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Fu W, Mo Y, Xiao Y, Liu C, Zhou F, Wang Y, Zhou J, Zhang YJ. Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential. J Chem Theory Comput 2024; 20:4533-4544. [PMID: 38828925 DOI: 10.1021/acs.jctc.3c01181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems.
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Affiliation(s)
- Weiqiang Fu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yujie Mo
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yi Xiao
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Chang Liu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Feng Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yang Wang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Jielong Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yingsheng J Zhang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
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2
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Liu H, Liu K, Zhu H, Guo W, Li Y. Explainable machine-learning predictions for catalysts in CO 2-assisted propane oxidative dehydrogenation. RSC Adv 2024; 14:7276-7282. [PMID: 38433939 PMCID: PMC10905517 DOI: 10.1039/d4ra00406j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/17/2024] [Indexed: 03/05/2024] Open
Abstract
Propylene is an important raw material in the chemical industry that needs new routes for its production to meet the demand. The CO2-assisted oxidative dehydrogenation of propane (CO2-ODHP) represents an ideal way to produce propylene and uses the greenhouse gas CO2. The design of catalysts with high efficiency is crucial in CO2-ODHP research. Data-driven machine learning is currently of great interest and gaining popularity in the heterogeneous catalysis field for guiding catalyst development. In this study, the reaction results of CO2-ODHP reported in the literature are combined and analyzed with varied machine learning algorithms such as artificial neural network (ANN), k-nearest neighbors (KNN), support vector regression (SVR) and random forest regression (RF)and were used to predict the propylene space-time yield. Specifically, the RF method serves as a superior performing algorithm for propane conversion and propylene selectivity prediction, and SHapley Additive exPlanations (SHAP) based on the Shapley value performs fine model interpretation. Reaction conditions and chemical components show different impacts on catalytic performance. The work provides a valuable perspective for the machine learning in light alkane conversion, and helps us to design catalyst by catalytic performance hidden in the data of literatures.
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Affiliation(s)
- Hongyu Liu
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing 102249 PR China
- National Engineering Research Center for Petroleum Refining Technology and Catalyst, Research Institute of Petroleum Progressing Co., Ltd., SINOPEC Beijing 100083 China
| | - Kangyu Liu
- National Engineering Research Center for Petroleum Refining Technology and Catalyst, Research Institute of Petroleum Progressing Co., Ltd., SINOPEC Beijing 100083 China
| | - Hairuo Zhu
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing 102249 PR China
| | - Weiqing Guo
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing 102249 PR China
| | - Yuming Li
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing 102249 PR China
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3
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Cuomo A, Ibarraran S, Sreekumar S, Li H, Eun J, Menzel JP, Zhang P, Buono F, Song JJ, Crabtree RH, Batista VS, Newhouse TR. Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction. ACS CENTRAL SCIENCE 2023; 9:1768-1774. [PMID: 37780365 PMCID: PMC10540279 DOI: 10.1021/acscentsci.3c00512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 10/03/2023]
Abstract
Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with P-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures.
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Affiliation(s)
- Abbigayle
E. Cuomo
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Sebastian Ibarraran
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Sanil Sreekumar
- Chemical
Development, Boehringer Ingelheim Pharmaceuticals
Inc, 900 Ridgebury Road, Ridgefield, Connecticut 06877, United States
| | - Haote Li
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Jungmin Eun
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Jan Paul Menzel
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Pengpeng Zhang
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Frederic Buono
- Chemical
Development, Boehringer Ingelheim Pharmaceuticals
Inc, 900 Ridgebury Road, Ridgefield, Connecticut 06877, United States
| | - Jinhua J. Song
- Chemical
Development, Boehringer Ingelheim Pharmaceuticals
Inc, 900 Ridgebury Road, Ridgefield, Connecticut 06877, United States
| | - Robert H. Crabtree
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Victor S. Batista
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
| | - Timothy R. Newhouse
- Department
of Chemistry, Yale University, New Haven, Connecticut 06511, United States
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4
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Chen BWJ, Zhang X, Zhang J. Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials. Chem Sci 2023; 14:8338-8354. [PMID: 37564405 PMCID: PMC10411631 DOI: 10.1039/d3sc02482b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/11/2023] [Indexed: 08/12/2023] Open
Abstract
Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.
