5 citations · 7 across the 5 of their papers we have counts for
5 papers
Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
Ali Baheri, AmirHossein Naghdi
We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs
Grzegorz Kaszuba, Amirhossein D. Naghdi, Dario Massa +3
Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial…
Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields
Cameron J. Owen, Amirhossein D. Naghdi, Anders Johansson +3
Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions,…
Neural Network Interatomic Potentials For Open Surface Nano-mechanics Applications
Amirhossein D. Naghdi, Franco Pellegrini, Emine Küçükbenli +4
Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such pu…