Automatica最新文献

英文中文

Predict globally, correct locally: Parallel-in-time optimization of neural networks 全局预测，局部修正：神经网络的并行实时优化

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-30 DOI: 10.1016/j.automatica.2024.111976

Panos Parpas, Corey Muir

The training of neural networks can be formulated as an optimal control problem of a dynamical system. The initial conditions of the dynamical system are given by the data. The objective of the control problem is to transform the initial conditions in a form that can be easily classified or regressed using linear methods. This link between optimal control of dynamical systems and neural networks has proved beneficial both from a theoretical and from a practical point of view. Several researchers have exploited this link to investigate the stability of different neural network architectures and develop memory efficient training algorithms. In this paper, we also adopt the dynamical systems view of neural networks, but our aim is different from earlier works. Instead, we develop a novel distributed optimization algorithm. The proposed algorithm addresses the most significant obstacle for distributed algorithms for neural network optimization: the network weights cannot be updated until the forward propagation of the data, and backward propagation of the gradients are complete. Using the dynamical systems point of view, we interpret the layers of a (residual) neural network as the discretized dynamics of a dynamical system and exploit the relationship between the co-states (adjoints) of the optimal control problem and backpropagation. We then develop a parallel-in-time method that updates the parameters of the network without waiting for the forward or back propagation algorithms to complete in full. We establish the convergence of the proposed algorithm. Preliminary numerical results suggest that the algorithm is competitive and more efficient than the state-of-the-art.

神经网络的训练可以表述为一个动态系统的最优控制问题。动态系统的初始条件由数据给出。控制问题的目标是将初始条件转换为一种易于使用线性方法进行分类或回归的形式。事实证明，动态系统优化控制与神经网络之间的这种联系无论从理论还是从实践角度来看都是有益的。一些研究人员利用这种联系研究了不同神经网络架构的稳定性，并开发出了高效记忆训练算法。在本文中，我们也采用了神经网络的动力系统观点，但我们的目标与之前的研究有所不同。相反，我们开发了一种新型分布式优化算法。所提出的算法解决了神经网络分布式优化算法的最大障碍：在数据的前向传播和梯度的后向传播完成之前，网络权重不能更新。我们从动态系统的角度出发，将（残差）神经网络的层解释为动态系统的离散动态，并利用最优控制问题的共态（邻接）与反向传播之间的关系。然后，我们开发了一种实时并行方法，无需等待前向或反向传播算法全部完成即可更新网络参数。我们确定了所提算法的收敛性。初步的数值结果表明，该算法具有竞争力，比最先进的算法更高效。

{"title":"Predict globally, correct locally: Parallel-in-time optimization of neural networks","authors":"Panos Parpas, Corey Muir","doi":"10.1016/j.automatica.2024.111976","DOIUrl":"10.1016/j.automatica.2024.111976","url":null,"abstract":"<div><div>The training of neural networks can be formulated as an optimal control problem of a dynamical system. The initial conditions of the dynamical system are given by the data. The objective of the control problem is to transform the initial conditions in a form that can be easily classified or regressed using linear methods. This link between optimal control of dynamical systems and neural networks has proved beneficial both from a theoretical and from a practical point of view. Several researchers have exploited this link to investigate the stability of different neural network architectures and develop memory efficient training algorithms. In this paper, we also adopt the dynamical systems view of neural networks, but our aim is different from earlier works. Instead, we develop a novel distributed optimization algorithm. The proposed algorithm addresses the most significant obstacle for distributed algorithms for neural network optimization: the network weights cannot be updated until the forward propagation of the data, and backward propagation of the gradients are complete. Using the dynamical systems point of view, we interpret the layers of a (residual) neural network as the discretized dynamics of a dynamical system and exploit the relationship between the co-states (adjoints) of the optimal control problem and backpropagation. We then develop a parallel-in-time method that updates the parameters of the network without waiting for the forward or back propagation algorithms to complete in full. We establish the convergence of the proposed algorithm. Preliminary numerical results suggest that the algorithm is competitive and more efficient than the state-of-the-art.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"171 ","pages":"Article 111976"},"PeriodicalIF":4.8,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Set-based value operators for non-stationary and uncertain Markov decision processes 非稳态和不确定马尔可夫决策过程的基于集合的值算子

