Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

Machine learning (ML) has emerged as a pervasive tool in science,
engineering, and beyond. Its success has also led to several synergies with
molecular dynamics (MD) simulations, which we use to ident ...

The Specialized High-Performance Network on Anton 3

Molecular dynamics (MD) simulation, a computationally intensive method that
provides invaluable insights into the behavior of biomolecules, typically
requires large-scale parallelization. Implementati ...

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

A large fraction of computational science involves simulating the dynamics of
particles that interact via pairwise or many-body interactions. These
simulations, called Molecular Dynamics (MD), span a ...

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Parametric and non-parametric machine learning potentials have emerged
recently as a way to improve the accuracy of bio-molecular simulations. Here,
we present NNP/MM, an hybrid method integrating neu ...

Graph Neural Networks Accelerated Molecular Dynamics

Molecular Dynamics (MD) simulation is a powerful tool for understanding the
dynamics and structure of matter. Since the resolution of MD is atomic-scale,
achieving long time-scale simulations with fem ...

Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms

Scientific computing applications have benefited greatly from high
performance computing infrastructure such as supercomputers. However, we are
seeing a paradigm shift in the computational structure, ...

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

Scientific applications that involve simulation ensembles can be accelerated
greatly by using experiment design methods to select the best simulations to
perform. Methods that use machine learning (ML ...

TorchMD: A deep learning framework for molecular simulations

Molecular dynamics simulations provide a mechanistic description of molecules
by relying on empirical potentials. The quality and transferability of such
potentials can be improved leveraging data-dri ...

State Predictive Information Bottleneck

The ability to make sense of the massive amounts of high-dimensional data
generated from molecular dynamics (MD) simulations is heavily dependent on the
knowledge of a low dimensional manifold (known ...

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method
of choice for modeling complex atomistic phenomena from first principles.
However, most AIMD applications are limited by com ...