Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning
James P. Andrews, Olga Gkountouna, Estela Blaisten‐Barojas
Abstract
protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.
Topics & Concepts
Computer scienceEnergeticsArtificial neural networkAggregate (composite)Artificial intelligenceSeries (stratigraphy)Machine learningTime seriesBiological systemNanotechnologyMaterials sciencePhysicsThermodynamicsPaleontologyBiologyMachine Learning in Materials ScienceComputational Drug Discovery MethodsProtein Structure and Dynamics