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-28 DOI: 10.1016/j.automatica.2024.111970

Sarah H.Q. Li , Assalé Adjé , Pierre-Loïc Garoche , Behçet Açıkmeşe

This paper analyzes finite-state Markov Decision Processes (MDPs) with nonstationary and uncertain parameters via set-based fixed point theory. Given compact parameter ambiguity sets, we demonstrate that a family of contraction operators, including the Bellman operator and the policy evaluation operator, can be extended to set-based contraction operators with a unique fixed point—a compact value function set. For non-stationary MDPs, we show that while the value function trajectory diverges, its Hausdorff distance from this fixed point converges to zero. In parameter uncertain MDPs, the fixed point’s extremum value functions are equivalent to the min–max value function in robust dynamic programming under the rectangularity condition. Furthermore, we show that the rectangularity condition is a sufficient condition for the fixed point to contain its own extremum value functions. Finally, we derive novel guarantees for probabilistic path planning in capricious wind fields and stratospheric station-keeping.

本文通过基于集合的定点理论分析了具有非稳态和不确定参数的有限状态马尔可夫决策过程（MDP）。给定紧凑的参数模糊集，我们证明包括贝尔曼算子和策略评估算子在内的一系列收缩算子可以扩展为具有唯一固定点--紧凑的价值函数集--的基于集合的收缩算子。对于非稳态 MDP，我们证明了虽然价值函数轨迹会发散，但其与该固定点的豪斯多夫距离会收敛为零。在参数不确定的 MDPs 中，在矩形条件下，固定点的极值函数等同于鲁棒动态程序设计中的最小值函数。此外，我们还证明了矩形条件是定点包含自身极值函数的充分条件。最后，我们推导出了在反复无常的风场和平流层定站中进行概率路径规划的新保证。

引用次数: 0

Nonuniqueness and convergence to equivalent solutions in observer-based inverse reinforcement learning 基于观测器的逆强化学习中的非唯一性和向等效解的收敛性

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-25 DOI: 10.1016/j.automatica.2024.111977

Jared Town , Zachary Morrison , Rushikesh Kamalapurkar

A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.

在线实时解决确定性反强化学习（IRL）问题的一个关键挑战是存在多个解决方案。非唯一性要求研究等效解的概念，即成本函数不同但反馈矩阵相同的解。虽然文献中已经开发出了收敛到等效解的离线算法，但解决非唯一性问题的在线实时技术却尚未问世。本文开发了一种可收敛到 IRL 问题近似等效解的正则化历史堆栈观测器。为了便于分析，本文提出了新的数据丰富性条件，并提供了仿真结果来证明所开发技术的有效性。

引用次数: 0

Successive over relaxation for model-free LQR control of discrete-time Markov jump systems 离散时马尔可夫跳跃系统无模型 LQR 控制的连续过度松弛

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-25 DOI: 10.1016/j.automatica.2024.111919

Wenwu Fan, Junlin Xiong

This paper aims to solve the model-free linear quadratic regulator problem for discrete-time Markov jump linear systems without requiring an initial stabilizing control policy. We propose both model-based and model-free successive over relaxation algorithms to learn the optimal control policy of discrete-time Markov jump linear systems. The model-free value iteration algorithm is a special case of our model-free algorithm when the relaxation factor equals one. A sufficient condition on the relaxation factor is provided to guarantee the convergence of our algorithms. Moreover, it is proved that our model-free algorithm can obtain an approximate optimal solution when the transition probability matrix is unknown. Finally, a numerical example is used to illustrate our results.

