● Covered by 1 source · 1 reportHigh impact
Schrödinger accelerates molecular discovery by 4x using AlphaEvolve
Key points
- Schrödinger utilized AlphaEvolve to enhance AI model training.
- Focus on improving Ewald summation performance in molecular mechanics.
- Achieved a 4x increase in processing speed for molecular simulations.
Background on Molecular Simulations
Computational chemistry traditionally uses force fields for speed or quantum methods for accuracy, but not both effectively.
Machine-learned force fields (MLFFs) aim to bridge this gap by using neural networks trained on quantum data.
Partnership with Google Cloud
To boost performance further, Schrödinger collaborated with Google Cloud to deploy AlphaEvolve.
This evolutionary AI from Google DeepMind refines algorithms iteratively to enhance efficiency in computation.
Identifying Performance Constraints
Schrödinger pinpointed neighbor list computation and Ewald summation as key bottlenecks in their MLFF training.
Adapting the Ewald summation involved overcoming its computational challenges, which previously relied on inefficient practices.
Implementation of AlphaEvolve
AlphaEvolve helped create a batched implementation of the Ewald summation through parallel batch matrix multiplication.
This new development significantly improved the speed of Schrödinger's PyTorch code, outperforming existing algorithms.
Evaluation of Results
Schrödinger adopted a multi-layered evaluation framework to assess the evolved code's performance and accuracy.
Metrics included inverse time to maximize throughput and functional correctness to ensure scientific reliability.
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Reporting from
Schrödinger partnered with Google Cloud to implement AlphaEvolve, enhancing their MLFF process by 4x. This advancement resolves the trade-off between speed and precision in molecular simulations, significantly impacting drug discovery and materials design.