@article {2022|2152,
	title = {Human Learning for Molecular Simulations: The Collective Variables Dashboard in VMD.},
	journal = {J Chem Theory Comput},
	volume = {18},
	year = {2022},
	month = {2022 Mar 08},
	pages = {1945-1956},
	abstract = {<p>The Collective Variables Dashboard is a software tool for real-time, seamless exploration of molecular structures and trajectories in a customizable space of collective variables. The Dashboard arises from the integration of the Collective Variables Module (also known as Colvars) with the visualization software VMD, augmented with a fully discoverable graphical interface offering interactive workflows for the design and analysis of collective variables. Typical use cases include a priori design of collective variables for enhanced sampling and free energy simulations as well as analysis of any type of simulation or collection of structures in a collective variable space. A combination of those cases commonly occurs when preliminary simulations, biased or unbiased, reveal that an optimized set of collective variables is necessary to improve sampling in further simulations. Then the Dashboard provides an efficient way to intuitively explore the space of likely collective variables, validate them on existing data, and use the resulting collective variable definitions directly in further biased simulations using the Collective Variables Module. Visualization of biasing energies and forces is proposed to help analyze or plan biased simulations. We illustrate the use of the Dashboard on two applications: discovering coordinates to describe ligand unbinding from a protein binding site and designing volume-based variables to bias the hydration of a transmembrane pore.</p>
},
	issn = {1549-9626},
	doi = {10.1021/acs.jctc.1c01081},
	author = {J{\'e}r{\^o}me H{\'e}nin and Lopes, Laura J S and Giacomo Fiorin}
}
@article {2020|2142,
	title = {Scalable molecular dynamics on CPU and GPU architectures with NAMD},
	journal = {The Journal of Chemical Physics},
	volume = {153},
	year = {2020},
	chapter = {044130},
	abstract = {<p>NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.</p>
},
	keywords = {NAMD},
	doi = {10.1063/5.0014475},
	url = {https://aip.scitation.org/doi/10.1063/5.0014475},
	author = {James Phillips and David Hardy and Julio Maia and John Stone and Joao Ribeiro and Rafael Bernardi and Ronak Buch and Giacomo Fiorin and J{\'e}r{\^o}me H{\'e}nin and Wei Jiang and Ryan McGreevy and Melo, Marcelo Cardoso dos Reis and Brian Radak and Robert Skeel and Abhishek Singharoy and Yi Wang and Benoit Roux and Aleksei Aksimentiev and Zan Luthey-Schulten and Laxmikant Kale and Klaus Schulten and Christophe Chipot and Emad Tajkhorshid}
}
@article {2013|1936,
	title = {Using collective variables to drive molecular dynamics simulations},
	journal = {Mol. Phys.},
	volume = {111},
	number = {22-23},
	year = {2013},
	pages = {3345{\textendash}3362},
	doi = {10.1080/00268976.2013.813594},
	author = {Giacomo Fiorin and Michael L Klein and J{\'e}r{\^o}me H{\'e}nin}
}
@article {2010|1851,
	title = {Exploring Multidimensional Free Energy Landscapes Using Time-Dependent Biases on Collective Variables},
	journal = {J. Chem. Theory Comput.},
	volume = {6},
	number = {1},
	year = {2010},
	pages = {35{\textendash}47},
	author = {J{\'e}r{\^o}me H{\'e}nin and Giacomo Fiorin and Christophe Chipot and Michael L Klein}
}
