Paper Info
Title
The Open Catalyst Challenge 2021: Competition Report.
Abstract
In this report, we describe the Open Catalyst Challenge held at NeurIPS 2021, focusing on using machine learning (ML) to accelerate the search for low-cost catalysts that can drive reactions converting renewable energy to storable forms. Specifically, the challenge required participants to develop ML approaches for relaxed energy prediction, i.e. given atomic positions for an adsorbate-catalyst system, the goal was to predict the energy of the system’s relaxed or lowest energy state. To perform well on this task, ML approaches need to approximate the quantum mechanical computations in Density Functional Theory (DFT). By modeling these accurately, the catalyst’s impact on the overall rate of a chemical reaction may be estimated; a key factor in filtering potential electrocatalyst materials. The challenge encouraged community-wide progress on this task and the winning approach improved direct relaxed energy prediction by 15% relative over the previous state-of-the-art.
Year
Venue
DocType
2021
Annual Conference on Neural Information Processing Systems
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
25
Name
Order
Citations
PageRank
Abhishek Das143323.54
Muhammed Shuaibi200.34
Aini Palizhati300.34
Siddharth Goyal400.68
Aditya Grover500.34
Adeesh Kolluru600.34
Janice Lan700.68
Ammar Rizvi800.34
Anuroop Sriram900.34
Brandon M. Wood1000.68
Devi Parikh112929132.01
Zachary W. Ulissi1242.51
C. Lawrence Zitnick137321332.72
Guolin Ke1400.34
Shuxin Zheng1543.10
Yu Shi1600.34
Di He1715419.76
Tie-yan Liu184662256.32
Chengxuan Ying1900.34
Jiacheng You2000.34
Yihan He2100.34
Rostislav Grigoriev2200.34
Ruslan Lukin2300.34
Adel Yarullin2400.34
Max Faleev2500.34