Accelerated Discovery of Sustainable Materials for the Semiconductor Industry
Advances in memory technologies, novel compute architectures, artificial intelligence, quantum computing, robotic automation, and cloud technologies are poised to further accelerate discovery, drive profound transformation, and help enable a sustainable future. However, can these same technologies allow the semiconductor industry to address its own key sustainability challenges to ensure a sustainable future for computing?
This talk will update some recent efforts that we had previously reported at the 2021 Strategic Materials Conference on of discovering more sustainable cationic onium compounds for use as photoacid generators [1-3]. Since then, the world has seen the explosive development of AI, and in particular, large language models and other so-called foundation models. At the same time, our work has shifted to focus on tackling the challenge of accelerating the discovery of replacement alternatives for per- and polyfluoroalkyl substances (PFAS). In this talk, we will discuss the application of tuned chemistry foundation models to that task of predicting environmental hazard properties [4] and generative workflows [5] to design new fluorine-free superacid moieties. These will be discussed in the context of building new tools and workflows to enable efficient Human-AI Co-creation [6,7].
1) Pyzer-Knapp, E.O.; Pitera, J.W.; Staar, P.W.J.; Takeda, S.; Laino, T.; Sanders, D.P.; Sexton, J.; Smith, J.R.;Curioni, A. "Accelerating materials discovery using artificial intelligence, high performance computing and robotics," npj Comput Mater 2022, 8, 84. https://doi.org/10.1038/s41524-022-00765-z
2) https://www.research.ibm.com/science/photoresist/
3) Hoffman, S.C.; Chenthamarakshan, V.; Zubarev, D.Y.; Sanders, D.P.; Das, P. “Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model,” NeurIPS, Deep Generative Models and Downstream Applications, 2021. https://doi.org/10.48550/arXiv.2112.01625
4) Soares, E.; Brazil, E. V.; Gutierrez, K. F. A.; Cerqueira, R.; Sanders, D.; Schmidt, K.; Zubarev, D. "Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction," Poster, AI4Science Workshop, 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, United States, (2023). https://doi.org/10.48550/arXiv.2306.14919
5) Soares, E.; Cipcigan, F.; Zubarev, D.; Brazil, E.V.; “A Framework for Toxic PFAS Replacement based on GFlowNet and Chemical Foundation Model,” Poster, AI4Science Workshop, 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, United States, (2023). https://openreview.net/forum?id=oIMhS5n0Qz
6) Zubarev, D.; Mendes, C.R.; Brazil, E.V.; Cerqueria, R.; Schmidt, K.; Segura, V.; Ferreira, J.J.; Sanders, D.P. “Toward Human-AI Co-creation to Accelerate Material Discovery,” Poster, AI4Science Workshop, 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA USA, 2 Dec 2022. https://doi.org/10.48550/arXiv.2211.04257
7) Ferreira, J.A.; Segura, V.; Souza, J.G.R.; Barbosa, G.D.J.; Gallas, J.; Cerqueira, R.; Zubarev, D. “Human-AI Co-Creation Approach to Find Forever Chemicals Replacements,” In GenAICHI Workshop at CHI 2023, April 28, 2023, Virtual Workshop. https://doi.org/10.48550/arXiv.2304.05389
BIOGRAPHY
Daniel P. Sanders, Ph.D. is a Principal Research Scientist and Senior Manager in charge of the Materials Discovery department at IBM Research – Almaden. Dan received his Ph.D. in Chemistry from the California Institute of Technology (Pasadena, CA) working in the lab of Prof. Robert H. Grubbs on metal-catalyzed fluoropolymers for 157nm lithography. Dan currently oversees a department whose mission is to develop and apply informatics, simulation, artificial intelligence, and advanced lab automation technologies to speed the discovery of new sustainable materials and apply these tools to pertinent use cases including semiconductors and energy storage.