5 papers
Guided Synthesis of EMT Zeolites by Machine Learning
Emmanuel A. Olanrewaju, Santosh Adhikari, Zhiyin Niu +4
Zeolites are microporous crystalline materials with diverse frameworks, widely used in industrial applications such as petroleum refining and molecular separation. Unlike most zeol…
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
Etinosa Osaro, Santosh Adhikari, Stamatia Zavitsanou +2
Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational throughput under constraints that…
Convergence Theory for Iterative LLM-Based Neural Architecture Search: A Parametric Cross-Entropy Framework with Closed-Form Proxy Reliability
Santosh Premi Adhikari, Radu Timofte, Dmitry Ignatov
Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS), yet no formal convergence theory exists for this class of algorithms…
Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs
Santosh Premi Adhikari, Radu Timofte, Dmitry Ignatov
Large language models (LLMs) show strong potential for neural architecture generation, yet existing approaches produce complete model implementations from scratch -- computationall…
Cartesian atomic moment machine learning interatomic potentials
Mingjian Wen, Wei-Fan Huang, Jin Dai +1
Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficie…