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The article introduces ARMOR, an agentic framework for predicting chemical reaction feasibility by adaptively prioritizing and resolving conflicts among multiple AI tools. It demonstrates superior performance over single-tool and aggregation methods on public datasets.
This paper presents an agentic system using Large Language Models to automate the discovery of exchange-correlation functionals in Density Functional Theory, achieving improvements over human-designed baselines while highlighting challenges with benchmark overfitting.
Researchers from Universitat Rovira i Virgili published a paper in Nature Machine Intelligence introducing CoCoGraph, an AI tool that generates chemically valid novel molecules using a constrained discrete diffusion process.
Microsoft Research releases Skala, a deep-learning exchange-correlation functional for DFT that achieves 2.8 kcal/mol accuracy on GMTKN55 at semi-local cost, outperforming traditional functionals across broad chemistry benchmarks.