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Affiliation(s)
- Benjamin W J Chen
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Xinglong Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
| | - Jia Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) 1 Fusionopolis Way, #16-16 Connexis Singapore 138632 Singapore
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5
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Wang X, Zhang Y, Zhang C, Wei H, Jin H, Mu Z, Chen X, Chen X, Wang P, Guo X, Ding F, Liu X, Ma L. Artificial intelligence-aided preparation of perovskite SrFe xZr 1-xO 3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment. CHEMOSPHERE 2023; 318:137825. [PMID: 36681194 DOI: 10.1016/j.chemosphere.2023.137825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/20/2022] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
Membrane technology has been widely used to treat wastewater from a variety of industries, but it also results in a large amount of concentrated wastewater containing organic pollutants after membrane treatment, which is challenging to decompose. Here in this work, a series of perovskite SrFexZr1-xO3-δ catalysts were prepared via a modified co-precipitation method and evaluated for catalytic ozone oxidative degradation of m-cresol. An artificial neural intelligence networks (ANN) model was employed to train the experimental data to optimize the preparation parameters of catalysts, with SrFe0.13Zr0.87O3-δ being the optimal catalysts. The resultant catalysts before and after reduction were then thoroughly characterized and tested for m-cresol degradation. It was found that the co-doping of Fe and Zr at the B-site and the improvement of oxygen vacancies and oxygen active species by reduction dramatically increased TOC removal rates up to 5 times compared with ozone alone, with the conversion rate of m-cresol reaching 100%. We also proposed a possible mechanism for m-cresol degradation via investigating the intermediates using GC-MS, and confirmed the good versatility of the reduced SrFe0.13Zr0.87O3-δ catalyst to remove other common organic pollutants in concentrated wastewater. This work demonstrates new prospects for the use of perovskite materials in wastewater treatment.
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Affiliation(s)
- Xu Wang
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Yanan Zhang
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Cheng Zhang
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Huangzhao Wei
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, PR China
| | - Haibo Jin
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Zhao Mu
- Institute of Applied Chemical Technology for Oilfield/ College of New Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Xiaofei Chen
- Chen Ping Laboratory of TIANS Engineering Technology Group Co. Ltd., Shijiazhuang 050000, Hebei, PR China
| | - Xinru Chen
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Ping Wang
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Xiaoyan Guo
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China
| | - Fuchen Ding
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China.
| | - Xiaowei Liu
- Advanced Membranes and Porous Materials Center, Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
| | - Lei Ma
- Beijing Key Laboratory of Fuels Cleaning and Advanced Catalytic Emission Reduction Technology/College of Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, Beijing, PR China.
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6
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Uralde J, Artetxe E, Barambones O, Calvo I, Fernández-Bustamante P, Martin I. Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:1690. [PMID: 36772730 PMCID: PMC9921284 DOI: 10.3390/s23031690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/23/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.
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Affiliation(s)
- Jokin Uralde
- Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
| | - Eneko Artetxe
- Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
| | - Oscar Barambones
- Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
| | - Isidro Calvo
- Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
| | - Pablo Fernández-Bustamante
- Department of Electrical Engineering, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
| | - Imanol Martin
- Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain
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7
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Li M, Zhao L, Jin S, Li D, Huang J, Liu J. Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization. Heliyon 2022; 8:e12301. [PMID: 36578395 PMCID: PMC9791839 DOI: 10.1016/j.heliyon.2022.e12301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 09/06/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale.
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Affiliation(s)
- Minghan Li
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Lingling Zhao
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Shuo Jin
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Danlu Li
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Jingyi Huang
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Jiaxin Liu
- China Nuclear Power Engineering Co., Ltd., 117 North West Third Ring Road, Beijing 100840, PR China
- School of Chemical Engineering, Dalian University of Technology, 2 Linggong Road, Dalian, Liaoning 116024, PR China
- Corresponding author at: China Nuclear Power Engineering Co., Ltd., 117 North West Third Ring Road, Beijing 100840, PR China.
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8
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Shirkoohi MG, Tyagi RD, Vanrolleghem PA, Drogui P. Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review. JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE & ENGINEERING 2022; 20:1089-1109. [PMID: 36406623 PMCID: PMC9672199 DOI: 10.1007/s40201-022-00835-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes. Supplementary Information The online version contains supplementary material available at 10.1007/s40201-022-00835-w.
