Our paper “Geometry, Not Energy Surface, Drives the Neutral MLIP–DFT Gap in Atomistic Interaction Surrogates”, the study behind RAPIDS, has been accepted at the AI4Physics workshop at ICML 2026 (OpenReview).

RAPIDS is the rapid physical validation engine of our autonomous discovery stack. It benchmarks machine-learning interatomic potentials (MLIPs) against DFT across 5,567 probe–target dimer interactions and 18 benchmark tasks. The finding behind the title: geometric representation, not the energy surface itself, drives the neutral MLIP–DFT gap.

The packaging matters as much as the finding. RAPIDS is exposed as a tool autonomous LLM agents can call, so agents like DToR and screening twins like T³ can get a fast atomistic sanity check before committing to a costly simulation or experiment.

Co-first-authored with Zixin Ding and Rodrigo P. Ferreira, together with Yuxin Chen and Junhong Chen. I’ll present it in July.