Review
Chemistry, Inorganic & Nuclear
Hakan Demir, Hilal Daglar, Hasan Can Gulbalkan, Gokhan Onder Aksu, Seda Keskin
Summary: The reticular chemistry of MOFs enables the generation of countless materials, some of which can replace traditional porous materials in various fields. High-throughput computational screening approaches based on molecular simulations are used to identify optimal MOFs. However, more efficient methods are still needed due to the rapidly growing number of MOFs.
COORDINATION CHEMISTRY REVIEWS
(2023)
Review
Materials Science, Multidisciplinary
Eric R. Beyerle, Ziyue Zou, Pratyush Tiwary
Summary: With the advancement of computer processors and GPUs, data-intensive ML and AI have been applied to overcome challenges in crystal nucleation, including identifying better reaction coordinates, developing accurate force fields for multiple polymorphs, improving identification methods for crystal phases and structures, and generating improved coarse-grained models for nucleation studies.
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
(2023)
Article
Energy & Fuels
Mohamed Mehana, Javier E. Santos, Chelsea Neil, James William Carey, George Guthrie, Jeffery Hyman, Qinjun Kang, Satish Karra, Mathew Sweeney, Hongwu Xu, Hari Viswanathan
Summary: This article summarizes important findings and methods regarding shale reservoirs to improve hydrocarbon extraction efficiency and minimize environmental impact. By integrating fundamental knowledge and machine learning, a pathway to enhance model prediction capabilities is outlined, and science-based workflows and platforms for pressure-drawdown optimization, real-time management, and uncertainty quantification are presented.
Review
Chemistry, Multidisciplinary
Filip Formalik, Kaihang Shi, Faramarz Joodaki, Xijun Wang, Randall Q. Snurr
Summary: This article focuses on the role of atomic-level modeling in metal-organic framework (MOF) research, including key methods such as density functional theory, Monte Carlo simulations, and molecular dynamics simulations. These methods provide new insights into MOF properties, such as predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information for classical simulations. The use of machine learning techniques in quantum and classical simulations is also discussed, which can enhance accuracy, reduce computational costs, and optimize MOF stability.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Review
Chemistry, Physical
Xinhua Liu, Lisheng Zhang, Hanqing Yu, Jianan Wang, Junfu Li, Kai Yang, Yunlong Zhao, Huizhi Wang, Billy Wu, Nigel P. Brandon, Shichun Yang
Summary: This study demonstrates a method to evaluate the overall lifecycle of lithium-ion batteries (LIBs) and discusses the bridging role of characterization techniques and modeling. Key parameters extracted from characterization can be used as digital inputs for modeling. Furthermore, advanced computational techniques can enhance the understanding and control of the battery lifecycle. The introduction of digital twins techniques enables real-time monitoring and control, as well as intelligent manufacturing.
ADVANCED ENERGY MATERIALS
(2022)
Article
Geosciences, Multidisciplinary
Christopher S. Bretherton
Summary: Physics-informed machine learning is rapidly advancing in geophysical simulation. Recent advances in graph neural networks and vision transformers have shown superior forecasting skills for global weather within 1-7 days, at integration times over 1,000 times faster than conventional models. However, longer simulations deteriorate quickly. Achieving high skill in both weather and climate applications remains a challenging goal for machine learning.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Review
Oncology
Yubo Tang, Sharmila Anandasabapathy, Rebecca Richards-Kortum
Summary: Optical endoscopy is crucial for diagnosing and treating GI malignancies. Recent advancements in the field include novel optical imaging technologies, molecular probes, and AI algorithms, which show promise in improving detection and diagnosis of early cancers and precancerous lesions.
MOLECULAR ONCOLOGY
(2021)
Review
Biochemistry & Molecular Biology
Salvatore Galati, Miriana Di Stefano, Elisa Martinelli, Giulio Poli, Tiziano Tuccinardi
Summary: In silico target fishing is an emerging approach in drug discovery that aims to identify potential protein targets for query molecules. It is used to clarify the mechanism of action and biological activities of compounds with unknown targets, as well as to identify off-targets of drug candidates to prevent adverse effects. This method has become increasingly important for polypharmacology, drug repurposing, and the discovery of new drug targets.
Article
Engineering, Chemical
Xi Cheng, Yangyanbing Liao, Zhao Lei, Jie Li, Xiaolei Fan, Xin Xiao
Summary: In this work, a multi-scale design framework of MOF-based membrane separation for CO2/CH4 mixture is proposed, which integrates molecular simulation, machine learning, and process modeling and simulation. The adsorption isotherms, permeability, and selectivity of a MOF-based membrane (IRMOF-1) for CO2/CH4 separation are evaluated using molecular simulation. Prediction models of adsorption capacity and self-diffusivity are established using machine learning methods, and then integrated with membrane separation process modeling. Case studies demonstrate the feasibility and superiority of the proposed integrated framework.
