AI BriefingSchrödinger Speeds Up Molecular Discovery by 4x with AlphaEvolve
Key point
Schrödinger adopted Google DeepMind's AlphaEvolve to resolve bottlenecks in molecular simulation algorithms, boosting processing speed by 4x.
Details
In computational chemistry research, high-precision quantum mechanical methods have the limitation of being too slow. Machine-learned force fields (MLFFs) emerged to address this, but computational speed constraints still remained when processing vast chemical libraries.
To overcome these performance constraints, Schrödinger partnered with Google Cloud to introduce AlphaEvolve. AlphaEvolve is an evolutionary AI coding agent developed by Google DeepMind that iteratively generates and improves algorithms to find the most efficient code paths.
Schrödinger identified two key algorithms that were degrading performance within the MLFF training pipeline.
- Neighbor list computation: the process of aggregating atomic neighbor data
- Ewald summation: the process of calculating long-range potentials
By using AlphaEvolve to optimize these algorithmic bottlenecks, Schrödinger was able to dramatically improve the training speed of AI models for energy and force calculations.
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