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Affiliation(s)
- Majid Gholami Shirkoohi
- Institut National de La Recherche Scientifique (INRS), Centre-Eau Terre Environnement, Université du Québec, 490 Rue de la Couronne, Québec, (QC) G1K 9A9 Canada
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
| | | | - Peter A. Vanrolleghem
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
- modelEAU, Département de Génie Civil Et de Génie Des Eaux, Université Laval, 1065 av. de la Médecine, Québec, (QC) G1V 0A6 Canada
| | - Patrick Drogui
- Institut National de La Recherche Scientifique (INRS), Centre-Eau Terre Environnement, Université du Québec, 490 Rue de la Couronne, Québec, (QC) G1K 9A9 Canada
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
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Bakhtyari A, Bardool R, Reza Rahimpour M, Mofarahi M, Lee CH. Performance Analysis and Artificial Intelligence Modeling for Enhanced Hydrogen Production by Catalytic Bio-alcohol Reforming in a Membrane-Assisted Reactor. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.118432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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10
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Psenakova Z, Smondrk M, Barabas J, Benova M, Brociek R, Wajda A, Kowol P, Coco S, Sciuto GL. Computational Analysis of a Multi-Layered Skin and Cardiac Pacemaker Model Based on Neural Network Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:6359. [PMID: 36080817 PMCID: PMC9459797 DOI: 10.3390/s22176359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/17/2022] [Accepted: 08/21/2022] [Indexed: 06/15/2023]
Abstract
The presented study discusses the possible disturbing effects of the electromagnetic field of antennas used in mobile phones or WiFi technologies on the pacemaker in the patient's body. This study aims to obtain information on how the thickness of skin layers (such as the thickness of the hypodermis) can affect the activity of a pacemaker exposed to a high-frequency electromagnetic field. This study describes the computational mathematical analysis and modeling of the heart pacemaker inserted under the skin exposed to various electromagnetic field sources, such as a PIFA antenna and a tuned dipole antenna. The finite integration technique (FIT) for a pacemaker model was implemented within the commercially available CST Microwave simulation software studio. Likewise, the equations that describe the mathematical relationship between the subcutaneous layer thickness and electric field according to different exposures of a tuned dipole and a PIFA antenna are used and applied for training a neural network. The main output of this study is the creation of a mathematical model and a multilayer feedforward neural network, which can show the dependence of the thickness of the hypodermis on the size of the electromagnetic field, from the simulated data from CST Studio.
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Affiliation(s)
- Zuzana Psenakova
- Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering, University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia
| | - Maros Smondrk
- Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering, University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia
| | - Jan Barabas
- Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering, University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia
| | - Mariana Benova
- Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering, University of Zilina, Univerzitna 1, 01026 Zilina, Slovakia
| | - Rafał Brociek
- Department of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Agata Wajda
- Institute of Energy and Fuel Processing Technology, 41-803 Zabrze, Poland
| | - Paweł Kowol
- Department of Mechatronics, Silesian University of Technology, Akademicka 10a, 44-100 Gliwice, Poland
| | - Salvatore Coco
- Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
| | - Grazia Lo Sciuto
- Department of Mechatronics, Silesian University of Technology, Akademicka 10a, 44-100 Gliwice, Poland
- Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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11
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Sulphur Oxidative Coupling of Methane process development and its modelling via Machine Learning. AIChE J 2022. [DOI: 10.1002/aic.17793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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12
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100983. [PMID: 35664686 PMCID: PMC9148440 DOI: 10.1016/j.imu.2022.100983] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.
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13
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Yang Z, Gao W. Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2106043. [PMID: 35229986 PMCID: PMC9036033 DOI: 10.1002/advs.202106043] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/02/2022] [Indexed: 05/28/2023]
Abstract
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.
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Affiliation(s)
- Ze Yang
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
| | - Wang Gao
- School of Materials Science and EngineeringJilin UniversityChangchun130022P. R. China
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14
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Santos LFL, Ferreira BX, Brandão ALT, Santos BF. Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Lucas F. L. Santos
- Department of Chemical and Materials Engineering (DEQM) Pontifical Catholic University of Rio de Janeiro (PUC‐Rio) , Rua Marquês de São Vicente, 225 – Gávea Rio de Janeiro RJ Brazil
| | - Bruno X. Ferreira
- Department of Chemical and Materials Engineering (DEQM) Pontifical Catholic University of Rio de Janeiro (PUC‐Rio) , Rua Marquês de São Vicente, 225 – Gávea Rio de Janeiro RJ Brazil
| | - Amanda L. T. Brandão
- Department of Chemical and Materials Engineering (DEQM) Pontifical Catholic University of Rio de Janeiro (PUC‐Rio) , Rua Marquês de São Vicente, 225 – Gávea Rio de Janeiro RJ Brazil
| | - Brunno F. Santos
- Department of Chemical and Materials Engineering (DEQM) Pontifical Catholic University of Rio de Janeiro (PUC‐Rio) , Rua Marquês de São Vicente, 225 – Gávea Rio de Janeiro RJ Brazil
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15
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Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches. Catalysts 2022. [DOI: 10.3390/catal12030347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of aumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with theumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, andess on the experimental conditions evaluated during model development.
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16
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Dashti A, Bazdar M, Akbari Fakhrabadi E, Mohammadi AH. Modeling of Catalytic CO
2
Methanation Using Smart Computational Schemes. Chem Eng Technol 2021. [DOI: 10.1002/ceat.202100557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Amir Dashti
- University of Kashan Department of Chemical Engineering, Faculty of Engineering 87317-53153 Kashan Iran
| | - Mahsa Bazdar
- University of Kashan Department of Chemical Engineering, Faculty of Engineering 87317-53153 Kashan Iran
| | | | - Amir H. Mohammadi
- Institut de Recherche en Génie Chimique et Pétrolier (IRGCP) Paris Cedex France
- University of KwaZulu-Natal, Howard College Campus Discipline of Chemical Engineering, School of Engineering King George V Avenue 4041 Durban South Africa
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17
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Application of computational approach in plastic pyrolysis kinetic modelling: a review. REACTION KINETICS MECHANISMS AND CATALYSIS 2021. [DOI: 10.1007/s11144-021-02093-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AbstractDuring the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.
