ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise distributions through a VAE framework to improve denoising-based pre-training for molecular force field prediction, outperforming fixed Gaussian noise approaches on quantum chemistry benchmarks.
ICML 2025 paper introducing SpaceFormer, a Transformer architecture that challenges the atom-centric paradigm by modeling the continuous 3D space surrounding molecules using adaptive multi-resolution grids, ranking first in 10 of 15 molecular property prediction tasks.
ICML 2025 methodological paper introducing SPHNet, which uses adaptive network sparsification to overcome the computational bottleneck of tensor products in SE(3)-equivariant networks, achieving up to 7x speedup and 75% memory reduction on DFT Hamiltonian prediction tasks.
ICML 2025 paper proposing energy conservation metrics as critical diagnostics for machine learning interatomic potentials and introducing eSEN, a novel architecture designed to bridge the gap between test-set accuracy and real simulation performance on materials property prediction.
The foundational 1984 paper introducing EAM, a semi-empirical many-body interatomic potential that incorporates density functional theory concepts to accurately simulate metallic systems while maintaining computational efficiency comparable to pair potentials.
Torrie and Valleau’s 1977 paper introducing importance sampling with non-physical distributions to overcome the sampling gap problem in Monte Carlo free-energy calculations, particularly for phase transitions.
A NeurIPS 2021 method paper introducing Noise Contrastive Priors to address the VAE ‘prior hole’ problem, where standard Gaussian priors assign high density to regions of latent space that don’t correspond to realistic data, using energy-based models trained with contrastive learning to match the aggregate posterior.
SubGrapher introduces a visual fingerprinting approach to Optical Chemical Structure Recognition that detects functional groups directly from images, enabling chemical database searches without full structure reconstruction and handling complex patent images including Markush structures.
Weiler et al.’s NeurIPS 2018 paper introducing 3D Steerable CNNs that achieve SE(3) equivariance through group representation theory and spherical harmonic convolution kernels, eliminating the need for rotational data augmentation and improving data efficiency for scientific applications with rotational symmetry like molecular and protein structures.
Proposes Ring-Free Language (RFL) to hierarchically decouple molecular graphs into skeletons, rings, and branches, solving issues with 1D serialization of complex 2D structures. Introduces the Molecular Skeleton Decoder (MSD) to progressively predict these components, achieving strong results on handwritten and printed chemical structure recognition benchmarks.