本文旨在解决离散-时间马尔可夫跳跃线性系统的无模型线性二次调节器问题，而无需初始稳定控制策略。我们提出了基于模型和无模型的迭代松弛算法来学习离散-时间马尔可夫跳跃线性系统的最优控制策略。当松弛因子等于 1 时，无模型值迭代算法是我们无模型算法的特例。我们提供了松弛因子的充分条件，以保证算法的收敛性。此外，还证明了当过渡概率矩阵未知时，我们的无模型算法可以获得近似最优解。最后，我们用一个数值示例来说明我们的结果。

引用次数: 0

Asymmetrical vulnerability of heterogeneous multi-agent systems under false-data injection attacks 异构多代理系统在虚假数据注入攻击下的非对称脆弱性

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-22 DOI: 10.1016/j.automatica.2024.111947

Tian-Yu Zhang , Guang-Hong Yang , Dan Ye

This paper investigates the vulnerability of heterogeneous multi-agent systems (MASs) in face of perfect false-data injection (FDI) attacks that stealthily destabilize the synchronization processes of agents. In contrast to homogeneous dynamics, heterogeneous dynamics can be asymmetrically worsened by attackers, which is a greater challenge for MAS security. First of all, the existence conditions of perfect FDI attacks in different agents are established based on the spectral radius of heterogeneous system matrices. Then, it is proven that the attack targets against communication links are determined by the characteristic space of different agents. Since the attack properties including the attack existence and targets are different in each agent, heterogeneous MASs have the asymmetrical vulnerability under perfect FDI attacks. Finally, a sufficient condition and a necessary condition are obtained to achieve perfect FDI attacks with minimum attack targets. A numerical simulation of heterogeneous MASs is presented to demonstrate the effectiveness of perfect FDI attacks.

本文研究了异构多代理系统（MAS）在面对完美的虚假数据注入（FDI）攻击时的脆弱性，这种攻击会隐蔽地破坏代理的同步过程。与同构动力学相比，异构动力学会被攻击者非对称地恶化，这对 MAS 的安全性是一个更大的挑战。首先，基于异构系统矩阵的谱半径，建立了不同代理中完美 FDI 攻击的存在条件。然后，证明针对通信链路的攻击目标是由不同代理的特征空间决定的。由于每个代理的攻击属性（包括攻击存在性和攻击目标）不同，异构 MAS 在完美 FDI 攻击下具有非对称脆弱性。最后，得到了以最小攻击目标实现完美 FDI 攻击的充分条件和必要条件。通过对异构 MAS 进行数值模拟，证明了完美 FDI 攻击的有效性。

引用次数: 0

Analysis of barrier function based adaptive sliding mode control in the presence of deterministic noise 存在确定性噪声时基于障碍函数的自适应滑模控制分析

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111946

Luis Ovalle , Andres Gonzalez , Leonid Fridman , Salah Laghrouche , Hussein Obeid

Barrier function-based adaptive sliding mode control (BFASMC) is analyzed in presence of deterministic measurement noise. It is shown that, considering only boundedness of the measurement noise, it is impossible to select the controller parameters to track some perturbation with unknown bound. Nonetheless, under the assumption of continuity of the noise, the tracking of such a perturbation is possible; however, the barrier function width depends on the bound of the noise. If Lipschitz continuity of the noise is assumed, then it follows that the width of the barrier function can be chosen arbitrarily.

在存在确定性测量噪声的情况下，对基于势垒函数的自适应滑模控制（BFASMC）进行了分析。结果表明，如果只考虑测量噪声的有界性，就不可能选择控制器参数来跟踪某些具有未知边界的扰动。然而，在噪声连续性的假设下，跟踪这种扰动是可能的；不过，障碍函数宽度取决于噪声的边界。如果假设噪声具有 Lipschitz 连续性，那么障碍函数的宽度就可以任意选择。

引用次数: 0

Stabilization for fast sampling discrete-time singularly perturbed singular Markovian systems 快速采样离散时奇异扰动奇异马尔可夫系统的稳定问题

