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Chemical design by artificial intelligence

Published:2022-09-28 Issue:12 Volume:157 Page:120401
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Ess Daniel H.¹^ORCID,Jelfs Kim E.²^ORCID,Kulik Heather J.³^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, USA

2. Department of Chemistry, Molecular Sciences Research Hub, 82 Wood Lane, White City Campus, Imperial College London, London W12 0BZ, United Kingdom

3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0123281

Reference29 articles.

1. Conductivity prediction model for ionic liquids using machine learning

2. Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break

3. Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design

4. Clustering a database of optically absorbing organic molecules via a hierarchical fingerprint scheme that categorizes similar functional molecular fragments

5. Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Nanomachines in living matters: the soft-robot cytochrome P450;Trends in Chemistry;2023-10

2. Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential;The Journal of Physical Chemistry A;2023-06-12

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