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Found 9 papers, 7 papers with code
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
Ranked #1 on
Band Gap
on Materials Project
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
Periodic Graph Transformers for Crystal Material Property Prediction
Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly.
Ranked #2 on
Band Gap
on JARVIS-DFT
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation
In this work, we propose GraphEBM, a molecular graph generation method via energy-based models (EBMs), as an exploratory work to perform permutation invariant and multi-objective molecule generation.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
GraphDF: A Discrete Flow Model for Molecular Graph Generation
We consider the problem of molecular graph generation using deep models.
GraphEBM: Molecular Graph Generation with Energy-Based Models
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.
