TSNet: predicting transition state structures with tensor field networks and transfer learning.
TSNet: predicting transition state structures with tensor field networks and transfer learning.
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DOI：
10.1039/d1sc01206a
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发表时间：
2021-07-28
期刊：
影响因子：
8.4
通讯作者：
Pearson J
中科院分区：
文献类型：
--
作者：
Jackson R;Zhang W;Pearson J
过渡态是化学中最重要的分子结构之一，对反应动力学、催化剂设计和蛋白质功能研究等许多领域都至关重要。然而，过渡态是非常不稳定的，通常只存在于飞秒量级。这些结构的瞬态性质使它们难以研究，因此化学家们经常求助于模拟。不幸的是，过渡态的计算机模拟也具有挑战性，因为它们是高维数学表面上的一阶鞍点。定位这些点是资源密集且不可靠的，导致方法可能需要很长时间才能收敛。机器学习是一种相对较新的算法，由于其具有高度精确的函数近似能力，它已经导致了几个计算领域的根本性变化，包括计算机视觉和自然语言处理。虽然机器学习在计算化学中被广泛采用，作为昂贵的量子力学计算的轻量级替代方案，但利用机器学习进行过渡态结构优化的研究很少。本文提出了一种新的基于张量场网络的端到端连体消息传递神经网络TSNet，该网络具有预测过渡状态几何的能力。本文还介绍了一个SN2反应的小数据集，其中包括过渡态结构——这是第一个专门为机器学习构建的数据集。最后，研究了迁移学习这一低数据补救技术，以了解在广泛可用的化学数据上预训练TSNet的可行性，该技术可以在训练过程中提供更好的起点，更快的收敛速度和更低的损失值。将详细讨论新数据集和模型的各个方面，以及基于机器学习的过渡状态预测的动机和未来的总体展望。过渡态是化学中最重要的分子结构之一，对反应动力学、催化剂设计和蛋白质功能研究等许多领域都至关重要。
Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of SN2 reactions which includes transition state structures – the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction. Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
3
作者：
Bento, AP;Solà, M;Bickelhaupt, FM
通讯作者：
Bickelhaupt, FM
影响因子：
3.7
作者：
Bartok, Albert P.;Kondor, Risi;Csanyi, Gabor
通讯作者：
Csanyi, Gabor
影响因子：
41.2
作者：
Ekins, Sean;Puhl, Ana C.;Clark, Alex M.
通讯作者：
Clark, Alex M.
DOI：
10.1007/bf01113251
发表时间：
1992-04-01
期刊：
THEORETICA CHIMICA ACTA
影响因子：
--
作者：
CULOT, P;DIVE, G;GHUYSEN, JM
通讯作者：
GHUYSEN, JM