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18
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Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.
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Abidi A, Jokar Z, Allahyari S, Kolahi Sadigh F, Mohammad Sajadi S, Firouzi P, Baleanu D, Ghaemi F, Karimipour A. Improve thermal performance of Simulated-Body-Fluid as a solution with an ion concentration close to human blood plasma, by additive Zinc Oxide and its composites: ZnO/Carbon Nanotube and ZnO/Hydroxyapatite. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117457] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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20
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Zhang N, Yang B, Liu K, Li H, Chen G, Qiu X, Li W, Hu J, Fu J, Jiang Y, Liu M, Ye J. Machine Learning in Screening High Performance Electrocatalysts for CO 2 Reduction. SMALL METHODS 2021; 5:e2100987. [PMID: 34927959 DOI: 10.1002/smtd.202100987] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/18/2021] [Indexed: 06/14/2023]
Abstract
Converting CO2 into carbon-based fuels is promising for relieving the greenhouse gas effect and the energy crisis. However, the selectivity and efficiency of current electrocatalysts for CO2 reductions are still not satisfactory. In this paper, the development of machine learning methods in screening CO2 reduction electrocatalysts over the recent years is reviewed. Through high-throughput calculation of some key descriptors such as adsorption energies, d-band center, and coordination number by well-constructed machine learning models, the catalytic activity, optimal composition, active sites, and CO2 reduction reaction pathway over various possible materials can be predicted and understood. Machine learning is now realized as a fast and low-cost method to effectively explore high performance electrocatalysts for CO2 reduction.
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Affiliation(s)
- Ning Zhang
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Baopeng Yang
- School of Physical Science and Electronics, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Kang Liu
- School of Physical Science and Electronics, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Hongmei Li
- School of Physical Science and Electronics, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Gen Chen
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Xiaoqing Qiu
- College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Wenzhang Li
- College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Junhua Hu
- School of Materials Science and Engineering, Zhengzhou University, Zhengzhou, 450002, P. R. China
| | - Junwei Fu
- School of Physical Science and Electronics, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Yong Jiang
- School of Materials Science and Engineering, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Min Liu
- School of Physical Science and Electronics, Central South University, Changsha, Hunan, 410083, P. R. China
| | - Jinhua Ye
- National Institute for Materials Science (NIMS), International Center for Materials Nanoarchitectonics (WPI-MANA), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
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21
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Chanussot L, Das A, Goyal S, Lavril T, Shuaibi M, Riviere M, Tran K, Heras-Domingo J, Ho C, Hu W, Palizhati A, Sriram A, Wood B, Yoon J, Parikh D, Zitnick CL, Ulissi Z. Open Catalyst 2020 (OC20) Dataset and Community Challenges. ACS Catal 2021. [DOI: 10.1021/acscatal.0c04525] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Lowik Chanussot
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Abhishek Das
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Siddharth Goyal
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Thibaut Lavril
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Muhammed Shuaibi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Morgane Riviere
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Kevin Tran
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Javier Heras-Domingo
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Caleb Ho
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Weihua Hu
- Computer Science Department, Stanford University, Stanford, California 94305, United States
| | - Aini Palizhati
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Anuroop Sriram
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Brandon Wood
- National Energy Research Scientific Computing Center (NERSC), Berkeley, California 94720, United States
| | - Junwoong Yoon
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Devi Parikh
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
- School of Interactive Computing, Georgia Tech, Atlanta, Georgia 30332, United States
| | - C. Lawrence Zitnick
- Facebook AI Research (FAIR), 1 Hacker Way, Menlo Park, California 94025, United States
| | - Zachary Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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22
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Ortega C, Otyuskaya D, Ras E, Virla LD, Patience GS, Dathe H. Experimental methods in chemical engineering: High throughput catalyst testing —
HTCT. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24089] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Carlos Ortega
- Catalysis Services, Avantium Amsterdam The Netherlands
| | | | - Erik‐Jan Ras
- Catalysis Services, Avantium Amsterdam The Netherlands
| | - Luis D. Virla
- Chemical & Petroleum Engineering University of Calgary Calgary Alberta Canada
| | | | - Hendrik Dathe
- Catalysis Services, Avantium Amsterdam The Netherlands
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23
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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Abstract
Mechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning and dewatering of sewage sludge is the task of laboratory work. This work can be accelerated through the use of artificial neural network (ANNs). The paper discusses the possibilities of using ANNs in predicting the dewatering efficiency of physically conditioned sludge. The input variables were only four parameters characterizing the conditioning methods and the dewatering method by centrifugation. These were the dose of the sludge skeleton builders (cement, gypsum, fly ash, and liquid glass), sonication parameters (sonication amplitude and time), and relative centrifugal force. Dewatering efficiency parameters such as sludge hydration and separation factor were output variables. Due to the nature of the research problem, two nonlinear networks were selected: a multilayer perceptron and a radial neural network. Based on the results of the prediction of artificial neural networks, it was found that these networks can be used to forecast the effectiveness of municipal sludge dewatering. The prediction error did not exceed 1.0% of the real value. ANN can therefore be useful in optimizing the dewatering process. In the case of the conducted research, it was the optimization of the sludge dewatering efficiency as a function of the type and parameters of conditioning factors. Therefore, it is possible to predict the dewatering efficiency of sludge that has not been tested in the laboratory, for example, with the use of other doses of physical conditioner. However, the condition for correct prediction and optimization was the use of a large dataset in the neural network training process.
