ML4Materials

from Molecules to Materials

Workshop @ ICLR '23, Fully Virtual, Kigali Rwanda


[Video Recording] [ICLR Page] [OpenReview]


Overview

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?


Call for papers


We encourage the community to contribute 4-page extended abstracts to our workshop. Example topics include (but not limited to):

All submissions are required to use the ICLR style file. References and appendices do not count towards the page limit, but reviewers will not be required to read beyond the first 4 pages. All accepted papers will be non-archival. Papers have to be anonymized for review, and submitted through the OpenReview workshop page.

Important dates



Tentative schedule (US ET)


09:00 - 09:10         Opening Remark

09:10 - 09:40         Invited Talk 1

09:40 - 10:10         Invited Talk 2

10:10 - 10:30         Break

10:30 - 11:00         Invited Talk 3

11:00 - 11:30         Invited Talk 4

11:30 - 12:00         Spotlight Talks

12:00 - 13:00         Poster Session 1

13:00 - 13:30         Break

13:30 - 14:00         Invited Talk 5

14:00 - 14:30         Invited Talk 6

14:30 - 14:50         Break

14:50 - 15:20         Invited Talk 7

15:20 - 15:50         Invited Talk 8

15:50 - 16:00         Break

16:00 - 17:00         Panel Discussion

17:00 - 18:00         Poster Session 2

18:00 - 18:10         Closing Remark

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Invited Speakers



Organizing Committee



Advising Committee



Program Committee, Reviewers


We are now actively looking for reviewers/PCs, if you are interested, please fill out this Google form!


Contact


If you have any questions, you can reach out to organizers at ml4materials@googlegroups.com


The webpage template is by the courtesy of CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision.