Paper

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

Unreadunread

AI summary

arXiv:2606. 17445v1 Announce Type: new Abstract: Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across a vast chemical space that is difficult to explore efficiently through conventional screening alone.

Reading status

Priority

About this paper

Authors: Dong Hyeon Mok, Jonggeol Na, Seoin Back
Released: 2026-06-17 (22d ago)
Topic: Related to current project
Relation to my work: Read next because Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, line, rate, control, without, binding, alone, trained. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.17445

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.