• Corpus ID: 238634584

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

@article{Xie2021CrystalDV,
  title={Crystal Diffusion Variational Autoencoder for Periodic Material Generation},
  author={Tian Xie and Xiang Fu and Octavian-Eugen Ganea and Regina Barzilay and T. Jaakkola},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06197}
}
Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms: 1) the coordinates must lie in the local energy minimum defined by quantum mechanics, and 2) global stability also requires the structure to follow the complex, yet specific bonding preferences between different atom types. Existing methods… 

Data-driven discovery of 2D materials by deep generative models

Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here, we show that a crystal diffusion variational

Physics Guided Generative Adversarial Networks for Generations of Crystal Materials with Symmetry Constraints

Discovering new materials is a long-standing challenging task that is critical to the progress of human society. Conventional approaches such as trial-and-error experiments and computational

Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

A parameterized Hamiltonian that strictly satisfies rotational equivariance and parity symmetry simultaneously is proposed, based on which an E(3) equivariant neural network called HamNet is developed to predict the ab initio tight-binding Hamiltonian of various molecules and solids.

Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints

Discovering new materials is a long-standing challenging task that is crucial to the progress of human society. Conventional approaches based on trial-and-error experiments and computational

A universal graph deep learning interatomic potential for the periodic table

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries

Atomic structure generation from reconstructing structural fingerprints

This work implements this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model and is able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept.

Score-based denoising for atomic structure identification

We propose an accurate method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts

A Score-based Geometric Model for Molecular Dynamics Simulations

A novel model called ScoreMD, which perturbs the molecular structure with a conditional noise depending on atomic accelerations and employs conformations at previous timeframes as the prior distribution for sampling, and incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations.

Deep Generative Model for Periodic Graphs

A new deep generative model for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns, and designs a new model learning objective that helps ensure the invariance of local-semantic representations for the graphs with the same local structure.

Equivariant Networks for Crystal Structures

A class of models that are equivariant with respect to crystalline symmetry groups are introduced, by generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph.

References

SHOWING 1-10 OF 72 REFERENCES

Inverse design of crystals using generalized invertible crystallographic representation

A generalized invertible representation that encodes the crystallographic information into the descriptors in both real space and reciprocal space is presented and it is shown that the VAE model predicts novel crystal structures that do not exist in the training and test database with targeted formation energies and band gaps.

Generative Adversarial Networks for Crystal Structure Prediction

These findings suggest that the generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.

Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on

A universal graph deep learning interatomic potential for the periodic table

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries

GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

This paper proposes a novel generative model named GEODIFF for molecular conformation prediction that treats each atom as a particle and learns to directly reverse the diffusion process (i.e., transforming from a noise distribution to stable conformations) as a Markov chain.

Rotation Invariant Graph Neural Networks using Spin Convolutions

A novel approach to modeling angular information between sets of neighboring atoms in a graph neural network is introduced and rotation invariance is achieved for the network’s edge messages through the use of a per-edge local coordinate frame and a novel spin convolution over the remaining degree of freedom.

Inverse Design of Solid-State Materials via a Continuous Representation

Learning Gradient Fields for Molecular Conformation Generation

A novel algorithm based on recent score-based generative models to effectively estimate the gradient fields of the log density of atomic coordinates is developed, which outperforms previous state-of-the-art baselines by a significant margin.

LEARNING NEURAL GENERATIVE DYNAMICS FOR MOLECULAR CONFORMATION GENERATION

A novel probabilistic framework to generate valid and diverse conformations given a molecular graph, enjoying a high model capacity to estimate the multimodal conformation distribution and explicitly capturing the complex long-range dependencies between atoms in the observation space is proposed.

SchNet - A deep learning architecture for molecules and materials.

The deep learning architecture SchNet is presented that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers and employs SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules.
...