@article{Coarse-Graining:2818,
      recid = {2818},
      author = {Durumeric, Aleksander Evren Paetzold},
      title = {Adversarial Analysis and Molecular Coarse-Graining},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2021-03},
      pages = {170},
      abstract = {Models are common in chemistry. When these models can be  described mathematically, their real world implications can  often be simulated using computers, enabling the use of  more complex models in hopes of improving scientific  predictions. Prior to providing useful results these models  must often be calibrated against existing scientific data.  Separately, machine learning has recently gained  significant traction in many applications. The algorithms  underpinning machine learning often similarly require  calibration prior to application. This work provides  mathematical and numerical results connecting these two  areas. Specifically, we consider novel applications of  classification when creating molecular models such as those  used in coarse-grained molecular dynamics. We focus on the  concept of adversaries, a tactic that has recently gained  traction in the machine learning community, and use this  framework to analyze the difference between various  coarse-grained ensembles and to parameterize new  coarse-grained force-fields. Collectively, we show that  classification is an effective tool for understanding the  differences between high dimensional free energy surfaces  and that adversarial parameterization strategies are  theoretically and numerically feasible for coarse-grained  models.},
      url = {http://knowledge.uchicago.edu/record/2818},
      doi = {https://doi.org/10.6082/uchicago.2818},
}