Inverse Design of Nanoparticles Using Multi‐Target Machine Learning-Reference-Cited by-同舟云学术

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Inverse Design of Nanoparticles Using Multi‐Target Machine Learning

Published:2021-12-07 Issue: Volume: Page:2100414
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Li Sichao¹,Barnard Amanda S.¹^ORCID

Affiliation:

1. School of Computing Australian National University Acton Australian Capital Territory 2601 Australia

Funder

National Computational Infrastructure

Publisher

Wiley

Subject

Multidisciplinary,Modelling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202100414

Reference60 articles.

1. Inverse design in search of materials with target functionalities

2. Inverse molecular design using machine learning: Generative models for matter engineering

3. Recent advances and applications of machine learning in solid-state materials science

4. B.Sanchez‐Lengeling C.Outeiral G. L.Guimaraes A.Aspuru‐Guzik ChemRxiv2017.https://doi.org/10.26434/chemrxiv.5309668.v3.

5. Deep Generative Models for Molecular Science

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2. Inverse design of aluminium alloys using multi-targeted regression;Journal of Materials Science;2024-01

3. Structural classification of Ag and Cu nanocrystals with machine learning;Nanoscale;2024

4. Inverse design for materials discovery from the multidimensional electronic density of states;Journal of Materials Chemistry A;2024

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