Using GNN property predictors as molecule generators

no code implementations • 5 Jun 2024 • Félix Therrien, Edward H. Sargent, Oleksandr Voznyy

In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties.

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

1 code implementation • 17 Jun 2022 • Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Felix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.

BIG-bench Machine Learning Property Prediction

Interpretable discovery of new semiconductors with machine learning

no code implementations • 12 Jan 2021 • Hitarth Choubisa, Petar Todorović, Joao M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy, Isaac Tamblyn, Edward Sargent

To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations.

BIG-bench Machine Learning Knowledge Distillation