Machine learning for &lt;i&gt;in silico&lt;/i&gt; protein research

Zhang, Jia-Hui; ,

doi:10.7498/aps.73.20231618

Cited by 2 publications

(1 citation statement)

References 123 publications

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“…可迁移性, 在应用于复杂体系时亦需要经过繁琐的人为调参过程. 随着近年来机器学习势函数 [9][10][11][12][13][14] 的发展, 其较高的精度和良好的通用性为解决上述问题提供了一个可能的方案. 不同于传统的以模型为核心的建模方法(常被称为模型驱动的方法), 机器学习方法是一种以数据为核心(数据驱动)的建模方法, 能够从大量数据中自主学习.…”

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Modeling ferroelectric phase transitions with graph convolutional neural networks

Ouyang,

Zhang,

Wang

et al. 2024

Acta Phys. Sin.

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Ferroelectric materials are widely used in functional devices, however, achieving convenient and accurate theoretical modeling of them has been a long-standing issue. Here, we propose a noval approach for the modeling of ferroelectric materials using graph convolutional neural networks (GNN). This approach utilizes GNNs to approximate the potential energy surface of ferroelectric materials, which then serves as a calculator to enable large-scale molecular dynamics simulations. Given atomic positions, the well-trained GNN model can provide accurate predictions on the potential energy and atomic forces, reaching a level of accuracy of 1 meV, which is comparable to ab initio calculations but with much less time expense by orders of magnitude. Benefiting from the high accuracy and fast prediction of neural networks, we further combine it with molecular dynamics simulations to investigate two representative ferroelectric materials—bulk GeTe and CsSnI3, and successfully produce their temperature-dependent structural phase transitions, which are in good agreement with experimental observations. During the GNN-based MD simulation of GeTe, we obeserve an unusual negative thermal expansion around the region of its ferroelectric phase transition, which has been reported in previous experiments. For CsSnI3, we correctly obtain the octahedron tilting patterns associated with its phase transition sequence. These results demonstrate the accuracy and reliability of GNNs in the modeling of potential energy surfaces of ferroelectric materials, providing a universal approach for their theoretical investigations.

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Modeling ferroelectric phase transitions with graph convolutional neural networks

Ouyang,

Zhang,

Wang

et al. 2024

Acta Phys. Sin.

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Progress in Protein Pre-training Models Integrated with Structural Knowledge

Tang Tian-Yi,

Xiong Yi-Ming,

Zhang Rui-Ge

et al. 2024

Acta Phys. Sin.

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The AI revolution sparked by natural language and image processing has brought new ideas and research paradigms to the field of protein computing. One significant advancement is the development of pre-trained protein language models through self-supervised learning from massive protein sequences. These pre-trained models encode various information about protein sequences, evolution, structures, and even functions, which can be easily transferred to various downstream tasks and demonstrate robust generalization capabilities. Recently, researchers are further developing multimodal pre-trained models that integrate more diverse types of data. This review summarizes the recent studies in this direction from the following aspects. Firstly, it reviews protein pre-trained models that integrate protein structures into language models; this is of particular importance since protein structure is the primary determinant of its function. Secondly, the pre-trained models that integrate protein dynamic information are introduced. These models may benefit downstream tasks such as protein-protein interactions, soft docking of ligands, and interactions involving allosteric proteins and intrinsic disordered proteins. Thirdly, the pre-trained models that integrate knowledge such as gene ontology are described. Fourthly, we briefly introduce pretrained models in RNA fields. Lastly, we introduce the most recent developments in protein designs and discuss the relations of these models with respect to the aforementioned pre-trained models that integrate protein structure information.

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Machine learning for <i>in silico</i> protein research

Cited by 2 publications

References 123 publications

Modeling ferroelectric phase transitions with graph convolutional neural networks

Modeling ferroelectric phase transitions with graph convolutional neural networks

Progress in Protein Pre-training Models Integrated with Structural Knowledge

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