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25
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Zheng S, Hu X. Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning. Front Psychol 2021; 12:594031. [PMID: 33658958 PMCID: PMC7917260 DOI: 10.3389/fpsyg.2021.594031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/18/2021] [Indexed: 11/30/2022] Open
Abstract
The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities.
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Affiliation(s)
- Shuang Zheng
- College of Media and International Culture, Zhejiang University, Hangzhou, China.,School of Media and Law, NingboTech University, Ningbo, China
| | - Xiaomei Hu
- School of Media and Law, NingboTech University, Ningbo, China
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26
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27
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High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks. MATHEMATICS 2021. [DOI: 10.3390/math9030244] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.
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28
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Park J, Forman BA, Lievens H. Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021; 14:2403-2417. [PMID: 35154559 PMCID: PMC8833106 DOI: 10.1109/jstars.2021.3053945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions.
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Affiliation(s)
- Jongmin Park
- Universities Space Research Association, Columbia, MD 21046 USA, and also with the NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA
| | - Barton A Forman
- Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742 USA
| | - Hans Lievens
- Department of Earth and Environmental Sciences, KU Leuven, 3000 Leuven, Belgium, and also with the Department of Environment, Ghent University, 9000 Ghent, Belgium
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29
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Assessment of TiO2 band gap from structural parameters using artificial neural networks. J Photochem Photobiol A Chem 2021. [DOI: 10.1016/j.jphotochem.2020.112870] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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30
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Khadem SA, Rey AD. Nucleation and growth of cholesteric collagen tactoids: A time-series statistical analysis based on integration of direct numerical simulation (DNS) and long short-term memory recurrent neural network (LSTM-RNN). J Colloid Interface Sci 2021; 582:859-873. [DOI: 10.1016/j.jcis.2020.08.052] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/13/2020] [Accepted: 08/13/2020] [Indexed: 12/12/2022]
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31
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Increasing the Efficiency of Optimized V-SBA-15 Catalysts in the Selective Oxidation of Methane to Formaldehyde by Artificial Neural Network Modelling. Catalysts 2020. [DOI: 10.3390/catal10121411] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The present study investigates the possibility of improving the selective oxidation of methane to formaldehyde over V-SBA-15 catalysts in two different ways. In a classical approach of catalyst optimization, the in situ synthesis of V-SBA-15 catalysts was optimized with regard to the applied pH value. Among the set of catalysts synthesized, a higher amount of incorporated vanadium, a higher content of polymeric VOx species as well as a less ordered structure of the support material were observed by increasing the pH values from 2.0 to 3.0. An optimum in performance during the selective oxidation of methane to formaldehyde with respect to activity and selectivity was found over V-SBA-15 prepared at a pH value of 2.5. With this knowledge, we have now evaluated the possibilities of reaction control using this catalyst. Specifically, artificial neural network modelling was applied after the collection of 232 training samples for obtaining insight into the influence of different reaction parameters (temperature; gas hourly space velocity (GHSV); and concentration of O2, N2 and H2O) onto methane conversion and selectivity towards formaldehyde. This optimization of reaction conditions resulted in an outstanding high space-time yield of 13.6 kgCH2O∙kgcat∙h−1.
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32
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Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon. Top Catal 2020. [DOI: 10.1007/s11244-020-01401-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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33
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Bondarev NV. Exploratory, Regression, and Neural Network Analysis of the
Stability of Cation Coronates in Selected Pure Solvents. RUSS J GEN CHEM+ 2020. [DOI: 10.1134/s107036322010014x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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34
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Brockkötter J, Cielanga M, Weber B, Jupke A. Prediction and Characterization of Flooding in Pulsed Sieve Plate Extraction Columns Using Data-Driven Models. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03282] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Michael Cielanga
- AVT - Fluid Process Engineering, RWTH Aachen University, Aachen 52056, Germany
| | - Benedikt Weber
- AVT - Fluid Process Engineering, RWTH Aachen University, Aachen 52056, Germany
| | - Andreas Jupke
- AVT - Fluid Process Engineering, RWTH Aachen University, Aachen 52056, Germany
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35
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Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network. MATERIALS 2020; 13:ma13194266. [PMID: 32992676 PMCID: PMC7579244 DOI: 10.3390/ma13194266] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/11/2020] [Accepted: 09/21/2020] [Indexed: 11/17/2022]
Abstract
Due to the non-linear characteristics of the processing parameters, predicting the desired properties of nanocomposites using the conventional regression approach is often unsatisfactory. Thus, it is essential to use a machine learning approach to determine the optimum processing parameters. In this study, a backpropagation deep neural network (DNN) with nanoclay and compatibilizer content, and processing parameters as input, was developed to predict the mechanical properties, including tensile modulus and tensile strength, of clay-reinforced polyethylene nanocomposites. The high accuracy of the developed model proves that DNN can be used as an efficient tool for predicting mechanical properties of the nanocomposites in terms of four independent parameters.
