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ML4MS 2024 Closing

Accelerating materials design with AI emulators and generators

Application of the Question Answering method to extract information from materials science literature

ML4MS 2024 Panel discussion

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Boosting Materials Design: Word Embeddings Meet Experimental Data

Fueling the Digital Chemistry Revolution with Language and Multimodal Foundation Models

Practical Machine Learning for Organic Small Molecule Modelling

Revealing Chemical Pathways in Reaction Data through Noctis

Lignin Carbohydrate Complexes – Learning the Structure-Property Relation with Artificial Intelligence

LATTE: an atomic environment descriptor based on Cartesian tensor contractions

Spectral Operator Representations