OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.Comment: 16 pages, 5 figure

Isochronal Sampling In Non-boltzmann Monte Carlo Methods.

Non-Boltzmann sampling (NBS) methods are usually able to overcome ergodicity issues which conventional Monte Carlo methods often undergo. In short, NBS methods are meant to broaden the sampling range of some suitable order parameter (e.g., energy). For many years, a standard for their development has been the choice of sampling weights that yield uniform sampling of a predefined parameter range. However, Trebst et al. [Phys. Rev. E 70, 046701 (2004)] demonstrated that better results are obtained by choosing weights that reduce as much as possible the average number of steps needed to complete a roundtrip in that range. In the present work, we prove that the method they developed to minimize roundtrip times also equalizes downtrip and uptrip times. Then, we propose a discrete-parameter extension using such isochronal character as our main goal. To assess the features of the new method, we carry out simulations of a spin system and of lattice chains designed to exhibit folding transition, thus being suitable models for proteins. Our results show that the new method performs on a par with the original method when the latter is applicable. However, there are cases in which the method of Trebst et al. becomes inapplicable, depending on the chosen order parameter and on the employed Monte Carlo moves. With a practical example, we demonstrate that our method can naturally handle these cases, thus being more robust than the original one. Finally, we find an interesting correspondence between the kind of approach dealt with here and the committor analysis of reaction coordinates, which is another topic of rising interest in the field of molecular simulation.13115411

2 o CONGRESSO BRASILEIRO DE P&D EM PETRÓLEO & GÁS POROSITY OF PACKED BEDS OF SPHEROCYLINDERS USING THE

Resumo – Aplicações de materiais granulares, tais como leitos fixos de partículas, são freqüentes no setor de petróleo e gás. Sólidos granulares podem também ser utilizados como adsorventes para armazenamento de gás natural em veículos. Portanto, o conhecimento de propriedades destes materiais é importante para um projeto de equipamentos seguro e eficiente. Neste trabalho, de forma a se avaliar a influência do formato das partículas sobre a porosidade de leitos de partículas, configurações de leitos monodispersos de esferocilindros de mesmo volume, mas com diferentes alongamentos, são obtidas através do método de Monte Carlo. Palavras-Chave: leitos de partículas; Monte Carlo; porosidade; esferocilindros Abstract – Applications of granular materials, such as packed particle beds, are frequent in the oil and gas sector. Granular solids can also be used as adsorbents for natural gas storage in vehicles. Therefore, knowledge of the properties of such materials is important for safe and efficient equipment design. In this work, in order to evaluate the influence of particle shape on the porosity of particle beds, configurations of monodispersed beds of spherocylinders with equal volume, but different elongations, are obtained using the Monte Carlo method