Optimizing the Optimizer: Language Models Discover Faster Molecular Relaxation Algorithms

Key points

• Problem: In geometry optimization at the DFT level each step needs one force (gradient) evaluation, which dominates the wall time, so the step count sets the cost. Letting an LLM agent rewrite an optimizer raises three constraints: DFT is too expensive to score hundreds of candidates, candidates must work on unseen molecules, and the agent must be kept from cutting force calls by stopping early.
• Input and output: The agent receives a starting program that merges Sella 2.5.0 into one file (algo.py, 5975 lines including comments and blank lines), training and validation molecule sets and a remote evaluator, edits only algo.py, and returns AutoSella optimizers that reach the same convergence criteria with fewer force calls. Each optimizer itself takes one molecule and a force evaluator and updates the structure until all five convergence conditions hold (cap of 200 force calls, 500 for Solvated PubChem), and the molecule sets cover drug-like molecules with their surrounding biomolecules and solvent. This is a method paper that finds, releases and evaluates new optimization algorithms with a gated autoresearch loop.
• Method: In each cycle the agent reads the cycle record, the narrative log and the mechanisms already rejected, commits one coherent edit to algo.py, and after evaluation receives a per-molecule table of relative force calls, relative energy, final energy difference and convergence status. The validity gate discards candidates on training that have evaluation errors, mean energy recovery below Sella's, or a cost drop smaller than the threshold δ, and the generalization gate accepts a new champion (the best program so far) only if it also cuts cost on validation without evaluation errors while keeping mean energy recovery. The search runs only on the cheap GFN2-xTB potential, algo.py is the only file the agent may edit, and changing the evaluator, convergence criteria or molecule sets and adding molecule-specific special cases are prohibited.
• Implementation: Claude Code driving Claude Fable 5.1 (max) and Codex driving GPT-6 Astra (high) each ran independently for 66 active research hours and produced AutoSella-F and AutoSella-A, and the remote evaluator, a Redis queue with 192 single-thread workers on 4 hosts with Intel Xeon Gold 6348 CPUs, scored one candidate in approximately 60 to 90 seconds on average, depending on the run. The 469 training structures combine 225 PubChem molecules and 219 DES370K dimers from SPICE 2.0.1 with 25 registered drugs from the RowanSci optimizer benchmark, and the 465 validation structures come from SPICE 2.0.1. Per the official GitHub README, the main branch holds the three frozen optimizers (AutoSella-F, A, G), a Sella 2.5.0 reference wrapper, the molecule preparation pipeline and an example, while the autoresearch harness, per-model run records and the xTB and DFT evaluations sit on separate branches.
• Results: On the four held-out GFN2-xTB sets AutoSella-F cut mean per-molecule force calls to 39.86 to 73.25% of Sella with higher mean energy recovery on all four, while AutoSella-A used 78.14 to 92.57% on the first three sets but rose to 118.77% on Solvated PubChem with lower mean energy recovery. Under r2SCAN-3c DFT, used only for evaluation, AutoSella-F needed about 23 to 60% fewer force calls per molecule than Sella with greater mean energy recovery, and on Solvated PubChem it used on average 40.24% of Sella's force calls per molecule with 1.27% higher mean energy recovery. Over 66 hours Fable accepted 37 of 108 evaluated candidates and Astra 29 of 672, and AutoSella-G from the Grok 4.6 run in the appendix lowered the training cost to 97.79%.
• Limits: Both gates judge by mean energy recovery, force-call cost and evaluation errors, and in the separate GFN2-xTB structure check of Appendix E the share of molecules where AutoSella-F reaches the same local minimum as Sella was 97.2% on Test, 93.2% on Dipeptides, 39.8% on amino acid and ligand pairs and 1.0% on Solvated PubChem. The authors note that the other endpoints lie below the reference energy on average, yet on GFN2-xTB AutoSella-F ended more than 1.0 kcal/mol above Sella on 18.74% of amino acid and ligand pairs and 38.00% of Solvated PubChem systems. AutoSella-A used 109.06% of Sella's force calls on Solvated PubChem even under DFT with lower mean energy recovery, and the Grok 4.6 result AutoSella-G was much worse on the intermolecular sets and was left out of the DFT evaluation.

Paper links

External research summaries. These are not HDATF publications or measured product results.