Boltzmann priors for Implicit Transfer Operators
Authors: Juan Viguera Diez, Mathias Schreiner, Ola Engkvist, Simon Olsson
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that Bo PITO interpolators can recover approximate dynamics from models trained on biased simulations. For the Prinz Potential, we find that while ITO suffers from poor performance modeling long-term dynamics when data is scarce, Bo PITO models accurately capture long-term dynamics without worsening the performance on short and medium time-scales. Furthermore, the sections '5.2 BOLTZMANN PRIORS FOR TRAINING DATA GENERATION', '5.3 BOPITO EFFICIENTLY SAMPLES LONG-TERM DYNAMICS IN A LOW-DATA CONTEXT', and '5.4 INTERPOLATING BETWEEN MODELS TRAINED ON OFF-EQUILIBRIUM DATA AND THE BOLTZMANN DISTRIBUTION WITH EXPERIMENTAL DATA' all describe empirical evaluation and data analysis. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg SE-41296 Gothenburg, Sweden. 2 Molecular AI, Discovery Sciences, R&D, Astra Zeneca Gothenburg, Pepparedsleden 1, 431 50 M olndal, Sweden. |
| Pseudocode | Yes | Algorithm 1 Training. Dis Exp is defined in Algorithm 4. Algorithm 2 Sampling from pθ(x0, N). Algorithm 3 Ancestral sampling. Algorithm 4 Sampling from Dis Exp. |
| Open Source Code | Yes | Code is available at https://github.com/olsson-group/bopito. |
| Open Datasets | Yes | Prinz potential is a 1D potential commonly used for benchmarking MD sampling methods (Prinz et al., 2011). We use publicly available data from Dibak et al. (2022), containing 1 µs simulation time split in 20 trajectories. |
| Dataset Splits | Yes | For different numbers of trajectories, n, we train 5000/n ITO models on n Prinz potential trajectories of length 150. Trajectories do not overlap among different trainings. Prinz potential 10 models are trained on non-overlapping trajectory sets for different numbers of trajectories. Alanine Dipeptide 10 models are trained on potentially overlapping random trajectory sets for different numbers of trajectories. Chignolin 4 models are trained on potentially overlapping random trajectory sets for different numbers of trajectories. |
| Hardware Specification | No | The computations in this work were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre. This statement does not provide specific hardware details such as GPU or CPU models, memory, or processing speeds. |
| Software Dependencies | No | We generate trajectories using an Euler-Maruyama integrator using the library Deeptime (Hoffmann et al., 2021). The paper mentions the software library 'Deeptime' but does not specify its version number. No other software dependencies are listed with specific version numbers. |
| Experiment Setup | Yes | We report architectural and training hyper-parameters in Table 1. Table 1 includes: Diffusion steps (500, 1000), Noise schedule (Sigmoidal, Polynomial), Batch size (2,097,152, 1,024/32), Learning rate (0.001), Layers (3, 5), Embedding dimension (256), Net dimension (256), Optimizer (Adam), Inference ODE steps (50, 100/50). |