OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.256 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- The Journal of Physical Chemistry B
- Topic
- Machine Learning in Materials Science
- Field
- Materials Science
- Canadian institutions
- —
- Funders
- National Cancer InstituteNational Heart, Lung, and Blood InstituteHorizon 2020 Framework ProgrammeEMD SeronoNational Institutes of HealthNational Institute of General Medical SciencesMinisterio de Ciencia e InnovaciónAgencia Estatal de InvestigaciónXtalPiVir BiotechnologyRelay TherapeuticsParker Institute for Cancer ImmunotherapyEntasis TherapeuticsChan Zuckerberg InitiativeNew York UniversityCycle for SurvivalMerck KGaAEngineering and Physical Sciences Research CouncilYork UniversityAstraZenecaMemorial Sloan-Kettering Cancer CenterDamon Runyon Cancer Research FoundationNational Science Foundation
- Keywords
- Computer scienceMolecular dynamicsCUDAInterface (matter)Artificial intelligenceComputational scienceMachine learningParallel computingChemistryComputational chemistry
- Has abstract in OpenAlex
- yes