Converting SELFIES Strings to 2D Molecular Images
Learn how to visualize SELFIES molecular representations and explore their unique advantages through random sampling, …
Machine-Learning
SELFIES (Self-Referencing Embedded Strings)
SELFIES is a 100% robust string-based representation for chemical molecules, designed for machine learning applications …...
Müller-Brown Potential
The Müller-Brown potential: a classic two-dimensional analytical benchmark for testing optimization algorithms, reaction …...
Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …...
Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling
Lu et al. introduce SpaceFormer, a Transformer that models entire 3D molecular space—not just atoms—for superior …...
The Dark Side of the Forces: Assessing Non-Conservative Force Models for Atomistic Machine Learning
Bigi et al. critique non-conservative force models in ML potentials, showing their simulation failures and proposing …...
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Dai et al.'s NeurIPS 2021 paper introducing Noise Contrastive Priors (NCPs) to address VAE's 'prior hole' problem with …...
GEOM Dataset: 3D Molecular Conformer Generation
Learn how GEOM transforms 2D molecular graphs into dynamic 3D conformer ensembles for molecular machine learning …
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Weiler et al.'s NeurIPS 2018 paper introducing SE(3)-equivariant CNNs for volumetric data using group theory and …...
LLMs for Page Stream Segmentation
Enhanced TABME benchmark for page stream segmentation, creating TABME++, showing fine-tuned decoder-based LLMs …...
Modern PyTorch Techniques for VAEs: A Tutorial
VAE tutorial using modern PyTorch: torch.distributions, optimization techniques, numerical stability, and implementation …