I recently moved to Generate Biomedicines Inc. to work on protein design, 
Emails:

wwang@generatebiomedicines.com 
or 
wujiewang02@gmail.com

Bio:

I am Wujie Wang, a 4th year graduate student at MIT in Materials Sc. & Eng. I hold a B.A. in Physics from Wesleyan and B.S in Engineering and Applied Sciences from Caltech. I am from Nanjing, China and attended Nanjing Foreign Language School. 

I am maintaining this website to showcase my work. 

Research Interests/Expertise:

• Differentiable simulations for control and learning tasks
• Develop computational methods to learn interpretable and symmetry preserving representation of molecular systems 
• Emergent information processing capacity in out-of-equilibrium molecular systems 
• High throughput screening of molecules/polymers, particularly, for electrolytes ​
• Energy-Based Models(EBM) for variational inference 

Notes:

• basic thermodynamics

   https://github.com/wwang2/thermo-notes/blob/main/thermodynamics/StatMechNotes.pdf

• Apply Automatic Differentiation to compute the Virial Stress Tensor

  https://github.com/wwang2/thermo-notes/blob/main/AutoDiff/On_computing_Virials_with_AD.pdf

Selected Projects:

NeuralForceField:

Graph convolution models for molecular dynamics 

https://github.com/learningmatter-mit/NeuralForceField

mdgrad:

differentiable molecular simulations for learning molecular interactions

https://github.com/torchmd/mdgrad 

Coarse-Graining Auto-Encoders: 

Code for learning for coarser represenation from Molecular Dynamics data

A super easy Google Colab walk-through: https://colab.research.google.com/github/wwang2/Coarse-Graining-Auto-encoders/blob/master/cgae_alanine_dipeptide_colab_walkthrough.ipynb

Selected Demos:

controllable polymer fold with Graph Neural Networks (Ref. [1])

Differentiable simulations: learning Hamiltonains to reporduce water pair correlations (Ref. [1])

ML-exploration of chemical space for ether-based Solvate Ionic Liquids (Ref. [2])

Unsupervised learning for Coarse-Grained Modeling (Ref. [3])

Papers/Preprints:

[1] Wang, W., Axelrod, S., & Gómez-Bombarelli, R. (2020). Differentiable Molecular Simulations for Control and Learning. ArXiv. Retrieved from https://arxiv.org/abs/2003.00868

[3] Ang, S. J., Wang, W., Schwalbe-Koda, D., Axelrod, S., & Gomez-Bombarelli, R. (2020). Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11910948.v1

[4] Wang, W., & Gómez-Bombarelli, R. (2019). Coarse-graining auto-encoders for molecular dynamics. Npj Computational Materials, 5(1). https://doi.org/10.1038/s41524-019-0261-5

[5] Wang, W., Nocka, L. M., Wiemann, B. Z., Hinckley, D. M., Mukerji, I., & Starr, F. W. (2016). Holliday Junction Thermodynamics and Structure: Coarse-Grained Simulations and Experiments. Scientific Reports, 6(3), 3–4. https://doi.org/10.1038/srep22863