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36
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Zulfiqar M, Chowdhury S, Omar AA, Siyal AA, Sufian S. Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO 2-P25: optimization and modeling approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:34018-34036. [PMID: 32557068 DOI: 10.1007/s11356-020-09674-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 06/09/2020] [Indexed: 06/11/2023]
Abstract
The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO2-P25 nanoparticles (TNPs). Dose of TNPs, pH, and AO7 concentration were selected as investigated parameters. RSM results reveal the reflective rate of AO7 removal of ~ 94.974% was obtained at pH 7.599, TNP dose of 0.748 g/L, and AO7 concentration of 28.483 mg/L. The resulting quadratic model is satisfactory with the highest coefficient of determination (R2) between the predicted and experimental data (R2 = 0.98 and adjusted R2 = 0.954). On the other hand, ANNs were successfully employed for modeling of AO7 degradation process. The proposed ANN model was absolutely fitted with experimental results producing the highest R2. Furthermore, root mean square error (RMSE), mean average deviation (MAD), absolute average relative error (AARE), and mean square error (MSE) were examined more to compare the predictive capabilities of ANN and RSM models. The experimental data was well fitted into pseudo-first-order and pseudo-second-order kinetics with more accuracy. Thermodynamic parameters, namely enthalpy, entropy, Gibbs' free energy, and activation energy, were also evaluated to suggest the nature of the degradation process. The increase of temperature was analyzed to be more suitable for the fast removal of AO7 over TNPs. Graphical abstract.
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Affiliation(s)
- Muhammad Zulfiqar
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Sri Iskandar, Perak, Malaysia.
| | - Sujan Chowdhury
- Chemical Engineering Department, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
| | - Abdul Aziz Omar
- Department of Computing and Information Systems, Sunway University, 47500, Petaling Jaya, Selangor, Malaysia
| | - Ahmer Ali Siyal
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Sri Iskandar, Perak, Malaysia
| | - Suriati Sufian
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Sri Iskandar, Perak, Malaysia.
- Centre of Innovative Nanostructures & Nanodevices (COINN), Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia.
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37
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O’Carroll D, English NJ. Self-ordering water molecules at TiO2 interfaces: Advances in structural classification. J Chem Phys 2020; 153:064502. [DOI: 10.1063/5.0011510] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Dáire O’Carroll
- School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Niall J. English
- School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland
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38
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Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis. J Med Syst 2020; 44:162. [PMID: 32767134 DOI: 10.1007/s10916-020-01626-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 07/21/2020] [Indexed: 12/21/2022]
Abstract
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction models available in the literature. A systematic review of research papers published from 2010 to the present, related to the prediction of temporary disability and absenteeism in available in different research databases, is presented in this paper. The review focuses primarily on scientific databases such as Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate. A total of 58 articles were obtained from which, after removing duplicates and applying the search criteria, 18 have been included in the review. In total, 44% of the articles were published in 2019, representing a significant growth in scientific work regarding these indicators. This study also evidenced the interest of several countries. In addition, 56% of the articles were found to base their study on regression methods, 33% in classification, and 11% in grouping. After this systematic review, the efficiency and usefulness of artificial neural networks in predicting absenteeism and temporary incapacity are demonstrated. The studies regarding absenteeism and temporary disability at work are mainly conducted in Brazil and India, which are responsible for 44% of the analyzed papers followed by Saudi Arabia, and Australia which represented 22%. ANNs are the most used method in both classification and regression models representing 83% and 80% of the analyzed works, respectively. Only 10% of the literature use SVM, which is the less used method in regression models. Moreover, Naïve Bayes is the less used method in classification models representing 17%.
