fig5

Figure 5. Agents use material computational tools and ML models to calculate the properties of materials. (A) Computational methods that Aitomia can use and the computing tasks it can perform^[72]; (B) GNN model for predicting Peierls barrier and potential energy change^[83]. Figure 5A is reproduced from “Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations”, arXiv:2505.08195, under CC BY 4.0 license^[72]. Figure 5B is reproduced from “Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems”, by Alireza Ghafarollahi and Markus J. Buehler, arXiv:2410.13768, under CC BY-NC-ND 4.0 license and the image has not been modified^[83]. ML: Machine learning; GNN: graph neural networks; AI: artificial intelligence; LLM: large language model; QM: quantum mechanics; DFT: density functional theory; ANI-1ccx-gelu: a universal interatomic potential for calculating IR anharmonic frequencies; OMNI-P2x: a universal neural network potential for excited-state simulations; MACE-OFF: a series of short-range transferable force fields for organic molecules; AIMent-2: the 2nd generation of atoms-in-molecules neural network potential; AIQM: AI-enhanced quantum mechanics methods; DENS24: density functional ensembles.