Generative deep learning as a tool for inverse design of high-entropy refractory alloys

Debnath, Arindam; Krajewski, Adam M.; Lin, Shuo; Ahn, Marcia; Li, Wenjie; Priya, Shanshank; Singh, Jogender; Shang, Shun‐Li; Beese, Allison M.; Liu, Zhe; Reinhart, Wesley F.

doi:10.48550/arxiv.2108.12019

Cited by 2 publications

(2 citation statements)

References 36 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…34 Deep generative frameworks employing neural networks (NNs) have proven to be an invaluable tool in this context. 44 Notably, these comprise a large zoo of approaches, including variational autoencoders (VAEs), 34−36,39,42,45−47 adversarial networks (GANs), 37,38,[41][42][43]48,49 and reinforcement learning (RL). 50,51 Given this wide range of approaches, it is a priori difficult to decide which method should be used for a new inverse design task.…”

Section: Introductionmentioning

confidence: 99%

Assessing Deep Generative Models in Chemical Composition Space

et al. 2022

View full text Add to dashboard Cite

The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing Deep Generative Models in Chemical Composition Space

et al. 2022

View full text Add to dashboard Cite

show abstract

“…34 In the above examples, deep generative frameworks employing neural networks (NNs) have proven to be an invaluable tool. 44 Notably, these comprise a large zoo of approaches, including variational autoencoders (VAEs), [34][35][36]39,42,[45][46][47] generative adversarial networks (GANs) 37,38,[41][42][43]48,49 and reinforcement learning (RL). 50,51 Given this wide range of approaches it is a priori difficult to decide which method should be used for a new inverse design task.…”

Section: Introductionmentioning

confidence: 99%

Assessing Deep Generative Models in Chemical Composition Space

Türk

Landini

Künkel

et al. 2022

Preprint

View full text Add to dashboard Cite

The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table, since even a single crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning (ML) models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on Reinforcement Learning (RL), Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2~million main group Elpasolites, the precision, coverage and diversity of the generated materials is rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed.

show abstract

Generative deep learning as a tool for inverse design of high-entropy refractory alloys

Cited by 2 publications

References 36 publications

Assessing Deep Generative Models in Chemical Composition Space

Assessing Deep Generative Models in Chemical Composition Space

Assessing Deep Generative Models in Chemical Composition Space

Contact Info

Product

Resources

About