1 citations · 1 across the 4 of their papers we have counts for
7 papers
Comparing Forward and Inverse Design Paradigms: A Case Study on Refractory High-Entropy Alloys
Arindam Debnath, Lavanya Raman, Wenjie Li +7
The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates t…
Investigating representation schemes for surrogate modeling of High Entropy Alloys
Arindam Debnath, Wesley F Reinhart
The design of new High Entropy Alloys that can achieve exceptional mechanical properties is presently of great interest to the materials science community. However, due to the diff…
Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks
Debjyoti Bhattacharya, Devon C. Kleeblatt, Antonia Statt +1
Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structur…
Generative deep learning as a tool for inverse design of high-entropy refractory alloys
Arindam Debnath, Adam M. Krajewski, Hui Sun +9
Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages…
Unsupervised learning of sequence-specific aggregation behavior for a model copolymer
Antonia Statt, Devon C. Kleeblatt, Wesley F. Reinhart
We apply a recently developed unsupervised machine learning scheme for local atomic environments to characterize large-scale, disordered aggregates formed by sequence-defined macro…
Unsupervised learning of atomic environments from simple features
Wesley F. Reinhart
I present a strategy for unsupervised manifold learning on local atomic environments in molecular simulations based on simple rotation- and permutation-invariant three-body feature…