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: A PyTorch ML Testbed
GPU-accelerated PyTorch framework for the Müller-Brown potential with JIT compilation and Langevin dynamics....
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 …...
Digital Restoration: Modernizing Rahman's 1964 Argon Simulation
How I used modern software engineering (caching, vectorization, and dependency locking) to reproduce a 60-year-old …...
Digital Restoration: Modernizing Rahman's 1964 Argon Simulation
A high-fidelity replication of foundational molecular dynamics using modern software engineering practices: caching, …...
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 …
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 …...