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Molecular Modelling

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Molecular Modelling

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Research Frontiers in Machine Learning Interatomic Potentials

Construction of neural network-based and graph neural network potentials for predicting atomic interactions with quantum accuracy at classical computational cost.

Equivariant Neural Networks for Molecular Force Fields

Transferability and Generalization in Machine-Learned Potentials

Quantum Mechanical Accuracy at Classical Simulation Speed

Uncertainty Quantification in Neural Network Interatomic Models

Many-Body Correlations in Machine Learning Potentials

Active Learning Strategies for Potential Energy Surfaces

Sparse Representations of Chemical Space in Neural Potentials

Reactive Dynamics with Machine-Learned Potential Energy Surfaces

Graph Neural Networks for Atomic Environment Encoding

Interpretability and Physical Constraints in ML Interatomic Models

All Molecular Modelling PhD categories