Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
Molecular-Dynamics
Müller-Brown Basin MA: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the deep reactant minimum (Basin MA) of the Müller-Brown …
Müller-Brown Basin MB: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the product minimum (Basin MB) of the Müller-Brown potential …
Müller-Brown Potential in PyTorch
PyTorch implementation of the Müller-Brown potential with performance optimizations, MD simulations, and analytical vs …...
Müller-Brown Transition: Langevin Dynamics Simulation
Extended Langevin dynamics simulation showing particle transitions between different basins of the Müller-Brown …
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …...
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Fu et al. propose energy conservation as a key MLIP diagnostic and introduce eSEN, bridging test accuracy and real …...
Liquid Argon: LAMMPS Simulation
LAMMPS molecular dynamics simulation of liquid argon demonstrating fundamental liquid-state behavior and molecular …
Rahman's 1964 Liquid Argon Simulation Replication
Replication of Rahman's 1964 molecular dynamics simulation using modern LAMMPS and Python, achieving quantitative …...
Replicating Rahman's 1964 Liquid Argon Simulation
Recreating the foundational 1964 molecular dynamics simulation with modern Python and LAMMPS, testing whether Rahman's …...
The Dark Side of the Forces: Assessing Non-Conservative Force Models for Atomistic Machine Learning
Bigi et al. critique non-conservative force models in ML potentials, showing their simulation failures and proposing …...
Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
Daw and Baskes's foundational 1984 paper introducing the Embedded-Atom Method (EAM), a many-body potential for metal …...