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111981

Yingqi Zhang , Haoqi Liang , Yuanqing Xia , Jingjing Yan

This paper considers the problems of stabilization and

H_{\infty}

control for fast sampling discrete-time singularly perturbed singular Markovian systems (SPSMSs). The system equivalent approach is initially introduced to transform the discrete fast sampling SPSMS model into the augmented SPSMS for the convenience of designing system controller. Secondly, sufficient condition on stochastically mean square admissibility is established for the fast sampling SPSMS. By separating matrix variables and singularly perturbed parameter, a state feedback controller is also provided to ensure stochastically mean square admissibility of the fast sampling augmented SPSMS. Then, the results are extended to

H_{\infty}

performance analysis and controller design in the presence of the external disturbances. The derived criteria can be converted to the feasible problems based on convex optimization, and the upper bound of singular perturbation parameter is also calculated. Besides, a discretized electrical circuit system is provided to verify the effectiveness and the superiority of the proposed approach.

本文探讨了快速采样离散时间奇异扰动奇异马尔可夫系统（SPSMS）的稳定和 H∞ 控制问题。首先引入系统等效方法，将离散快速采样奇异马尔可夫系统模型转化为增强奇异马尔可夫系统模型，以方便设计系统控制器。其次，为快速采样 SPSMS 建立了随机均方可接受性的充分条件。通过分离矩阵变量和奇异扰动参数，还提供了一种状态反馈控制器，以确保快速采样增强 SPSMS 的随机均方可接受性。然后，将结果扩展到存在外部扰动时的 H∞ 性能分析和控制器设计。得出的准则可转换为基于凸优化的可行问题，并计算了奇异扰动参数的上界。此外，还提供了一个离散电路系统来验证所提方法的有效性和优越性。

{"title":"Stabilization for fast sampling discrete-time singularly perturbed singular Markovian systems","authors":"Yingqi Zhang , Haoqi Liang , Yuanqing Xia , Jingjing Yan","doi":"10.1016/j.automatica.2024.111981","DOIUrl":"10.1016/j.automatica.2024.111981","url":null,"abstract":"<div><div>This paper considers the problems of stabilization and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> control for fast sampling discrete-time singularly perturbed singular Markovian systems (SPSMSs). The system equivalent approach is initially introduced to transform the discrete fast sampling SPSMS model into the augmented SPSMS for the convenience of designing system controller. Secondly, sufficient condition on stochastically mean square admissibility is established for the fast sampling SPSMS. By separating matrix variables and singularly perturbed parameter, a state feedback controller is also provided to ensure stochastically mean square admissibility of the fast sampling augmented SPSMS. Then, the results are extended to <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance analysis and controller design in the presence of the external disturbances. The derived criteria can be converted to the feasible problems based on convex optimization, and the upper bound of singular perturbation parameter is also calculated. Besides, a discretized electrical circuit system is provided to verify the effectiveness and the superiority of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"171 ","pages":"Article 111981"},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximate constrained stochastic optimal control via parameterized input inference 通过参数化输入推理实现近似受限随机最优控制

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-21 DOI: 10.1016/j.automatica.2024.111978

Shahbaz P. Qadri Syed, He Bai

Approximate methods to solve stochastic optimal control (SOC) problems have received significant interest from researchers in the past decade. Probabilistic inference approaches to SOC have been developed to solve nonlinear quadratic Gaussian problems. In this work, we propose an Expectation–Maximization (EM) based inference procedure to generate state-feedback controls for constrained SOC problems. We consider the inequality constraints for the state and controls and also the structural constraints for the controls. We employ barrier functions to address state and control constraints. We show that the expectation step leads to smoothing of the state-control pair while the maximization step on the non-zero subsets of the control parameters allows inference of structured stochastic optimal controllers. We demonstrate the effectiveness of the algorithm on unicycle obstacle avoidance and four-unicycle formation control examples. In these examples, we perform an empirical study on the parametric effect of barrier functions on the state constraint satisfaction. We also present a comparative study of smoothing algorithms on the performance of the proposed approach.

