[go: up one dir, main page]

Schütte et al., 2012 - Google Patents

Optimal control of molecular dynamics using Markov state models

Schütte et al., 2012

View PDF
Document ID
15421643536351414518
Author
Schütte C
Winkelmann S
Hartmann C
Publication year
Publication venue
Mathematical programming

External Links

Snippet

A numerical scheme for solving high-dimensional stochastic control problems on an infinite time horizon that appear relevant in the context of molecular dynamics is outlined. The scheme rests on the interpretation of the corresponding Hamilton–Jacobi–Bellman equation …
Continue reading at publications.imp.fu-berlin.de (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
    • G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations

Similar Documents

Publication Publication Date Title
Schütte et al. Optimal control of molecular dynamics using Markov state models
Zhang et al. DeePCG: Constructing coarse-grained models via deep neural networks
Reuter et al. Generalized Markov modeling of nonreversible molecular kinetics
Marsili et al. Self-healing umbrella sampling: a non-equilibrium approach for quantitative free energy calculations
Ryczko et al. Toward orbital-free density functional theory with small data sets and deep learning
Fiedler et al. Predicting electronic structures at any length scale with machine learning
Vlachas et al. Accelerated simulations of molecular systems through learning of effective dynamics
Dong et al. Learning and robust control in quantum technology
Fabiani et al. Parsimonious physics-informed random projection neural networks for initial value problems of ODEs and index-1 DAEs
Shaidu et al. A systematic approach to generating accurate neural network potentials: The case of carbon
Van Speybroeck et al. Towards modeling spatiotemporal processes in metal–organic frameworks
Prinz et al. Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics
Sarich et al. Markov state models for rare events in molecular dynamics
Olsson et al. Dynamic graphical models of molecular kinetics
Bevan et al. Controlling assembly of colloidal particles into structured objects: Basic strategy and a case study
Marcolongo et al. Gauge fixing for heat-transport simulations
Ndengué et al. Rotational Excitations in CO–CO Collisions at Low Temperature: Time-Independent and Multiconfigurational Time-Dependent Hartree Calculations
Lin et al. Automatic evolution of machine-learning-based quantum dynamics with uncertainty analysis
Falletta et al. Unified differentiable learning of electric response
Jnane et al. Analog quantum simulation of non-condon effects in molecular spectroscopy
Chodera et al. Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty
Fernandez et al. Quantitative structure–property relationship modeling of electronic properties of graphene using atomic radial distribution function scores
Rydzewski Spectral map: Embedding slow kinetics in collective variables
Autieri et al. Dominant reaction pathways in high-dimensional systems
Singh et al. Variational path sampling of rare dynamical events