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Found 11 papers, 2 papers with code
Accurate and scalable exchange-correlation with deep learning
Skala achieves chemical accuracy for atomization energies of small molecules while retaining the computational efficiency typical of semi-local DFT.
Improving Equivariant Networks with Probabilistic Symmetry Breaking
However, equivariant networks cannot break symmetries: the output of an equivariant network must, by definition, have at least the same self-symmetries as the input.
On the Identifiability of Causal Abstractions
We introduce a theoretical framework that calculates the degree to which we can identify a causal model, given a set of possible interventions, up to an abstraction that describes the system at a higher level of granularity.
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis.
Symmetry-Aware Generative Modeling through Learned Canonicalization
Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations.
Symmetry Breaking and Equivariant Neural Networks
Using symmetry as an inductive bias in deep learning has been proven to be a principled approach for sample-efficient model design.
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node.
Equivariant Networks for Crystal Structures
Supervised learning with deep models has tremendous potential for applications in materials science.
Equivariance with Learned Canonicalization Functions
Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations.
Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests
Crystal graph convolutional neural networks (CGCNN), materials graph network (MEGNet) and random forests are trained on the Materials Project database that contains the results of high-throughput DFT predictions.
Gradient Starvation: A Learning Proclivity in Neural Networks
We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks.
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