Materials and Chemistry Working Group

Interim co-leads

Dave Braines (IBM Research)
Tomoki Nagai (JSR Corporation)
Seiji Takeda, for Japan and Asia (IBM Research)
Flaviu Cipcigan, for Europe (IBM Research)
Jed Pitera, for North America (IBM Research)
Emilio Vital Brazil, for South America (IBM Research)

Projects

The following open source models are available on GitHub:

…and we have plans for more models to be released soon.

Agenda

We invite you to be part of our ever-growing community of innovators, researchers, and industry experts who share a common goal: to develop safe, scientifically reliable, and accessible AI-powered tools for materials discovery and chemistry.

The following projects have been proposed and are being considered, and we’re looking for volunteers to join or co-lead in these areas:

Training fused foundation models.
Trans-dimensional flow-matching for molecular generation
Training of GFlowNets for materials generation
Reproducing AlphaFold 3 capabilities
Organizing hackathons

Other resources

The IBM team have released 3 foundation models at github.com/ibm/materials and we have plans for further releases in 2024 and 2025. These are supported by these publications and more:

[1] Soares, Eduardo, Victor Shirasuna, Emilio Vital Brazil, Renato Cerqueira, Dmitry Zubarev, and Kristin Schmidt. "A Large Encoder-Decoder Family of Foundation Models For Chemical Language." arXiv preprint arXiv:2407.20267 (2024).
[2] Soares, Eduardo, Akihiro Kishimoto, Victor Shirasuna, Hiroshi Kajino, Emilio Ashton Vital Brazil, Seiji Takeda, and Renato Cerqueira. "A Multi-View approach based on Graphs and Chemical Language Foundation Model for Molecular Properties Prediction." In AAAI Conference on Artificial Intelligence. 2024.
[3] Soares, Eduardo Almeida, Victor Shirasuna, Emilio Ashton Vital Brazil, Renato Fontoura de Gusmao Cerqueira, Dmitry Zubarev, Tiffany Callahan, and Sara Capponi. "MoLMamba: A Large State-Space-based Foundation Model for Chemistry." In American Chemical Society (ACS) Fall Meeting. 2024.
[4] Anonymous, 2024. Agnostic Causality-Driven Enhancement of Chemical Foundation Models on Downstream Tasks. NeurIPS 2024 (in preparation)
[5] Kishimoto, Akihiro, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, and Seiji Takeda. "MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network." arXiv preprint arXiv:2309.16374 (2023).
[6] Takeda, Seiji, Lisa Hamada, Emilio Ashton Vital Brazil, Eduardo Almeida Soares, and Hajime Shinohara. "SELF-BART: A Transformer-based Molecular Representation Model using SELFIES." arXiv (2024).
[7] SELFIES-TED : A Robust Transformer Model for Molecular Representation using SELFIES, in review for ICLR 2025 [link]
[8] Priyadarsini, Indra, Vidushi Sharma, Seiji Takeda, Akihiro Kishimoto, Lisa Hamada, and Hajime Shinohara. "Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model." arXiv preprint arXiv:2406.19792 (2024).

Meeting schedule

We have virtual meetings approximately every two weeks separately in Japan and Europe/America timezones. We also arrange workshops and social events at relevant large conferences (ICML, ACS etc). Look out for us at the social event “Breaking Silos: Open Collaboration for AI x Science” at NeurIPS 2024 in Vancouver.

Frequently Asked Questions (FAQ)

How can I join the AI Alliance Working Group for Materials and Chemistry (WG4M)? Contact us via this form and we’ll add you to the slack channel and mailing list.
How can I access the open-source models that have already been released? You can find the existing open-source foundation models on GitHub here, with example notebooks, instructions on usage, and links to the model weights on HuggingFace for each of the released models..