ReaxFF is a computationally efficient model for reactive molecular dynamics simulations that has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF training sets typically contain diverse properties with different units, some of which are more abundant (by orders of magnitude) than others. To find the best parameters, one conventionally minimizes a weighted sum of squared errors over all of the data in the training set. One of the challenges in such numerical optimizations is to assign weights so that the optimized parameters represent a good compromise among all the requirements defined in the training set. This work introduces a new loss function, called Balanced Loss, and a workflow that replaces weight assignment with a more manageable procedure. The training data are divided into categories with corresponding “tolerances”, i.e., acceptable root-mean-square errors for the categories, which define the expectations for the optimized ReaxFF parameters. Through the Log-Sum-Exp form of Balanced Loss, the parameter optimization is also a validation of one’s expectations, providing meaningful feedback that can be used to reconfigure the tolerances if needed. The new methodology is demonstrated with a nontrivial parametrization of ReaxFF for water adsorption on alumina. This results in a new force field that reproduces both the rare and frequent properties of a validation set not used for training. We also demonstrate the robustness of the new force field with a molecular dynamics simulation of water desorption from a γ-Al₂O₃ slab model.

中文翻译：

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管理反作用力场开发中的期望和不平衡训练数据：氧化铝上水吸附的应用

ReaxFF 是一种计算高效的反应分子动力学模拟模型，已应用于多种化学系统。当 ReaxFF 参数尚不可用于感兴趣的化学时，必须（重新）优化它们，为此定义一组新 ReaxFF 参数应重现的训练数据。 ReaxFF 训练集通常包含具有不同单位的多种属性，其中一些属性比其他属性更丰富（数量级）。为了找到最佳参数，通常会最小化训练集中所有数据的加权平方和。这种数值优化的挑战之一是分配权重，以便优化的参数代表训练集中定义的所有要求之间的良好折衷。这项工作引入了一种新的损失函数，称为平衡损失，以及用更易于管理的程序取代权重分配的工作流程。训练数据被分成具有相应“容差”的类别，即类别的可接受的均方根误差，其定义了优化的ReaxFF参数的期望。通过平衡损失的 Log-Sum-Exp 形式，参数优化也是对期望的验证，提供有意义的反馈，可用于在需要时重新配置容差。通过对氧化铝上水吸附的 ReaxFF 进行重要参数化，证明了新方法。这产生了一个新的力场，它再现了未用于训练的验证集的罕见和频繁属性。我们还通过 γ-Al ₂ O ₃板模型水解吸的分子动力学模拟证明了新力场的鲁棒性。