Global machine learning potentials for molecular crystals
I Žugec, RM Geilhufe, I Lončarić - The Journal of chemical physics, 2024 - pubs.aip.org
Molecular crystals are difficult to model with accurate first-principles methods due to large
unit cells. On the other hand, accurate modeling is required as polymorphs often differ by …
unit cells. On the other hand, accurate modeling is required as polymorphs often differ by …
Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control
M Lu, S Rao, H Yue, J Han, J Wang - Crystal Growth & Design, 2024 - ACS Publications
Crystals are integral to a variety of industrial applications, such as the development of
pharmaceuticals and advancements in material science. To anticipate crystal behavior and …
pharmaceuticals and advancements in material science. To anticipate crystal behavior and …
Efficient Evolutionary Search over Chemical Space with Large Language Models
Molecular discovery, when formulated as an optimization problem, presents significant
computational challenges because optimization objectives can be non-differentiable …
computational challenges because optimization objectives can be non-differentiable …
Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Pose
M Kilgour, J Rogal, M Tuckerman - arXiv preprint arXiv:2405.13791, 2024 - arxiv.org
The point cloud is a flexible representation for a wide variety of data types, and is a
particularly natural fit for the 3D conformations of molecules. Extant molecule embedding …
particularly natural fit for the 3D conformations of molecules. Extant molecule embedding …