Molecular dynamics simulations are helpful tools for a range of applications, ranging from drug discovery to protein structure determination. The successful use of this technology largely depends on the potential function, or force field, used to determine the potential energy at each configuration of the system. Most force fields encode all of the relevant parameters to be used in distinct atom types, each associated with parameters for all parts of the force field, typically bond stretches, angle bends, torsions, and nonbonded terms accounting for van der Waals and electrostatic interactions. Much attention has been paid to the nonbonded parameters and their derivation, which are important in particular due to their governance of noncovalent interactions, such as protein-ligand binding. Parametrization involves adjusting the nonbonded parameters to minimize the error between simulation results and experimental properties, such as heats of vaporization and densities of neat liquids. In this setting, determining the best set of atom types is far from trivial, and the large number of parameters to be fit for the atom types in a typical force field can make it difficult to approach a true optimum. Here, we utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF 1.7 force field, even when trained on a relatively small amount of experimental data.

中文翻译：

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气相量子计算到一般力场Lennard-Jones参数的数据驱动映射。

分子动力学模拟是从药物发现到蛋白质结构确定等一系列应用的有用工具。该技术的成功使用在很大程度上取决于用于确定系统每种配置的势能的势函数或力场。大多数力场会编码要在不同原子类型中使用的所有相关参数，每个参数都与力场所有部分的参数相关联，通常是键合拉伸，角度弯曲，扭转和非键合项，这些都考虑了范德华力和静电相互作用。对于非键合参数及其推导已给予了很多关注，由于它们对非共价相互作用（如蛋白质-配体结合）的控制，这一点尤其重要。参数化包括调整非键合参数，以最大程度地减少模拟结果与实验特性（例如汽化热和纯液体密度）之间的误差。在这种情况下，确定最佳的原子类型集绝非易事，而在典型的力场中要适合于原子类型的大量参数可能会使实现真正的最佳值变得困难。在这里，我们利用先前描述的最小基础迭代股票持有人（MBIS）方法对电子密度进行分子内原子划分。然后使用一组映射参数将这些原子密度的信息映射到Lennard-Jones参数，该映射参数比力场中典型的原子类型数量小得多。这种方法在两个方面具有优势：它通过允许每个原子具有唯一的Lennard-Jones参数来消除原子类型，并且大大减少了要优化的参数数量。我们表明，即使在相对少量的实验数据上进行训练，该方法所产生的结果也可以与使用GAFF 1.7型力场获得的结果相媲美。