2 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Discovering new photovoltaics using optimal transport theory
Matthew A. H. Walker, Zibo Zhou, Junayd Ul Islam +1
Searching by chemical and structural analogy is one of the most commonly used and successful approaches to materials discovery. However, formulating this task for algorithmic imple…
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…
Learning disentangled latent representations facilitates discovery and design of functional materials
Jaehoon Cha, Tingyao Lu, Matthew Walker +1
The discovery of new materials is often constrained by the need for large labelled datasets or expensive simulations. In this study, we explore the use of Disentangling Autoencoder…
The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?
Matthew Walker, Keith T. Butler
Computational screening has become a powerful complement to experimental efforts in the discovery of high-performance photovoltaic (PV) materials. Most workflows rely on density fu…