Open Catalyst Project:
The Open Catalyst Project is a collaborative research effort between Facebook AI Research (FAIR) and Carnegie Mellon University’s (CMU) Department of Chemical Engineering, and aims to develop new Machine Learning methods and models to accelerate the catalyst simulation process for renewable energy technologies and improve our ability to predict activity/selectivity across catalyst composition.
To achieve that in the short term, we need participation from the Machine Learning community in solving key challenges in catalysis. In this talk, we will first describe the OC20 dataset – a dataset of 134M training points coming from ~1.5M DFT relaxations across 53 elements and their binary/ternary materials, various low-index facets, and adsorbates spanning 56 common reaction intermediates with relevance to carbon, oxygen, and nitrogen thermal and electrochemical reactions. We will then describe some of our most recent modeling improvements on the OC20 dataset, specifically Graph Parallel (published at ICLR 2022) and GemNet-OC (under review), which are the current state-of-the-art on this dataset.
Webinar Sessions
Tuesday, April 26th: 10:00 AM PDT (USA) 1:00 PM EDT (USA) 19:00 CEST (EUROPE) Live Q&A Wednesday, April 27th: 07:00 AM PDT (USA) 10:00 AM EDT (USA) 15:00 BST (EUROPE) 16:00 CEST (EUROPE) 19:30 IST (INDIA) Thursday, April 28th: 08:00 CEST (EUROPE) 11:30 IST (INDIA) 14:00 CST (CHINA) 15:00 JST (JAPAN)
*Registrations will also include a link to the recording and slides after the live Q&A session ends.
Abhishek Das, Ph.D
Abhishek Das is a Research Scientist at Facebook AI Research (FAIR). His current research focuses on building deep learning models for accelerating electrocatalyst discovery for renewable energy storage as part of the Open Catalyst Project.
He was awared his PhD in Computer Science at Georgia Institute of Technology, where his thesis focused on deep learning and its applications in building agents that can see (computer vision), talk (language modeling), and act (reinforcement learning), and won a honorable mention for the 2020 AAAI/ACM SIGAI Doctoral Dissertation Award. During and prior to his PhD, he has held visiting research positions at Queensland Brain Institute, Virginia Tech, Facebook AI Research, DeepMind, and Tesla Autopilot. He graduated from Indian Institute of Technology Roorkee in 2015 with a Bachelor's degree in Electrical Engineering. He has published at distinguished conferences -- CVPR, ICCV, ICML, ICLR, IJCAI, CoRL, ECCV, EMNLP, ICASSP -- and journals -- IJCV, PAMI, CVIU. He is a recipient of graduate fellowships from Facebook, Adobe, Snap and top reviewer awards at CVPR and NeurIPS.
Anuroop Sriram, MS
Anuroop Sriram is a Research Engineer at Facebook AI Research (FAIR), working on applying machine learning to mitigating climate change. At FAIR, he has previously worked on machine learning for medical imaging and speech recognition. Prior to this, he was a Research Scientist at Baidu Research, where he worked on large scale speech recognition.
Anuroop has a Master of Science degree in Language Technologies from Carnegie Mellon University. He has published in top tier conferences such as NeurIPS, ICLR, CVPR, Interspeech, ICASSP and MICCAI, as well as journals such as Radiology: AI, ACS Catalysis, and AJR.