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AdsMind system uses AI agents to accelerate catalyst discovery

By PulseAugur Editorial · [3 sources] ·

Researchers have developed AdsMind, a novel multi-agent system designed to accelerate the discovery of adsorption configurations on heterogeneous catalyst surfaces. This closed-loop framework integrates machine learning force fields (MLFFs) with large language models (LLMs) to enable autonomous error correction and improve search reliability. AdsMind significantly reduces the number of MLFF relaxations required compared to heuristic methods, achieving high success rates and providing more accurate results than open-loop LLM agents, thereby supporting more efficient autonomous chemistry workflows. AI

IMPACT This system could significantly speed up materials science research by automating complex configuration discovery processes.

RANK_REASON The cluster contains a research paper detailing a new AI system for a scientific discovery task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

AdsMind system uses AI agents to accelerate catalyst discovery

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Zongmin Zhang, Yuyang Lou, Bowen Zhang, Junwu Chen, Ryo Kuroki, Xuan Vu Nguyen, Edvin Fako, Lixue Cheng, Philippe Schwaller ·

    AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces

    arXiv:2606.19152v1 Announce Type: cross Abstract: Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive. Machine-learning force fields (…

  2. arXiv cs.AI TIER_1 English(EN) · Philippe Schwaller ·

    AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces

    Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive. Machine-learning force fields (MLFFs) accelerate structural relaxation but leave …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces

    Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive. Machine-learning force fields (MLFFs) accelerate structural relaxation but leave …

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