title: Understanding and Mitigating Distribution Shifts For Machine Learning
    Force Fields
summary: Machine Learning Force Fields (MLFFs) are a promising alternative to
  expensive ab initio quantum mechanical molecular simulations. Given the
  diversity of chemical spaces that are of interest and the cost of generating
  new data, it is important to understand how MLFFs generalize beyond their
  training distributions. In order to characterize and better understand
  distribution shifts in MLFFs, we conduct diagnostic experiments on chemical
  datasets, revealing common shifts that pose significant challenges, even for
  large foundation models trained on extensive data. Based on these observations,
  we hypothesize that current supervised training methods inadequately regularize
  MLFFs, resulting in overfitting and learning poor representations of
  out-of-distribution systems. We then propose two new methods as initial steps
  for mitigating distribution shifts for MLFFs. Our methods focus on test-time
  refinement strategies that incur minimal computational cost and do not use
  expensive ab initio reference labels. The first strategy, based on spectral
  graph theory, modifies the edges of test graphs to align with graph structures
  seen during training. Our second strategy improves representations for
  out-of-distribution systems at test-time by taking gradient steps using an
  auxiliary objective, such as a cheap physical prior. Our test-time refinement
  strategies significantly reduce errors on out-of-distribution systems,
  suggesting that MLFFs are capable of and can move towards modeling diverse
  chemical spaces, but are not being effectively trained to do so. Our
  experiments establish clear benchmarks for evaluating the generalization
  capabilities of the next generation of MLFFs. Our code is available at
  https://tkreiman.github.io/projects/mlff_distribution_shifts/.
paper_authors._id: 67d323231d5d1fd92c35ad1c
paper_authors.name: Tobias Kreiman
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-13T18:32:48.171Z
paper_authors._id: 64bece3e01f1983a86a3e043
paper_authors.avatarUrl: /avatars/7f6372d409e8eb0474f7ff6282cfd4e9.svg
paper_authors.fullname: Toby Kreiman
paper_authors.type: user
paper_authors.user: tkreiman
paper_authors._id: 67d323231d5d1fd92c35ad1d
paper_authors.name: Aditi S. Krishnapriyan
paper_title: Understanding and Mitigating Distribution Shifts For Machine Learning
    Force Fields
paper_summary: Machine Learning Force Fields (MLFFs) are a promising alternative to
  expensive ab initio quantum mechanical molecular simulations. Given the
  diversity of chemical spaces that are of interest and the cost of generating
  new data, it is important to understand how MLFFs generalize beyond their
  training distributions. In order to characterize and better understand
  distribution shifts in MLFFs, we conduct diagnostic experiments on chemical
  datasets, revealing common shifts that pose significant challenges, even for
  large foundation models trained on extensive data. Based on these observations,
  we hypothesize that current supervised training methods inadequately regularize
  MLFFs, resulting in overfitting and learning poor representations of
  out-of-distribution systems. We then propose two new methods as initial steps
  for mitigating distribution shifts for MLFFs. Our methods focus on test-time
  refinement strategies that incur minimal computational cost and do not use
  expensive ab initio reference labels. The first strategy, based on spectral
  graph theory, modifies the edges of test graphs to align with graph structures
  seen during training. Our second strategy improves representations for
  out-of-distribution systems at test-time by taking gradient steps using an
  auxiliary objective, such as a cheap physical prior. Our test-time refinement
  strategies significantly reduce errors on out-of-distribution systems,
  suggesting that MLFFs are capable of and can move towards modeling diverse
  chemical spaces, but are not being effectively trained to do so. Our
  experiments establish clear benchmarks for evaluating the generalization
  capabilities of the next generation of MLFFs. Our code is available at
  https://tkreiman.github.io/projects/mlff_distribution_shifts/.
paper_ai_keywords: Machine Learning Force Fields (MLFFs) ab initio quantum mechanical molecular simulations chemical spaces distribution shifts diagnostic experiments supervised training methods overfitting poor representations spectral graph theory graph structures gradient steps auxiliary objective physical prior test-time refinement strategies