在过去十年中，解决随机最优控制（SOC）问题的近似方法受到了研究人员的极大关注。为解决非线性二次高斯问题，已经开发出了 SOC 的概率推理方法。在这项工作中，我们提出了一种基于期望最大化（EM）的推理程序，用于生成受约束 SOC 问题的状态反馈控制。我们考虑了状态和控制的不等式约束，以及控制的结构约束。我们采用障碍函数来解决状态和控制约束。我们证明，期望步骤会导致状态-控制对的平滑化，而控制参数非零子集上的最大化步骤允许推断结构化随机最优控制器。我们在单车避障和四单车编队控制示例中演示了该算法的有效性。在这些示例中，我们对障碍函数对状态约束满足的参数影响进行了实证研究。我们还介绍了平滑算法与所提方法性能的比较研究。

{"title":"Approximate constrained stochastic optimal control via parameterized input inference","authors":"Shahbaz P. Qadri Syed, He Bai","doi":"10.1016/j.automatica.2024.111978","DOIUrl":"10.1016/j.automatica.2024.111978","url":null,"abstract":"<div><div>Approximate methods to solve stochastic optimal control (SOC) problems have received significant interest from researchers in the past decade. Probabilistic inference approaches to SOC have been developed to solve nonlinear quadratic Gaussian problems. In this work, we propose an Expectation–Maximization (EM) based inference procedure to generate state-feedback controls for constrained SOC problems. We consider the inequality constraints for the state and controls and also the structural constraints for the controls. We employ barrier functions to address state and control constraints. We show that the expectation step leads to smoothing of the state-control pair while the maximization step on the non-zero subsets of the control parameters allows inference of structured stochastic optimal controllers. We demonstrate the effectiveness of the algorithm on unicycle obstacle avoidance and four-unicycle formation control examples. In these examples, we perform an empirical study on the parametric effect of barrier functions on the state constraint satisfaction. We also present a comparative study of smoothing algorithms on the performance of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"171 ","pages":"Article 111978"},"PeriodicalIF":4.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “Assessment of initial-state-opacity in live and bounded labeled Petri net systems via optimization techniques” [Automatica 152 (2023) 110911] 对 "通过优化技术评估实时和有界标记 Petri 网系统中的初始状态容量 "的更正[Automatica 152 (2023) 110911]

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-20 DOI: 10.1016/j.automatica.2024.111958

Francesco Basile , Gianmaria De Tommasi , Carlo Motta

引用次数: 0

Non-identifier based adaptive control of a chain of integrators and perturbations with unknown delays and parameters 具有未知延迟和参数的积分器和扰动链的非识别器自适应控制

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica

Pub Date : 2024-10-18 DOI: 10.1016/j.automatica.2024.111969

Xin Yu , Wei Lin

This paper solves the problem of how to control a chain of integrators with unknown delays in both state and input by memoryless state feedback. Inspired by the non-identifier based adaptive control scheme (Lei and Lin, 2006) as well as the recent progress in stabilizing time-delay feedforward systems with unknown parameters via dynamic state compensation (Sun and Lin, 2023), we design a memoryless universal controller (finite-dimensional) which adaptively stabilizes the time-delay integrators whose all delays are not known a priori. As a byproduct of this development, global adaptive regulation of a class of nonlinear systems with unknown delays and unknown parameters is also solved by the proposed universal control strategy.

本文解决了如何通过无记忆状态反馈控制状态和输入均为未知延迟的积分器链的问题。受基于非识别器的自适应控制方案（Lei 和 Lin，2006 年）以及最近通过动态状态补偿稳定具有未知参数的时延前馈系统的研究进展（Sun 和 Lin，2023 年）的启发，我们设计了一种无记忆通用控制器（有限维），它能自适应地稳定所有延迟都不已知的时延积分器。作为这一发展的副产品，所提出的通用控制策略还解决了一类具有未知延迟和未知参数的非线性系统的全局自适应调节问题。

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Automatica

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