2 papers
physics.chem-ph2025
Benchmarking Simulacra AI's Quantum Accurate Synthetic Data Generation for Chemical Sciences
Fabio Falcioni, Elena Orlova, Timothy Heightman +2
In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the en…
physics.chem-ph2025
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet +11
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of…