
SELFIES (Self-Referencing Embedded Strings)
SELFIES is a 100% robust molecular string representation for ML, implemented in the open-source selfies Python library.

SELFIES is a 100% robust molecular string representation for ML, implemented in the open-source selfies Python library.

Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …

Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …

Lu et al. introduce SpaceFormer, a Transformer that models entire 3D molecular space (not just atoms) for superior …

Bigi et al. critique non-conservative force models in ML potentials, showing their simulation failures and proposing …

Luo et al. introduce SPHNet, using adaptive sparsity to dramatically improve SE(3)-equivariant Hamiltonian prediction …

Fu et al. propose energy conservation as a key MLIP diagnostic and introduce eSEN, bridging test accuracy and real …

Aneja et al.'s NeurIPS 2021 paper introducing Noise Contrastive Priors (NCPs) to address VAE's 'prior hole' problem with …

Learn how GEOM transforms 2D molecular graphs into dynamic 3D conformer ensembles for molecular machine learning …

Skinnider (2024) shows that generating invalid SMILES actually improves chemical language model performance through …

An end-to-end cheminformatics pipeline transforming 1D chemical formulas into 3D conformer datasets using graph …

Learn to implement VAEs in PyTorch: ELBO objective, reparameterization trick, loss scaling, and MNIST experiments on …