Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been focused on simulation techniques but relatively fewer works are done on methods for analyzing the simulation results. Large-scale simulations usually generate massive amounts of data, which make manual analysis infeasible, particularly when it is necessary to look into the details of the simulation results. In this dissertation, we propose a system that uses computational method to automatically perform analysis of simulation data, which represent atomic position-time series. The system identifies, in an automated fashion, the micro-level events (such as the bond formation/breaking) tha...
As computational power grows, materials simulation becomes an increasingly valuable scientific tool....
Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by ...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...
Time-varying data from simulations of dynamical systems are rich in spatio-temporal information. A k...
As the exploration of materials trends further towards the atomic scale, understanding the dynamic p...
Advances in computing power have made it possible for scientists to perform atomistic simulations of...
International audienceGraph theory algorithms have been proposed in order to identify, follow in tim...
Extracting and interpreting the information contained in large sets of time-varying three dimensiona...
We have proposed a scheme to support interactive visualization at space-time multiresolution of the ...
In this thesis we present an exploration system tailored for very large molecular simulations which ...
© 2019, The Author(s). Understanding the dynamical processes that govern the performance of function...
Molecular Dynamics (MD) is a computer simulation method that studies the physical movements of atom...
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling ...
For over 60 years computers have been used to simulate biological systems in molecular detail using ...
textRare-event phenomena are ubiquitous in nature. We propose a new strategy, kappa-dynamics, to mod...
As computational power grows, materials simulation becomes an increasingly valuable scientific tool....
Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by ...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...
Time-varying data from simulations of dynamical systems are rich in spatio-temporal information. A k...
As the exploration of materials trends further towards the atomic scale, understanding the dynamic p...
Advances in computing power have made it possible for scientists to perform atomistic simulations of...
International audienceGraph theory algorithms have been proposed in order to identify, follow in tim...
Extracting and interpreting the information contained in large sets of time-varying three dimensiona...
We have proposed a scheme to support interactive visualization at space-time multiresolution of the ...
In this thesis we present an exploration system tailored for very large molecular simulations which ...
© 2019, The Author(s). Understanding the dynamical processes that govern the performance of function...
Molecular Dynamics (MD) is a computer simulation method that studies the physical movements of atom...
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling ...
For over 60 years computers have been used to simulate biological systems in molecular detail using ...
textRare-event phenomena are ubiquitous in nature. We propose a new strategy, kappa-dynamics, to mod...
As computational power grows, materials simulation becomes an increasingly valuable scientific tool....
Molecular dynamics (MD) simulation is the workhorse of various scientific domains but is limited by ...
In this thesis, we extend the scope of atomistic simulations through a combination of machine learni...