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Erdem Günay M, Yıldırım R. Recent advances in knowledge discovery for heterogeneous catalysis using machine learning. CATALYSIS REVIEWS-SCIENCE AND ENGINEERING 2020. [DOI: 10.1080/01614940.2020.1770402] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- M. Erdem Günay
- Department of Energy Systems Engineering, Istanbul Bilgi University, Istanbul, Turkey
| | - Ramazan Yıldırım
- Department of Chemical Engineering, Boğaziçi University, Istanbul, Turkey
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40
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O’Carroll D, Martinez-Gonzalez JA, English NJ. Coherency spectral analysis of interfacial water at TiO2 surfaces. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1764951] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Dáire O’Carroll
- School of Chemical & Bioprocess Engineering, University College Dublin, Dublin, Ireland
| | | | - Niall J. English
- School of Chemical & Bioprocess Engineering, University College Dublin, Dublin, Ireland
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41
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Friederich P, Dos Passos Gomes G, De Bin R, Aspuru-Guzik A, Balcells D. Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex. Chem Sci 2020; 11:4584-4601. [PMID: 33224459 PMCID: PMC7659707 DOI: 10.1039/d0sc00445f] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/06/2020] [Indexed: 12/15/2022] Open
Abstract
A machine learning exploration of the chemical space surrounding Vaska's complex.
Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol–1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol–1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.
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Affiliation(s)
- Pascal Friederich
- Chemical Physics Theory Group , Department of Chemistry , University of Toronto , Toronto , Ontario M5S 3H6 , Canada.,Institute of Nanotechnology , Karlsruhe Institute of Technology , Hermann-von-Helmholtz-Platz 1 , 76344 Eggenstein-Leopoldshafen , Germany.,Department of Computer Science , University of Toronto , 214 College St. , Toronto , Ontario M5T 3A1 , Canada
| | - Gabriel Dos Passos Gomes
- Chemical Physics Theory Group , Department of Chemistry , University of Toronto , Toronto , Ontario M5S 3H6 , Canada.,Department of Computer Science , University of Toronto , 214 College St. , Toronto , Ontario M5T 3A1 , Canada
| | - Riccardo De Bin
- Department of Mathematics , University of Oslo , P. O. Box 1053, Blindern , N-0316 , Oslo , Norway
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group , Department of Chemistry , University of Toronto , Toronto , Ontario M5S 3H6 , Canada.,Department of Computer Science , University of Toronto , 214 College St. , Toronto , Ontario M5T 3A1 , Canada.,Vector Institute for Artificial Intelligence , 661 University Ave. Suite 710 , Toronto , Ontario M5G 1M1 , Canada.,Lebovic Fellow , Canadian Institute for Advanced Research (CIFAR) , 661 University Ave , Toronto , ON M5G 1M1 , Canada
| | - David Balcells
- Hylleraas Centre for Quantum Molecular Sciences , Department of Chemistry , University of Oslo , P. O. Box 1033, Blindern , N-0315 , Oslo , Norway .
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Boucheikhchoukh A, Thibault J, Fauteux‐Lefebvre C. Catalyst design using artificial intelligence:
SO
2
to
SO
3
case study. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23756] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Ariel Boucheikhchoukh
- Department of Chemical and Biological EngineeringUniversity of Ottawa Ottawa Ontario Canada
| | - Jules Thibault
- Department of Chemical and Biological EngineeringUniversity of Ottawa Ottawa Ontario Canada
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Abstract
The world needs new materials to stimulate the chemical industry in key sectors of our economy: environment and sustainability, information storage, optical telecommunications, and catalysis. Yet, nearly all functional materials are still discovered by "trial-and-error", of which the lack of predictability affords a major materials bottleneck to technological innovation. The average "molecule-to-market" lead time for materials discovery is currently 20 years. This is far too long for industrial needs, as highlighted by the Materials Genome Initiative, which has ambitious targets of up to 4-fold reductions in average molecule-to-market lead times. Such a large step change in progress can only be realistically achieved if one adopts an entirely new approach to materials discovery. Fortunately, a fundamentally new approach to materials discovery has been emerging, whereby data science with artificial intelligence offers a prospective solution to speed up these average molecule-to-market lead times.This approach is known as data-driven materials discovery. Its broad prospects have only recently become a reality, given the timely and major advances in "big data", artificial intelligence, and high-performance computing (HPC). Access to massive data sets has been stimulated by government-regulated open-access requirements for data and literature. Natural-language processing (NLP) and machine-learning (ML) tools that can mine data and find patterns therein are becoming mainstream. Exascale HPC capabilities that can aid data mining and pattern recognition and also generate their own data from calculations are now within our grasp. These timely advances present an ideal opportunity to develop data-driven materials-discovery strategies to systematically design and predict new chemicals for a given device application.This Account shows how data science can afford materials discovery via a four-step "design-to-device" pipeline that entails (1) data extraction, (2) data enrichment, (3) material prediction, and (4) experimental validation. Massive databases of cognate chemical and property information are first forged from "chemistry-aware" natural-language-processing tools, such as ChemDataExtractor, and enriched using machine-learning methods and high-throughput quantum-chemical calculations. New materials for a bespoke application can then be predicted by mining these databases with algorithmic encodings of relationships between chemical structures and physical properties that are known to deliver functional materials. These may take the form of classification, enumeration, or machine-learning algorithms. A data-mining workflow short-lists these predictions to a handful of lead candidate materials that go forward to experimental validation. This design-to-device approach is being developed to offer a roadmap for the accelerated discovery of new chemicals for functional applications. Case studies presented demonstrate its utility for photovoltaic, optical, and catalytic applications. While this Account is focused on applications in the physical sciences, the generic pipeline discussed is readily transferable to other scientific disciplines such as biology and medicine.
