Computer science, Materials science, Bayesian optimization, Perovskite (structure), Workflow, and Throughput.
Autonomous experiments using active learning and AI
Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials
Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing
Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations
Two-step machine learning enables optimized nanoparticle synthesis
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
A data fusion approach to optimize compositional stability of halide perovskites
Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites
AI Applications through the Whole Life Cycle of Material Discovery
Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
The realistic energy yield potential of GaAs-on-Si tandem solar cells: a theoretical case study