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Automated force field optimisation of small molecules using a gradient-based workflow package

机译:使用基于梯度的工作流程包对小分子进行自动力场优化

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In this study, the recently developed gradient-based optimisation workflow for the automated development of molecular models is for the first time applied to the parameterisation of force fields for molecular dynamics simulations. As a proof-of-concept, two small molecules (benzene and phosgene) are considered. In order to optimise the underlying intermolecular force field (described by the (12,6)-Lennard-Jones and the Coulomb potential), the energetic and diameter parameters ε and σ are fitted to experimental physical properties by gradient-based numerical optimisation techniques. Thereby, a quadratic loss function between experimental and simulated target properties is minimised with respect to the force field parameters. In this proof-of-concept, the considered physical target properties are chosen to be diverse: density, enthalpy of vapourisation and self-diffusion coefficient are optimised simultaneously at different temperatures. We found that in both cases, the optimisation could be successfully concluded by fulfillment of a pre-defined stopping criterion. Since a fairly small number of iterations were needed to do so, this study will serve as a good starting point for more complex systems and further improvements of the parametrisation task.View full textDownload full textKeywordsforce field development, numerical optimisation, gradient-based algorithms, molecular dynamics, Lennard-Jones potentialRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/08927022.2010.513974
机译:在这项研究中,最近开发的用于分子模型自动开发的基于梯度的优化工作流首次应用于分子动力学模拟的力场参数化。作为概念验证,考虑了两个小分子(苯和光气)。为了优化潜在的分子间力场(由(12,6)-Lennard-Jones和库仑势描述),通过基于梯度的数值优化技术将能量和直径参数和Ï拟合到实验物理性质。因此,相对于力场参数,实验和模拟目标属性之间的二次损失函数最小。在此概念验证中,考虑的物理目标属性应选择为多种多样:同时在不同温度下优化密度,汽化焓和自扩散系数。我们发现,在两种情况下,都可以通过满足预定的停止标准来成功地完成优化。由于这样做的迭代次数很少,因此,本研究将为更复杂的系统和参数化任务的进一步改进提供一个良好的起点。查看全文下载全文关键字关键字字段开发,数值优化,基于梯度的算法,分子动力学,伦纳德·琼斯电位,相关变量var addthis_config = {ui_cobrand:“泰勒和弗朗西斯在线”,servicescompact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,更多”,发布:“ ra -4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/08927022.2010.513974

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