596 citations · 1.3k across the 9 of their papers we have counts for
10 papers
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Janosh Riebesell, T. Wesley Surta, Rhys Goodall +2
Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practi…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
Abhijith S. Parackal, Rhys E. A. Goodall, Felix A. Faber +1
The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically…
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…
Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining
Rhys E. A. Goodall, Abhijith S. Parackal, Felix A. Faber +2
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that mac…
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
Shreshth A. Malik, Rhys E. A. Goodall, Alpha A. Lee
A common bottleneck for materials discovery is synthesis. While recent methodological advances have resulted in major improvements in the ability to predicatively design novel mate…
Machine learnt approximations to the bridge function yield improved closures for the Ornstein-Zernike equation
Rhys E. A. Goodall, Alpha A. Lee
A key challenge for soft materials design and coarse-graining simulations is determining interaction potentials between components that give rise to desired condensed-phase structu…
Materials Informatics Reveals Unexplored Structure Space in Cuprate Superconductors
Rhys E. A. Goodall, Bonan Zhu, Judith L. MacManus-Driscoll +1
High-temperature superconducting cuprates have the potential to be transformative in a wide range of energy applications. In this work we analyse the corpus of historical data abou…
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Rhys E. A. Goodall, Alpha A. Lee
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a k…