From cac51280c600c9fb078280edcdfd157553dd25a4 Mon Sep 17 00:00:00 2001
From: Bozhao Nan <57164369+bznan@users.noreply.github.com>
Date: Sun, 7 Apr 2024 13:43:19 -0400
Subject: [PATCH] Update project-40-reaction_multi-agents.md

Updated X post, members' affiliation
---
 _projects/project-40-reaction_multi-agents.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/_projects/project-40-reaction_multi-agents.md b/_projects/project-40-reaction_multi-agents.md
index e7a9ee2..07caf22 100644
--- a/_projects/project-40-reaction_multi-agents.md
+++ b/_projects/project-40-reaction_multi-agents.md
@@ -3,17 +3,18 @@ number: 40 # leave as-is, maintainers will adjust
 title: Optimizing Chemical Reaction Conditions with Multi-Agent Systems Using Large Language Models and Bayesian Optimization
 topic: general
 team_leads:
-  - Bozhao Nan (University of Notre Dame)
-  - Taicheng Guo (University of Notre Dame)
+  - Bozhao Nan (University of Notre Dame) @bznan
+  - Taicheng Guo (University of Notre Dame) @taichengguo
 
 # Comment these lines by prepending the pound symbol (#) to each line to hide these elements
 contributors:
-  - Kehan Guo (University of Notre Dame)
-  - Yanqiao Zhu (UCLA)
+  - Kehan Guo (University of Notre Dame) @KehanGuo2
+  - Yanqiao Zhu (UCLA) @SXKDZ
 
 github: AC-BO-Hackathon/project-reaction_BO_agents
 youtube_video: xf6rfyUQeZQ
 
+Check out our social media post on X: https://x.com/Bozhao95501764/status/1777029207857451508
 ---
 
 This project is focused on enhancing the efficiency of the Suzuki reaction process through an advanced multi-agent system, incorporating large language models (LLMs) and Bayesian Optimization (BO). The innovation lies in the employment of specialized sub-agents, each with expertise in a crucial domain of the reaction: catalyst design, solvent effects, and base selection. These agents work in concert with a supervisory agent, which integrates their insights and findings. This collaborative framework aims to optimize reaction conditions iteratively, leveraging both prior knowledge and experimental data to navigate the chemical space effectively.