Update PhD

_config.yml

@@ -10,7 +10,7 @@ locale                   : "en-US"
 title                    : "Changwen Xu"
 title_separator          : "-"
 name                     : &name "Changwen Xu"
-description              : &description "Graduate Student at Carnegie Mellon University"
+description              : &description "Ph.D. Student at University of Michigan"
 url                      : https://ChangwenXu98.github.io # the base hostname & protocol for your site e.g. "https://mmistakes.github.io"
 baseurl                  : "" # the subpath of your site, e.g. "/blog"
 repository               : "academicpages/academicpages.github.io"
@@ -82,12 +82,12 @@ analytics:
 author:
   name             : "Changwen Xu"
   avatar           : "profile.jpg"
-  bio              : "Graduate Student at Carnegie Mellon University"
-  location         : "Pittsburgh, PA"
+  bio              : "Ph.D. Student at University of Michigan"
+  location         : "Ann Arbor, MI"
   employer         :
   # pubmed           : "https://www.ncbi.nlm.nih.gov/pubmed/?term=john+snow"
   googlescholar    : "https://scholar.google.com/citations?user=GyVx78kAAAAJ&hl=en"
-  email            : "changwex@andrew.cmu.edu"
+  email            : "changwex@umich.edu"
   researchgate     : "https://www.researchgate.net/profile/Changwen-Xu-3"
   uri              :
   bitbucket        :

_pages/about.md

@@ -10,7 +10,7 @@ redirect_from:
 
 About Me
 ------
-I'm going to be a PhD student in MechE at University of Michigan advised by [Prof. Viswanathan](https://www.andrew.cmu.edu/user/venkatv/index.html). Earlier, I received M.S. in Computational Materials Science and Engineering at Carnegie Mellon University and did research in [Mechanical and AI Lab](https://sites.google.com/view/barati) advised by [Prof. Farimani](https://www.meche.engineering.cmu.edu/directory/bios/barati-farimani-amir.html). Besides, I received B.Eng in Materials Science and Engineering at South China University of Technology.
+I am a Ph.D. student in Mechanical Engineering at University of Michigan advised by [Prof. Viswanathan](https://www.andrew.cmu.edu/user/venkatv/index.html) working on Molecular Machine Learning. Earlier, I received M.S. in Computational Materials Science and Engineering at Carnegie Mellon University and did research in [Mechanical and AI Lab](https://sites.google.com/view/barati) advised by [Prof. Barati Farimani](https://www.meche.engineering.cmu.edu/directory/bios/barati-farimani-amir.html). Besides, I received B.Eng in Materials Science and Engineering at South China University of Technology.
 
 My research interest lies in combining Artificial Intelligence with interdisciplinary science and engineering problems. My current research focuses on implementing and improving deep learning in molecular property prediction and conformation generation to understand complex chemical systems. I believe that deep learning models are able to learn representations from data so that we can understand scientific problems from a data science view, which is a significant transformation to traditional research strategies. 
 

_pages/cv.md

@@ -13,11 +13,17 @@ You can download my [CV](http://ChangwenXu98.github.io/files/CV.pdf) (last updat
 
 Education
 ======
+* Ph.D. in Mechanical Engineering, University of Michigan, 2027 (expected)
 * B.E. in Materials Science and Engineering, South China University of Technology, 2021
 * M.S. in Computational Materials Science and Engineering, Carnegie Mellon University, 2022
 
 Work experience
 ======
+* Summer 2023: Machine Learning Summer Internship
+  * Redesign Science
+  * Duties included: Improving Machine-Learned Collective Variables with Energy-based Path Construction
+  * Supervisor: Dr. Andreas Mardt
+
 * Summer 2022 and Spring 2023: Research Assistant
   * Carnegie Mellon University
   * Duties included: Developing AI for molecule property prediction

_publications/2023-06-30-Denoise.md

@@ -0,0 +1,17 @@
+---
+title: "Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials"
+collection: publications
+permalink: /publication/2023-06-30-Denoise
+date: 2023-06-30
+venue: 'Journal of Chemical Theory and Computation'
+authors: Y Wang, C Xu, Z Li, and A Barati Farimani, J. Chem. Theory Comput., 2023
+
+---
+
+<a href='https://pubs.acs.org/doi/10.1021/acs.jctc.3c00289'>Paper available here</a>
+
+---
+
+**Abstract** 
+
+Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) approaches for molecular potential predictions. However, building accurate and transferable potential models using GNNs remains challenging, as the data are greatly limited by the expensive computational costs and level of theory of QM methods, especially for large and complex molecular systems. In this work, we propose denoise pretraining on nonequilibrium molecular conformations to achieve more accurate and transferable GNN potential predictions. Specifically, atomic coordinates of sampled nonequilibrium conformations are perturbed by random noises, and GNNs are pretrained to denoise the perturbed molecular conformations which recovers the original coordinates. Rigorous experiments on multiple benchmarks reveal that pretraining significantly improves the accuracy of neural potentials. Furthermore, we show that the proposed pretraining approach is model-agnostic, as it improves the performance of different invariant and equivariant GNNs. Notably, our models pretrained on small molecules demonstrate remarkable transferability, improving performance when fine-tuned on diverse molecular systems, including different elements, charged molecules, biomolecules, and larger systems. These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems.