ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.
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DOI：
10.1039/c6sc05720a
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发表时间：
2017-04-01
期刊：
影响因子：
8.4
通讯作者：
Roitberg AE
中科院分区：
文献类型：
--
作者：
Smith JS;Isayev O;Roitberg AE
我们展示了一个在量子力学（QM）密度泛函理论（DFT）计算的能量数据集上训练的深度神经网络（NN）如何能够为含H、C、N和O原子的有机分子学习到一种准确且可迁移的原子势。
深度学习正在革新科学技术的许多领域，尤其是图像、文本和语音识别。在本文中，我们展示了一个在量子力学（QM）DFT计算上训练的深度神经网络（NN）如何能够为有机分子学习到一种准确且可迁移的势。我们引入了ANAKIN - ME（用于分子能量的精确神经网络引擎，Accurate NeurAl networK engINe for Molecular Energies），简称ANI。ANI是一种新方法，其设计目的是开发可迁移的神经网络势，它利用高度改进的贝赫勒（Behler）和帕里内洛（Parrinello）对称函数来构建单原子原子环境向量（AEV）作为分子表征。AEV提供了将神经网络训练到跨越构型和构象空间的数据的能力，这是此前在此规模上未曾实现的壮举。我们利用ANI构建了一个称为ANI - 1的势，它在GDB数据库的一个子集上进行训练，该子集包含最多8个重原子，以便预测含四种原子类型（H、C、N和O）的有机分子的总能量。为了获得分子势能面的加速但具有物理相关性的采样，我们还提出了一种简正模式采样（NMS）方法来生成分子构象。通过一系列案例研究，我们表明，与参考DFT计算相比，ANI - 1在比训练数据集中所包含的大得多的分子系统（多达54个原子）上具有化学准确性。
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms. Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.