From 43fe929cd0d4e6b716687a3b937093e3edee18b3 Mon Sep 17 00:00:00 2001 From: Ray Zhu Date: Fri, 29 Mar 2024 01:15:17 -0500 Subject: [PATCH 1/2] Update project-09-pme-no-hikari.md --- _projects/project-09-pme-no-hikari.md | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/_projects/project-09-pme-no-hikari.md b/_projects/project-09-pme-no-hikari.md index eb774ee..08559e1 100644 --- a/_projects/project-09-pme-no-hikari.md +++ b/_projects/project-09-pme-no-hikari.md @@ -1,23 +1,22 @@ --- number: 9 -title: Optimizing The CO2 Adsorption Capacity of Metal-Organic Frameworks Using Thompson Sampling +title: Optimizing The CO2 Uptake of Metal-Organic Framework Using Thompson Sampling topic: general team_leads: - - Ray Zhu (University of Chicago) + - Ray Zhu (University of Chicago) @Ray16 contributors: - - Oliver Tang (University of Chicago) - - Suraj Sudhakar (University of Chicago) - - Jaehee Park (University of Chicago) - - Rija Ansari (National Research Council) - - Nilesh Jain (Notsohuman.ai) + - Oliver Tang (University of Chicago) @oytang + - Suraj Sudhakar (University of Chicago) @surajsudhakar99 + - Jaehee Park (University of Chicago) @wogml1997 + - Rija Ansari (National Research Council) @rija-ansari github: AC-BO-Hackathon/real-world-pme-no-hikari -# youtube_video: [Youtube_Video_ID] +youtube_video: https://www.youtube.com/watch?v=l0aVZDMwIMU --- -Metal-organic frameworks are promising materials for carbon capture at large scale. In this work, we investigate the optimal MOF selection strategy to find top-performing candidates with the highest CO2 uptake. We adopt the CRAFTED MOF dataset and build Bayesian models with Thompson sampling acquisition function to perform candidate selection. We also benchmark Thompson sampling against other acquisition functions to compare their performance in finding top MOF candidates. +Metal-organic frameworks are nanoporous materials that shows great promise for carbon capture at large scale. In this work, we adopt the CRAFTED MOF dataset and build Bayesian optimization model with Thompson sampling acquisition function to perform candidate selection for MOfs with high CO2 uptake. We benchmark Thompson sampling against random sampling method to compare their performance in finding high-performers. References: From 8382448f22eccc4c5eb7f271907175474de08544 Mon Sep 17 00:00:00 2001 From: "Sterling G. Baird" Date: Mon, 6 May 2024 15:40:03 -0400 Subject: [PATCH 2/2] Update project-09-pme-no-hikari.md --- _projects/project-09-pme-no-hikari.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_projects/project-09-pme-no-hikari.md b/_projects/project-09-pme-no-hikari.md index 08559e1..8aba338 100644 --- a/_projects/project-09-pme-no-hikari.md +++ b/_projects/project-09-pme-no-hikari.md @@ -12,7 +12,7 @@ contributors: - Rija Ansari (National Research Council) @rija-ansari github: AC-BO-Hackathon/real-world-pme-no-hikari -youtube_video: https://www.youtube.com/watch?v=l0aVZDMwIMU +youtube_video: l0aVZDMwIMU ---