APS March Meeting 2024
Invited talk

Computational Materials Discovery for Carbon Dioxide Capture Applications

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Artificial intelligence (AI) is aiding the discovery of sustainable materials in every step along the computational workflow. Machine learning (ML) supports the automated extraction of data from the literature, the creation of large simulation data sets, the generative design of new materials, as well as the computational validation of discovery outcomes. In this talk, I will present our team’s research in the computational discovery of polymers [1,2] and nanopores [3,4,5] for carbon dioxide capture applications. I will discuss some of challenges in the AI/ML design of complex materials and provide examples of how discovery outcomes could be computationally validated, prior to lab synthesis and characterization. In view of global challenges such as climate change, open-science strategies with publicly shared data and models are needed for accelerating computational materials discovery. References: [1] Giro, R., Hsu, H., Kishimoto, A. et al. AI powered, automated discovery of polymer membranes for carbon capture. npj Comput Mater 9, 133 (2023). [2] Ferrari, B.S., Manica, M., Giro, R. et al. Predicting polymerization reactions via transfer learning using chemical language models. Preprint (2023). [3] Oliveira, F.L., Cleeton, C., Neumann Barros Ferreira, R. et al. CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks. Sci Data 10, 230 (2023). [4] Zheng, B., Lopes Oliveira, F., Neumann Barros Ferreira, R. et al. Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2’s Chemisorption and Diffusion in Mg-MOF-74. ACS Nano 17 (6), 5579-5587 (2023). [5] Cipcigan, F., Booth, J., Neumann Barros Ferreira, R. et al. Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GflowNets. Preprint (2023).