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Affiliation(s)
- Jacqueline M. Cole
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K
- ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
- Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, U.K
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44
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Agarwal S, Mehta S, Joshi K. Understanding the ML black box with simple descriptors to predict cluster–adsorbate interaction energy. NEW J CHEM 2020. [DOI: 10.1039/d0nj00633e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Density functional theory (DFT) is currently one of the most accurate and yet practical theories used to gain insight into the properties of materials.
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Affiliation(s)
- Sheena Agarwal
- Physical and Materials Chemistry Division
- CSIR-National Chemical Laboratory
- Pune-411008
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Shweta Mehta
- Physical and Materials Chemistry Division
- CSIR-National Chemical Laboratory
- Pune-411008
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Kavita Joshi
- Physical and Materials Chemistry Division
- CSIR-National Chemical Laboratory
- Pune-411008
- India
- Academy of Scientific and Innovative Research (AcSIR)
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45
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Shevchenko AP, Eremin RA, Blatov VA. The CSD and knowledge databases: from answers to questions. CrystEngComm 2020. [DOI: 10.1039/d0ce00265h] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
We develop tools for extracting new information on crystal structures from crystallographic databases and show how to use these tools in the design of coordination compounds.
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Affiliation(s)
- Alexander P. Shevchenko
- Samara Center for Theoretical Materials Science (SCTMS)
- Samara University
- 443011 Samara
- Russian Federation
- Samara Center for Theoretical Materials Science (SCTMS)
| | - Roman A. Eremin
- Samara Center for Theoretical Materials Science (SCTMS)
- Samara University
- 443011 Samara
- Russian Federation
- Samara Center for Theoretical Materials Science (SCTMS)
| | - Vladislav A. Blatov
- Samara Center for Theoretical Materials Science (SCTMS)
- Samara University
- 443011 Samara
- Russian Federation
- Samara Center for Theoretical Materials Science (SCTMS)
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46
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Cova TFGG, Pais AACC. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns. Front Chem 2019; 7:809. [PMID: 32039134 PMCID: PMC6988795 DOI: 10.3389/fchem.2019.00809] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/11/2019] [Indexed: 12/14/2022] Open
Abstract
Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.
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Affiliation(s)
- Tânia F. G. G. Cova
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Alberto A. C. C. Pais
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
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Talwar S, Verma AK, Sangal VK. Modeling and optimization of fixed mode dual effect (photocatalysis and photo-Fenton) assisted Metronidazole degradation using ANN coupled with genetic algorithm. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 250:109428. [PMID: 31545174 DOI: 10.1016/j.jenvman.2019.109428] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 07/01/2019] [Accepted: 08/17/2019] [Indexed: 06/10/2023]
Abstract
The study presents the novel concept of application of fixed bed-batch mode in-situ dual effect of degradation of photo-Fenton and photocatalysis using composite material composed of fuller's earth and foundry sand for the removal of antibiotic Metronidazole. The composite material was involved in the subsequent leaching of iron prompting to in-situ photo-Fenton reactions while TiO2 layer immobilized upon the support involved in photocatalysis. Dual process facilitated the significant reduction in treatment time as 80% of the compound degraded with 30 min of the reaction. Both the processes almost took 180 min for 50% degradation, when applied individually. No doubt, there was an astonishing increase in the rate constant. An artificial neural network model coupled with the genetic algorithm was employed for the optimization of various parameters like H2O2 dose, treatment time, number of beads, pH, etc. Various characterizations have been performed to confirm the novelty of the process. Extended recyclability up to 70 recycles of these composite beads really confirmed the feasibility of this technology for field-scale applications.
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Affiliation(s)
- Steffi Talwar
- Department of Chemical Engineering, Thapar Institute of Engineering and Technology, Patiala, India
| | - Anoop Kumar Verma
- School of Energy and Environment, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.
| | - Vikas Kumar Sangal
- Department of Chemical Engineering, Malaviya National Institute of Technology, Jaipur, India
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Haleem A, Javaid M, Khan IH. Current status and applications of Artificial Intelligence (AI) in medical field: An overview. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.cmrp.2019.11.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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49
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Zulfiqar M, Samsudin MFR, Sufian S. Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network. J Photochem Photobiol A Chem 2019. [DOI: 10.1016/j.jphotochem.2019.112039] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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50
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Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming. Catalysts 2019. [DOI: 10.3390/catal9090738] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven–Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H2 production were strongly correlated with the observed values.
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