Deep Reinforcement Learning for Inverse Inorganic Materials Design

“Models of delayed reinforcement learning”

CJCH Watkins · 1989

Earlier work this paper cites.

“Q-learning”

Christopher Watkins and Peter Dayan · 1992

Earlier work this paper cites.

“Commentary: The Materials Project: A materials genome approach to accelerating materials innovation”

Anubhav Jain et al · 2013

Earlier work this paper cites.

“Playing atari with deep reinforcement learning”

Original

Volodymyr Mnih et al · 2013

Earlier work this paper cites.

“Reinforcement learning for robots using neural networks”, 1993

Long-Ji Lin · 2013

Earlier work this paper cites.

“A general-purpose machine learning framework for predicting properties of inorganic materials”

Logan Ward, Ankit Agrawal, Alok Choudhary and Christopher Wolverton · 2016

Earlier work this paper cites.

“Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC)”, 2017

Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel Guimaraes and Alan Aspuru-Guzik · 2017

Earlier work this paper cites.

“Molecular de-novo design through deep reinforcement learning”

Marcus Olivecrona, Thomas Blaschke, Ola Engkvist and Hongming Chen · 2017

Cited alongside, same era.

“Automatic chemical design using a data-driven continuous representation of molecules”

Rafael Gómez-Bombarelli et al · 2018

Cited alongside, same era.

“Molecular generative model based on conditional variational autoencoder for de novo molecular design”

Jaechang Lim, Seongok Ryu, Jin Kim and Woo Kim · 2018

Cited alongside, same era.

“Deep reinforcement learning for de novo drug design”

Mariya Popova, Olexandr Isayev and Alexander Tropsha · 2018

Cited alongside, same era.

“Elemnet: Deep learning the chemistry of materials from only elemental composition”

Dipendra Jha et al · 2018

Cited alongside, same era.

“Optimization of molecules via deep reinforcement learning”

“Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials”

Yabo Dan et al · 2020

Later among the works it cites.

“The earth mover’s distance as a metric for the space of inorganic compositions”

Cameron Hargreaves et al · 2020

Later among the works it cites.

“Deep learning enabled inorganic material generator”

Yashaswi Pathak et al · 2020

Later among the works it cites.

“Predicting materials properties without crystal structure: Deep representation learning from stoichiometry”

Rhys Goodall and Alpha Lee · 2020

Later among the works it cites.

“Constrained model-free reinforcement learning for process optimization”

Elton Pan et al · 2021

Later among the works it cites.

“TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery”