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ILVES: Accurate and Efficient Bond Length and Angle Constraints in Molecular Dynamics All-atom, force field-based molecular dynamics simulations are essential tools in computational chemistry, enabling the prediction and analysis of biomolecular systems with atomic-level resolution. However, as system sizes and simulation time scales increase, so does the associated computational cost. To extend simulated time using the same resources, a common strategy is to constrain the fastest degrees of freedom, such as bond lengths, allowing for larger integration time steps without compromising accuracy. The de facto state-of-the-art algorithms for this purpose─SHAKE, LINCS, and P-LINCS─are integrated into most molecular dynamics packages and widely adopted across the field. Despite their impact, these methods exhibit limitations: all converge slowly when high numerical accuracy is required, and the LINCS and P-LINCS algorithms cannot handle general angular constraints, limiting further increases in time step. In this article, we introduce ILVES, a family of parallel algorithms that converge so rapidly that it is now practical to solve bond length and associated angular constraint equations as accurately as the hardware will allow. We have integrated ILVES into Gromacs, and our analysis demonstrates that it is superior to the state-of-the-art when constraining bond lengths. Due to its better convergence properties, we also show that if the time step is increased up to 3.5 fs by enforcing angular constraints, ILVES enables a 1.65× increase in simulated time using the same computational resources and wall-clock time, an outcome unattainable with current methods. This advance can significantly reduce the computational cost of most all-atom molecular dynamics simulations while improving their accuracy and extending access to larger systems and longer time scales.

#iqfpaper ILVES: Accurate and Efficient Bond Length and Angle Constraints in Molecular Dynamics. Published in Journal of Chemical Theory and Computation

Collaboration with @diis-uz.bsky.social @i3a.unizar.es @unizar.es #umeauniversity #BSC_CNS #cnr

bit.ly/3IltZ4A

🦠Reprograman bacterias para que ‘aprendan’ a degradar plásticos sin emplear #ADN externo

🔬La técnica del #CSIC y #BSC_CNS modifica las funciones celulares de las bacterias sin añadir genes exógenos

👨‍🔬Este método permite que aquellas que no degradan plásticos, consigan hacerlo

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