Christopher Kuenneth

About Christopher Kuenneth

Christopher Kuenneth, With an exceptional h-index of 17 and a recent h-index of 17 (since 2020), a distinguished researcher at Georgia Institute of Technology, specializes in the field of Materials Informatics, Polymer Informatics, Machine Learning, Data-Driven Materials Design.

His recent articles reflect a diverse array of research interests and contributions to the field:

Polymer informatics beyond homopolymers

Amphiphilic zwitterionic bioderived block copolymers from glutamic acid and cholesterol–Ability to form nanoparticles and serve as vectors for the delivery of 6‐mercaptopurine

polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing

Polymer informatics at scale with multitask graph neural networks

Bioplastic design using multitask deep neural networks

Characteristics of Low‐Energy Phases of Hafnia and Zirconia from Density Functional Theory Calculations

Copolymer informatics with multitask deep neural networks

Christopher Kuenneth Information

University

Position

___

Citations(all)

2056

Citations(since 2020)

1780

Cited By

823

hIndex(all)

17

hIndex(since 2020)

17

i10Index(all)

21

i10Index(since 2020)

21

Email

University Profile Page

Google Scholar

Christopher Kuenneth Skills & Research Interests

Materials Informatics

Polymer Informatics

Machine Learning

Data-Driven Materials Design

Top articles of Christopher Kuenneth

Polymer informatics beyond homopolymers

MRS Bulletin

2024/1

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Amphiphilic zwitterionic bioderived block copolymers from glutamic acid and cholesterol–Ability to form nanoparticles and serve as vectors for the delivery of 6‐mercaptopurine

Macromolecular Chemistry and Physics

2023/12

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Richard Hoogenboom
Richard Hoogenboom

H-Index: 51

polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Nature Communications

2023/7/11

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing

npj Computational Materials

2023/4/5

Polymer informatics at scale with multitask graph neural networks

Chemistry of Materials

2023/2/15

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Bioplastic design using multitask deep neural networks

Communications Materials

2022/12/3

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Characteristics of Low‐Energy Phases of Hafnia and Zirconia from Density Functional Theory Calculations

physica status solidi (RRL)–Rapid Research Letters

2022/10

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Copolymer informatics with multitask deep neural networks

Macromolecules

2021/6/29

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Polymer informatics with multi-task learning

Patterns

2021/4/9

Polymer informatics: Current status and critical next steps

2021/4/1

Lihua Chen
Lihua Chen

H-Index: 14

Chiho Kim
Chiho Kim

H-Index: 17

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

A massive dataset of synthesis-friendly hypothetical polymers

APS March Meeting Abstracts

2021

Chiho Kim
Chiho Kim

H-Index: 17

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Deepak Kamal
Deepak Kamal

H-Index: 3

An efficient deep learning scheme to predict the electronic structure of materials and molecules: The example of graphene-derived allotropes

The Journal of Physical Chemistry A

2020/11/3

Christopher Kuenneth
Christopher Kuenneth

H-Index: 12

Neural Network Based Molecular Dynamics to Study Polymers

Bulletin of the American Physical Society

2020

See List of Professors in Christopher Kuenneth University(Georgia Institute of Technology)

Co-Authors

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