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 (MLFFs) accelerate structural relaxation but leave the search over the vast configurational space a major bottleneck, and open-loop large language model (LLM) agents lack a physics-grounded feedback mechanism to correct erroneous initial guesses. We propose AdsMind (Adsorption configuration discovery with Machine intell
The rapid advancement of large language models and machine learning force fields is enabling new approaches to complex scientific discovery, addressing long-standing computational challenges in materials science and chemistry.
This development can significantly accelerate the discovery and optimization of heterogeneous catalysts, which are crucial for energy-efficient industrial processes and the development of new materials.
The ability to self-correct and accelerate the discovery of adsorption configurations with physics-grounded AI agents changes the methodology for materials discovery, potentially reducing R&D cycles and costs.
- · Materials scientists
- · Chemical engineers
- · Pharma R&D
- · Catalyst manufacturers
- · Traditional high-throughput screening methods
- · Computational chemistry reliance on purely human-driven intuition
More efficient catalyst designs emerge, leading to improved industrial chemical processes.
Reduced environmental impact and energy consumption in critical manufacturing sectors due to optimized catalysts.
The methodology is generalized to other scientific discovery challenges, further accelerating research across various disciplines.
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Read at arXiv cs.AI