Update CV

_pages/about.md

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 About Me
 ------
-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.
+I am a Ph.D. student in Mechanical Engineering at University of Michigan, working on Molecular Machine Learning in [EEG](https://www.cmu.edu/me/venkatgroup/) 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. 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. 
+My research interest lies in combining Artificial Intelligence with interdisciplinary science and engineering problems. My current research focuses on implementing and improving foundation models for material discovery and leveraging scientific machine learning for electrolyte optimization. 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 from traditional research strategies. 
 
 Research Interests
 ------
 1. Machine Learning
 2. Molecular Modeling
 3. AI4Science
-4. Large Language Models
-5. Equivariant GNNs
+4. SciML
+5. Differentiable Physics
 

_pages/cv.md

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   - /resume
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-You can download my [CV](http://ChangwenXu98.github.io/files/CV.pdf) (last updated August 2023)
+You can download my [CV](http://ChangwenXu98.github.io/files/CV.pdf) (last updated September 2023)
 
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_publications/2023-08-30-Matinformer.md

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+---
+title: "Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction"
+collection: publications
+permalink: /publication/2023-08-30-Matinformer
+date: 2023-08-30
+venue: 'arXiv preprint arXiv:2308.16259'
+authors: H Huang, R Magar, C Xu, and A Barati Farimani, arXiv preprint arXiv:2308.16259, 2023
+
+---
+
+<a href='https://arxiv.org/abs/2308.16259'>Paper available here</a>
+
+---
+
+**Abstract** 
+
+Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.