163 research outputs found
Vector Field Oriented Diffusion Model for Crystal Material Generation
Discovering crystal structures with specific chemical properties has become
an increasingly important focus in material science. However, current models
are limited in their ability to generate new crystal lattices, as they only
consider atomic positions or chemical composition. To address this issue, we
propose a probabilistic diffusion model that utilizes a geometrically
equivariant GNN to consider atomic positions and crystal lattices jointly. To
evaluate the effectiveness of our model, we introduce a new generation metric
inspired by Frechet Inception Distance, but based on GNN energy prediction
rather than InceptionV3 used in computer vision. In addition to commonly used
metrics like validity, which assesses the plausibility of a structure, this new
metric offers a more comprehensive evaluation of our model's capabilities. Our
experiments on existing benchmarks show the significance of our diffusion
model. We also show that our method can effectively learn meaningful
representations
Optimized Crystallographic Graph Generation for Material Science
Graph neural networks are widely used in machine learning applied to
chemistry, and in particular for material science discovery. For crystalline
materials, however, generating graph-based representation from geometrical
information for neural networks is not a trivial task. The periodicity of
crystalline needs efficient implementations to be processed in real-time under
a massively parallel environment. With the aim of training graph-based
generative models of new material discovery, we propose an efficient tool to
generate cutoff graphs and k-nearest-neighbours graphs of periodic structures
within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework
to generate graphs in real-time during the training of neural network
architecture. Our tool can update a graph of a structure, making generative
models able to update the geometry and process the updated graph during the
forward propagation on the GPU side. Our code is publicly available at
https://github.com/aklipf/mat-graph
Automated rule base completion as Bayesian concept induction
Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method
Learning conceptual space representations of interrelated concepts
Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations associate each object from a given domain of interest with a point in a high-dimensional Euclidean space, but they do not model the concepts from this domain, and can thus not directly be used for categorization and related cognitive tasks. A natural solution is to represent concepts as Gaussians, learned from the representations of their instances, but this can only be reliably done if sufficiently many instances are given, which is often not the case. In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other. We show that this leads to substantially better predictions in a knowledge base completion task
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection
One of the long-standing challenges in lexical semantics consists in learning
representations of words which reflect their semantic properties. The
remarkable success of word embeddings for this purpose suggests that
high-quality representations can be obtained by summarizing the sentence
contexts of word mentions. In this paper, we propose a method for learning word
representations that follows this basic strategy, but differs from standard
word embeddings in two important ways. First, we take advantage of
contextualized language models (CLMs) rather than bags of word vectors to
encode contexts. Second, rather than learning a word vector directly, we use a
topic model to partition the contexts in which words appear, and then learn
different topic-specific vectors for each word. Finally, we use a task-specific
supervision signal to make a soft selection of the resulting vectors. We show
that this simple strategy leads to high-quality word vectors, which are more
predictive of semantic properties than word embeddings and existing CLM-based
strategies
Ontology completion using graph convolutional networks
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Unsupervised Learning of Distributional Relation Vectors
Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that cooccurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space
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