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Computational Interdisciplinary Science

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Computational Interdisciplinary Science

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Research Frontiers in Machine Learning for Molecular Dynamics Simulation

Developing neural network architectures to accelerate molecular dynamics simulations by learning force fields and potential energy surfaces from quantum mechanical data.

Neural Operators for Long-Timescale Molecular Evolution

Equivariant Graph Networks in Protein Conformational Landscapes

Differentiable Physics for Force Field Discovery

Sparse Latent Representations of Molecular Phase Transitions

Generative Models for Rare Event Sampling in Biomolecules

Uncertainty Quantification in Machine-Learned Interatomic Potentials

Hybrid Quantum-Classical Embeddings for Molecular Dynamics

Multi-Scale Information Flow in Coarse-Grained Simulations

Active Learning Strategies for Chemical Space Exploration

Temporal Symmetries and Conservation Laws in Neural Dynamics

All Computational Interdisciplinary Science PhD categories