Enumeration of de novo inorganic complexes for chemical discovery and machine learning-Reference-Cited by-同舟云学术

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Published:2020 Issue:1 Volume:5 Page:139-152
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Gugler Stefan¹²³⁴^ORCID,Janet Jon Paul¹²³⁴^ORCID,Kulik Heather J.¹²³⁴^ORCID

Affiliation:

1. Department of Chemical Engineering

2. Massachusetts Institute of Technology

3. Cambridge

4. USA

Abstract

Enumerated, de novo transition metal complexes have unique spin state properties and accelerate machine learning model training.

Funder

American Association for the Advancement of Science

Burroughs Wellcome Fund

Office of Naval Research Global

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2020/ME/C9ME00069K

Reference112 articles.

1. AFLOW: An automatic framework for high-throughput materials discovery

2. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

3. molSimplify: A toolkit for automating discovery in inorganic chemistry

4. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Cited by 27 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. Molecular relaxation by reverse diffusion with time step prediction;Machine Learning: Science and Technology;2024-08-06

2. Advancing Molecular-Scale Logic Devices through Multistage Switching in a Luminescent Bimetallic Ru(II)-Terpyridine Complex;Inorganic Chemistry;2024-08-02

3. The future of bone regeneration: Artificial intelligence in biomaterials discovery;Materials Today Communications;2024-08

4. Directional multiobjective optimization of metal complexes at the billion-system scale;Nature Computational Science;2024-03-29

5. Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design;ACS Catalysis;2023-04-12