Benchmark and Critical Evaluation for ML Force Fields with Molecular Simulations

Explore a comprehensive talk on benchmarking and critically evaluating machine learning force fields in molecular simulations. Delve into the limitations of current benchmarking practices that focus primarily on force and energy prediction errors, and discover a novel benchmark suite designed to assess the practical performance of ML force fields in producing realistic molecular dynamics trajectories. Learn about curated representative MD systems, including water, organic molecules, peptides, and materials, and understand the evaluation metrics corresponding to specific scientific objectives. Gain insights into the performance of state-of-the-art ML force field models, their shortcomings, and potential areas for improvement, with a particular emphasis on stability as a key metric. Explore the comprehensive open-source codebase provided for training and simulation with ML force fields, and engage with the speaker's perspectives on the future directions of molecular dynamics simulations beyond traditional force fields.

- Intro
- Molecular Dynamics Simulations
- ML Force Fields
- Simulation with Force Fields
- Force/energy Prediction Error
- Considerations in MD Simulations
- Scientifically Motivated Metrics
- Can SOTA ML Force Fields Simulate Various MD Systems? Key Results
- Takeaways
- Beyond Force Fields: A Spectrum of MD Simulation Problems
- Q+A