Cheng-Wei Ju

University of Chicago

United States

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Articles (11)

This review explores machine learning's role in energy chemistry, spanning organic photovoltaics, perovskites, catalysis, and batteries, highlighting its potential to accelerate eco-friendly, sustainable energy development.

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2023

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2023

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2022

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2022

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2021

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2021

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2021

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2021

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2021

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2021

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2021

Collaborators (3)

Zhou Lin

Assistant Professor

University of Massachusetts Amherst

UNITED STATES

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Lucas Frédéric

Maître de Conférence (Assistant Professor)

FRANCE

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Grégory Pieters

FRANCE

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Cheng-Wei Ju

Research Interests

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Articles (11)

Machine learning in energy chemistry: introduction, challenges and perspectives

Machine learning in energy chemistry: introduction, challenges and perspectives

m7G-quant-seq: Quantitative Detection of RNA Internal N7-Methylguanosine

Nickel(0)-catalyzed divergent reactions of silacyclobutanes with internal alkynes

Stacked Ensemble Machine Learning for Range-Separation Parameters

Amplification of Dissymmetry Factors in π-Extended [7]- and [9]Helicenes.

A ring expansion strategy towards diverse azaheterocycles

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

A Highly Luminescent Nitrogen-Doped Nanographene as an Acid- and Metal-Sensitive Fluorophore for Optical Imaging

Hot Carrier Transfer in a Graphene/PtSe2 Heterostructure Tuned by a Substrate-Introduced Effective Electric Field

Modular Synthesis of Pentagonal and Hexagonal Ring-Fused NBN-Phenalenes Leading to an Excited-State Aromatization-Induced Structural Planarization Molecular Library

Collaborators (3)

Zhou Lin

Lucas Frédéric

Grégory Pieters

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