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ML4Materials from Molecules to Materials |
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Workshop @ ICLR '23, Fully Virtual, Kigali Rwanda |
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Many of the world's most crucial challenges, such as access to renewable energy, energy storage, or clean water, are currently fundamentally bottlenecked by materials challenges. The discovery of new materials drives the development of key technologies like solar cells, batteries, and catalysis. Machine learning has significantly impacted the modeling of drug-like molecules and proteins, including the discovery of new antibiotics and the accurate prediction of 3D protein structures. Geometric deep learning methods, in particular, have made tremendous progress in modeling atomic structures and are a promising direction for solving open problems in computational materials science.
While there has been growing interest in materials discovery with machine learning, the specific modeling challenges posed by materials have been largely unknown to the broader community. In particular, compared with the domain of drug-like molecules and proteins, the modeling of materials has the two major challenges outlined below.
First, materials-specific inductive biases are needed to develop successful ML models. For example, materials often don't have a handy representation, like 2D graphs for molecules or sequences for proteins. Moreover, most materials are found in the condensed phase. This means they need to be represented under periodic boundary conditions, introducing challenges to both representation learning and generative models.
Second, there exists a broad range of interesting materials classes, such as inorganic crystals, polymers, catalytic surfaces, nanoporous materials, and more. Each class of materials demands a different approach to represent their structures and new tasks/data sets to enable rapid ML developments.
This workshop aims at bringing together the community to discuss and tackle these two types of challenges. In session A, we will feature speakers to discuss the latest progress in developing ML models for materials focusing on algorithmic challenges, covering topics like geometric deep learning and generative models. In particular, what can we learn from the more developed field of ML for molecules and proteins, and where might challenges differ and opportunities for novel developments lie? In session B, we will feature speakers to discuss unique challenges for each sub-field of materials design and how to define meaningful tasks that are relevant to the domain, covering areas including inorganic materials, polymers, nanoporous materials, and catalysis. More specifically, what are the key materials design problems that ML can help tackle?
09:00 - 09:10 Opening Remark
09:10 - 09:40 Invited Talk, Boris Kozinsky
09:40 - 10:10 Invited Talk, Marivi Fernandez-Serra
Machine learning approaches to improve the exchange and correlation functional in Density functional Theory
10:10 - 10:30 Break
10:30 - 11:00 Invited Talk, Tess Smidt
Harnessing the properties of equivariant neural networks to understand and design materials
11:00 - 11:30 Invited Talk, Andrew Ferguson
Machine learning-guided directed evolution of functional proteins
11:30 - 12:00 Spotlight Talks
JAX-XC: Exchange Correlation Functionals Library in Jax
Crystal Structure Prediction by Joint Equivariant Diffusion on Lattices and Fractional Coordinates
Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Material Science Benchmark and ...
12:00 - 13:00 Poster Session 1
13:00 - 13:30 Break
13:30 - 14:00 Invited Talk, Yousung Jung
Machine learning to generate molecules and materials and their synthesis predictions
14:00 - 14:30 Invited Talk, Rafael Gomez-Bombarelli
14:30 - 14:50 Break
14:50 - 15:20 Invited Talk, Shyue Ping Ong
A potential of everything
15:20 - 15:50 Invited Talk, Zachary Ulissi
Open datasets/models in catalysis: recent progress their use to massively accelerate adsorption energy workflows
15:50 - 16:00 Break
16:00 - 17:00 Panel Discussion
Boris Kozinsky · Tess Smidt · Rafael Gomez-Bombarelli · Marivi Fernandez-Serra · Zachary Ulissi · Shyue Ping Ong ·
Yousung Jung · Andrew Ferguson
17:00 - 18:00 Poster Session 2
18:00 - 18:10 Closing Remark
Maksim Zhdanov; Andrey Zhdanov
Gang Liu; Meng Jiang
Seojin Kim; Jaehyun Nam; Junsu Kim; Hankook Lee; Sungsoo Ahn; Jinwoo Shin
Shuan Chen; Yousung Jung
KISHALAY DAS; Bidisha Samanta; Pawan Goyal; Seung-Cheol Lee; Satadeep Bhattacharjee; Niloy Ganguly
Eyal Rozenberg; Ehud Rivlin; Daniel Freedman
Aleksandr Dekhovich; Ozgur Taylan Turan; Jiaxiang Yi; Miguel Anibal Bessa
Rui Jiao; Wenbing Huang; Peijia Lin; Jiaqi Han; Pin Chen; Yutong Lu; Yang Liu
Jiri Hostas; Maicon Pierre Lourenço; John Garcia; Hatef Shahmohamadi; Alain Tchagang; Karthik Shankar; Venkataraman Thangadurai; Dennis R. Salahub
Han Qi; Stefano Rando; Xinyang Geng; Iku Ohama; Aviral Kumar; Sergey Levine
Xuxi Chen; Tianlong Chen; Everardo Yeriel Olivares; Kate Elder; Scott McCall; Aurelien Perron; Joseph McKeown; Bhavya Kailkhura; Zhangyang Wang; Brian Gallagher
Kunhao Zheng; Min Lin
Namkyeong Lee; Heewoong Noh; Sungwon Kim; Dongmin Hyun; Gyoung S. Na; Chanyoung Park
Namid Stillman; Silke Henkes; Roberto Mayor; Gilles Louppe
Janosh Riebesell; Rhys Goodall; Anubhav Jain; Kristin Persson; Alpha Lee
Hilary Egan; Davi Marcelo Febba; Andriy Zakutayev
Sebastian Larsen; Paul A. Hooper
Sumukh Vasisht Shankar; Darrel D'Souza; Jonathan P Singer; Robin Walters
Eyal Rozenberg; Aviv Karnieli; Ofir Yesharim; Joshua Foley-Comer; Sivan Trajtenberg-Mills; Sarika Mishra; Shashi Prabhakar; Ravindra Pratap Singh; Daniel Freedman; Alex M. Bronstein; Ady Arie
Prashant Govindarajan; Santiago Miret; Jarrid Rector-Brooks; Mariano Phielipp; Janarthanan Rajendran; Sarath Chandar
Tyler H Chang; Jakob R Elias; Stefan M. Wild; Santanu Chaudhuri; Joseph A. Libera
Xianjun Yang; Stephen Wilson; Linda Petzold
Daniel Wines; Kevin F Garrity; Tian Xie; Kamal Choudhary
Yuliia Orlova; Gavin Keith Ridley; Frederick Zhao; Rafael Gomez-Bombarelli
Bruno Alves Ribeiro; Joao Alves Ribeiro; Faez Ahmed; Hugo Penedones; Jorge Belinha; Luís Sarmento; Miguel Bessa; Sérgio Tavares
Vahe Gharakhanyan; Max Shirokawa Aalto; Aminah Alsoulah; Nongnuch Artrith; Alexander Urban
