14 citations · 31 across the 10 of their papers we have counts for
10 papers
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
Laurence I. Midgley, Chen Lin, J. Harry Moore +8
We present an evaluation of CSP-MACE-Å, a machine learning interatomic potential intended to replace DFT in crystal structure prediction (CSP). We decompose the total energy into s…
Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
Fengzhe Zhang, Laurence I. Midgley, José Miguel Hernández-Lobato
Score-based diffusion models (SBDMs) are powerful amortized samplers for Boltzmann distributions; however, imperfect score estimates bias downstream Monte Carlo estimates. Classica…
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models
Fengzhe Zhang, Jiajun He, Laurence I. Midgley +2
Diffusion models have shown promising potential for advancing Boltzmann Generators. However, two critical challenges persist: (1) inherent errors in samples due to model imperfecti…
SE(3) Equivariant Augmented Coupling Flows
Laurence I. Midgley, Vincent Stimper, Javier Antorán +3
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coup…
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Clément Bonnet, Daniel Luo, Donal Byrne +21
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for…
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Clément Bonnet, Laurence Midgley, Alexandre Laterre
Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-grad…
Synthesis of separation processes with reinforcement learning
Stephan C. P. A. van Kalmthout, Laurence I. Midgley, Meik B. Franke
This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence.…
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +2
Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for…
Bootstrap Your Flow
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +1
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current fl…
Deep Reinforcement Learning for Process Synthesis
Laurence Illing Midgley
This paper demonstrates the application of reinforcement learning (RL) to process synthesis by presenting Distillation Gym, a set of RL environments in which an RL agent is tasked…