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Found 16 papers, 11 papers with code
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning.
Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes
Such a reprocessing model would be characterised by lags between X-ray and optical/UV emission due to differences in light travel time.
Gaussian Processes
High Energy Astrophysical Phenomena
Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes
Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials.
Investigating 3D Atomic Environments for Enhanced QSAR
Most approaches use molecular descriptors based on a 2D representation of molecules as a graph of atoms and bonds, abstracting away the molecular shape.
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
A common bottleneck for materials discovery is synthesis.
Computational Physics Materials Science
Coarse-graining and designing liquids by combining machine learning with liquid state theory
Pioneering work in liquid state theory derived analytical closures for the framework.
Soft Condensed Matter Computational Physics
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs.
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost.
Antifragile and Robust Heteroscedastic Bayesian Optimisation
Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design.
Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction
Machine learning methods may have the potential to significantly accelerate drug discovery.
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery.
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
Geometry of energy landscapes and the optimizability of deep neural networks
Deep neural networks are workhorse models in machine learning with multiple layers of non-linear functions composed in series.
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
We derive a correspondence between parameter inference and free energy minimisation in statistical physics.
