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20232026
most citedReflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

5 citations · 11 across the 12 of their papers we have counts for

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cs.LG2026

Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy

Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin +4

Modern automated microscopy faces a fundamental discovery challenge: in many systems, the most important scientific information does not reside in the immediately visible image fea…

cs.LG20262 cited

Towards Self-Optimizing Electron Microscope: Robust Tuning of Aberration Coefficients via Physics-Aware Multi-Objective Bayesian Optimization

Utkarsh Pratiush, Austin Houston, Richard Liu +2

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors…

cs.LG2025

DIVIDE: A Framework for Learning from Independent Multi-Mechanism Data Using Deep Encoders and Gaussian Processes

Vivek Chawla, Boris Slautin, Utkarsh Pratiush +2

Scientific datasets often arise from multiple independent mechanisms such as spatial, categorical or structural effects, whose combined influence obscures their individual contribu…

cs.LG2025

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model

Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu +2

We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature o…

cs.LG20245 cited

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…

cs.LG20232 cited

EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations

Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan +7

Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries.…