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Shen SC, Khare E, Lee NA, Saad MK, Kaplan DL, Buehler MJ. Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics. Chem Rev 2023; 123:2242-2275. [PMID: 36603542 DOI: 10.1021/acs.chemrev.2c00479] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.
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Affiliation(s)
- Sabrina C Shen
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Eesha Khare
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nicolas A Lee
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,School of Architecture and Planning, Media Lab, Massachusetts Institute of Technology, 75 Amherst Street, Cambridge, Massachusetts 02139, United States
| | - Michael K Saad
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Center for Computational Science and Engineering, Schwarzman College of Computing, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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2
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Guo J, Sun M, Zhao X, Shi C, Su H, Guo Y, Pu X. General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation. J Chem Inf Model 2023; 63:1143-1156. [PMID: 36734616 DOI: 10.1021/acs.jcim.2c01538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.
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Affiliation(s)
- Jiali Guo
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Ming Sun
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Xueyan Zhao
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang621900, China
| | - Chaojie Shi
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Haoming Su
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China
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Alim MA, Repon MR, Islam T, Mishfa KF, Jalil MA, Aljabri MD, Rahman MM. Mapping the Progress in Natural Dye‐Sensitized Solar Cells: Materials, Parameters and Durability. ChemistrySelect 2022. [DOI: 10.1002/slct.202201557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Md. Abdul Alim
- Department of Textile Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh
| | - Md. Reazuddin Repon
- ZR Research Institute for Advanced Materials Sherpur 2100 Bangladesh
- Department of Production Engineering Faculty of Mechanical Engineering and Design Kaunas University of Technology Studentų 56 LT-51424 Kaunas Lithuania
| | - Tarikul Islam
- ZR Research Institute for Advanced Materials Sherpur 2100 Bangladesh
- Department of Textile Engineering Jashore University of Science and Technology Jashore 7408 Bangladesh
| | - Kaniz Fatima Mishfa
- Department of Textile Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh
| | - Mohammad Abdul Jalil
- Department of Textile Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh
| | - Mahmood D. Aljabri
- Department of Chemistry University College in Al-Jamoum Umm Al-Qura University Makkah 21955 Saudi Arabia
| | - Mohammed M. Rahman
- Center of Excellence for Advanced Materials Research (CEAMR) & Department of Chemistry Faculty of Science King Abdulaziz University Jeddah 21589 Saudi Arabia
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Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications. Molecules 2022; 27:molecules27113574. [PMID: 35684512 PMCID: PMC9182285 DOI: 10.3390/molecules27113574] [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: 04/07/2022] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 12/04/2022] Open
Abstract
Anion Exchange Membrane (AEM) fuel cells have attracted growing interest, due to their encouraging advantages, including high power density and relatively low cost. AEM is a polymer matrix, which conducts hydroxide (OH−) ions, prevents physical contact of electrodes, and has positively charged head groups (mainly quaternary ammonium (QA) groups), covalently bound to the polymer backbone. The chemical instability of the quaternary ammonium (QA)-based head groups, at alkaline pH and elevated temperature, is a significant threshold in AEMFC technology. This review work aims to introduce recent studies on the chemical stability of various QA-based head groups and transportation of OH− ions in AEMFC, via modeling and simulation techniques, at different scales. It starts by introducing the fundamental theories behind AEM-based fuel-cell technology. In the main body of this review, we present selected computational studies that deal with the effects of various parameters on AEMs, via a variety of multi-length and multi-time-scale modeling and simulation methods. Such methods include electronic structure calculations via the quantum Density Functional Theory (DFT), ab initio, classical all-atom Molecular Dynamics (MD) simulations, and coarse-grained MD simulations. The explored processing and structural parameters include temperature, hydration levels, several QA-based head groups, various types of QA-based head groups and backbones, etc. Nowadays, many methods and software packages for molecular and materials modeling are available. Applications of such methods may help to understand the transportation mechanisms of OH− ions, the chemical stability of functional head groups, and many other relevant properties, leading to a performance-based molecular and structure design as well as, ultimately, improved AEM-based fuel cell performances. This contribution aims to introduce those molecular modeling methods and their recent applications to the AEM-based fuel cells research community.
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Yang Y, Wang J, Shu Y, Ji Y, Dong H, Li Y. Significance of density functional theory (DFT) calculations for electrocatalysis of N 2 and CO 2 reduction reactions. Phys Chem Chem Phys 2022; 24:8591-8603. [PMID: 35352075 DOI: 10.1039/d1cp05442b] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Density functional theory (DFT) based computational methods have shown great significance in developing high-performance electrocatalysts. In this perspective, we briefly summarized the state-of-the-art research progress of electrocatalysts for the nitrogen reduction reaction (NRR) and CO2 reduction reaction (CO2RR), which are important processes for the conversion of common molecules into value-added products. With the help of DFT calculations, various modulation strategies are employed to improve the catalytic activity and performance of NRR and CO2RR electrocatalysts. DFT calculations are performed to confirm the surface catalytic sites, evaluate the catalytic activity, reveal the possible reaction mechanisms, and design novel structures with high catalytic performance. By discussing the currently applied computational methods and conditions during the calculations, we outlined our concerns on the prospects and future challenges of DFT calculations in electrocatalysis studies.
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Affiliation(s)
- Yingke Yang
- School of Materials Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, China. .,Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Jiawen Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Yunpeng Shu
- School of Materials Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, China.
| | - Yujin Ji
- Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
| | - Huilong Dong
- School of Materials Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, China.
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China. .,Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China
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Brian D, Sun X. Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Via Machine Learning. J Phys Chem B 2021; 125:13267-13278. [PMID: 34825563 DOI: 10.1021/acs.jpcb.1c08260] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in an explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with R2 > 0.97 and both mean absolute error and root-mean-square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties, given any conformation of the molecule. We demonstrate some immediate utilities of the CT landscape such as calculating the ensemble-averaged CT rate constant and interpreting the effects of molecular structural features on the CT rate. The unprecedented CT landscape will be useful for investigating real-time CT dynamics in nanoscale- and mesoscale-condensed phase systems and for the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices.
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Affiliation(s)
- Dominikus Brian
- Division of Arts and Sciences, NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China.,Department of Chemistry, New York University, New York, New York 10003, United States
| | - Xiang Sun
- Division of Arts and Sciences, NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China.,Department of Chemistry, New York University, New York, New York 10003, United States.,State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai 200241, China
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Wen Y, Liu Y, Yan B, Gaudin T, Ma J, Ma H. Simultaneous Optimization of Donor/Acceptor Pairs and Device Specifications for Nonfullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors. J Phys Chem Lett 2021; 12:4980-4986. [PMID: 34015223 DOI: 10.1021/acs.jpclett.1c01099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Optimally efficient organic solar cells require not only a careful choice of new donor (D) and/or acceptor (A) molecules but also the fine-tuning of experimental fabrication conditions for organic solar cells (OSCs). Herein, a new framework for simultaneously optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationship (QSPR) model built by machine learning. Combining the device bulk properties with structural and electronic properties, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a large chemical space of 1 942 785 D/A pairs is explored to find potential synergistic ones. Favorable device bulk properties such as the root-mean-square of surfaces roughness for D/A blends and the D/A weight ratio are further screened by grid search methods. Overall, this study indicates that the simultaneous optimization of D/A molecule pairs and device specifications by theoretical calculations can accelerate the improvement of OSC efficiencies.
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Affiliation(s)
- Yaping Wen
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
| | - Yunhao Liu
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
| | - Bohan Yan
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
| | - Théophile Gaudin
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
| | - Jing Ma
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
| | - Haibo Ma
- School of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China
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