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Deep-Learning

Computational Chemistry

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 …...

Computational Chemistry

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 …...

Computational Chemistry

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity

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

Computational Chemistry

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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

Computational Chemistry

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 …...

Generative Modeling

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 …...

Computational Chemistry
Comparison of 2D molecular graph versus 3D conformer ensemble showing latanoprost molecule in multiple conformations

GEOM Dataset: 3D Molecular Conformer Generation

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

Deep 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 …...

Generative Modeling
Variational Autoencoder architecture diagram showing encoder, latent space, and decoder

Modern PyTorch Techniques for VAEs: A Tutorial

VAE tutorial using modern PyTorch: torch.distributions, optimization techniques, numerical stability, and implementation …

Generative Modeling
Wasserstein distance visualization showing Earth-Mover distance concept for GAN training

GAN Objective Functions: A Comprehensive Guide

Complete guide to GAN objective functions including WGAN, LSGAN, Fisher GAN, and more. Understand which loss function to …

Generative Modeling
Illustration of GAN training process showing adversarial competition between generator and discriminator

Understanding Generative Adversarial Networks (GANs)

Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …