Publications (9)
Learned Force Fields Are Ready For Ground State Catalyst Discovery
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose +6
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery. Our key finding is that relaxation using forces fro…
Distral: Robust Multitask Reinforcement Learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki +5
Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data…
Policy Distillation
Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre +6
Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specif…
Hot Charge Transfer States and Charge Generation in Donor Acceptor Blends
James Kirkpatrick
In an organic blend the vibrational normal mode excited by exciton splitting is the same as the one coupled to charge hopping. Excess driving force for exciton splitting can theref…
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz +11
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been wi…
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt +5
In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both p…