Learning molecular potentials with neural networks

人工神经网络 计算机科学 困境 人工智能 机器学习 领域(数学) 神经系统网络模型 计算模型 多样性(控制论) 数据科学 循环神经网络 人工神经网络的类型 数学 几何学 纯数学
作者
Hatice Gökcan,Olexandr Isayev
出处
期刊:Wiley Interdisciplinary Reviews: Computational Molecular Science [Wiley]
卷期号:12 (2) 被引量:36
标识
DOI:10.1002/wcms.1564
摘要

Abstract The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics > Molecular Interactions Software > Molecular Modeling
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
感动书文完成签到,获得积分10
1秒前
一往之前发布了新的文献求助10
2秒前
在水一方应助sss采纳,获得10
2秒前
可靠的电源完成签到,获得积分10
2秒前
star关注了科研通微信公众号
3秒前
科研通AI2S应助晶生采纳,获得10
3秒前
Leafff发布了新的文献求助10
6秒前
yu完成签到,获得积分10
6秒前
pace完成签到,获得积分10
7秒前
不配.应助一往之前采纳,获得20
7秒前
一二三完成签到,获得积分20
8秒前
Schroenius完成签到 ,获得积分10
8秒前
云瑾应助葛辉辉采纳,获得20
8秒前
8秒前
ailemonmint完成签到 ,获得积分10
9秒前
12秒前
bkagyin应助wangayting采纳,获得30
13秒前
13秒前
14秒前
可爱的函函应助睡不醒采纳,获得10
15秒前
16秒前
一往之前完成签到,获得积分10
16秒前
mayue发布了新的文献求助10
16秒前
17秒前
xzc发布了新的文献求助10
18秒前
狠毒的小龙虾完成签到,获得积分10
20秒前
Singularity应助斯文棒球采纳,获得10
22秒前
Heart发布了新的文献求助10
23秒前
情怀应助YCWZ采纳,获得10
24秒前
stuffmatter完成签到,获得积分0
24秒前
科研通AI2S应助Zzzz采纳,获得10
24秒前
XYF完成签到 ,获得积分10
24秒前
Singularity应助lily88采纳,获得10
25秒前
26秒前
平常的蜜粉完成签到,获得积分10
26秒前
Halo完成签到,获得积分10
28秒前
28秒前
29秒前
31秒前
高分求助中
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Very-high-order BVD Schemes Using β-variable THINC Method 568
Chen Hansheng: China’s Last Romantic Revolutionary 500
XAFS for Everyone 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3137922
求助须知:如何正确求助?哪些是违规求助? 2788820
关于积分的说明 7788709
捐赠科研通 2445219
什么是DOI,文献DOI怎么找? 1300219
科研通“疑难数据库(出版商)”最低求助积分说明 625878
版权声明 601046