18 citations · 23 across the 6 of their papers we have counts for
6 papers
Discovery and recovery of crystalline materials with property-conditioned transformers
Cyprien Bone, Matthew Walker, Bradley A. A. Martin +6
Generative models have recently shown great promise for accelerating the design and discovery of new functional materials. Conditional generation enhances this capacity by allowing…
Crystal Structure Generation with Autoregressive Large Language Modeling
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating an…
Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even to…
Distributed Representations of Atoms and Materials for Machine Learning
Luis M. Antunes, Ricardo Grau-Crespo, Keith T. Butler
The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based repres…
TensorX: Extensible API for Neural Network Model Design and Deployment
Davide Nunes, Luis Antunes
TensorX is a Python library for prototyping, design, and deployment of complex neural network models in TensorFlow. A special emphasis is put on ease of use, performance, and API c…
Neural Random Projections for Language Modelling
Davide Nunes, Luis Antunes
Neural network-based language models deal with data sparsity problems by mapping the large discrete space of words into a smaller continuous space of real-valued vectors. By learni…