JOURNAL OF MEMBRANE SCIENCE
(2023)
Review
Materials Science, Multidisciplinary
Hakan Demir, Seda Keskin
Summary: Membrane-based separation can save energy compared to conventional methods, and metal-organic frameworks (MOFs) are considered as next-generation materials for high separation performance and energy efficiency. Efficient modeling approaches are needed to expedite the design and selection of optimal MOF-based membranes. Recent developments in atomic simulations and artificial intelligence methods have opened up a new era of membrane modeling.
MACROMOLECULAR MATERIALS AND ENGINEERING
(2023)
Review
Materials Science, Multidisciplinary
Feiyang Wang, Hong-Hui Wu, Linshuo Dong, Guangfei Pan, Xiaoye Zhou, Shuize Wang, Ruiqiang Guo, Guilin Wu, Junheng Gao, Fu-Zhi Dai, Xinping Mao
Summary: Multi-component alloys have excellent performance but identifying optimized alloys for specific purposes is challenging due to the vast range of compositions and microstructures. Large-scale atomic simulations using interatomic potentials have been widely used to overcome this challenge. This review summarizes the latest advances in atomic simulation techniques for multi-component alloys and discusses the fitting processes for different types of interatomic potentials. It also addresses the challenges and future perspectives in developing machine learning potentials. Overall, it provides a valuable resource for researchers interested in developing optimized multi-component alloys using atomic simulation techniques.
JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Yu Cheng, Marco G. Giometto, Pit Kauffmann, Ling Lin, Chen Cao, Cody Zupnick, Harold Li, Qi Li, Yu Huang, Ryan Abernathey, Pierre Gentine
Summary: In large-eddy simulations, subgrid-scale processes are parameterized as a function of filtered grid-scale variables. This paper applies supervised deep neural networks (DNNs) to learn subgrid stresses and achieves higher correlation compared to traditional models, with applicability to different resolutions and stability conditions.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Review
Engineering, Environmental
Haonan Ma, Weidong Zhang, Yao Wang, Yibo Ai, Wenyue Zheng
Summary: In order to quantify corrosion damage and develop effective pipeline integrity management strategies, a reliable corrosion growth model is necessary. However, there is currently no generally accepted optimal method for predicting corrosion growth due to the complexity of the corrosion process, data availability issues, and limitations of existing models. This paper reviews the concepts, performance, and application of existing pipeline corrosion growth models, analyzes deterministic and probabilistic models in detail, and introduces the latest applications of machine learning and deep learning in corrosion growth modeling. Hybrid approach models, which combine various models, are proposed as they offer better performance and interpretability than single models and should be focused on in future corrosion growth prediction development. Suggestions for future development are also provided to address challenges and deficiencies in the current modeling process.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Review
Engineering, Marine
Hansong Tang, Charles Reid Nichols, Lynn Donelson Wright, Donald Resio
Summary: Coastal ocean flows are influenced by a complex suite of processes that need to be simulated at different temporal and spatial scales involving multiple physical phenomena.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Piotr Nawrocki, Jakub Pajor, Bartlomiej Sniezynski, Joanna Kolodziej
Summary: In this paper, a security-aware task allocation model strategy is proposed for Mobile Cloud Computing. The model generates an optimal and secure configuration of communication protocols to meet the specific data confidentiality requirements defined by end users. Resource utilization is predicted using Machine Learning methods, and the optimal secure service for task execution is selected. The results show a significant improvement in the level of security compared to a configuration based on processing time and energy consumption as the main criteria for task allocation.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Chemistry, Physical
Mohammad Atif Faiz Afzal, Mojtaba Haghighatlari, Sai Prasad Ganesh, Chong Cheng, Johannes Hachmann
JOURNAL OF PHYSICAL CHEMISTRY C
(2019)
Review
Chemistry, Multidisciplinary
Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava U. Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2020)
Article
Chemistry, Physical
Marcus D. Hanwell, Chris Harris, Alessandro Genova, Mojtaba Haghighatlari, Muammar El Khatib, Patrick Avery, Johannes Hachmann, Wibe Albert de Jong
Summary: The Open Chemistry project has developed an open-source framework that provides an end-to-end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using various modern tools. These tools are based on top open-source community projects like Jupyter, 3D accelerated visualization, NWChem, Psi4, as well as emerging machine learning and data mining tools.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
(2021)
Review
Chemistry, Multidisciplinary
Gaurav Vishwakarma, Aditya Sonpal, Johannes Hachmann
Summary: This review highlights two important issues to consider when applying machine learning in the chemical and materials domain: statistical loss function metrics for model validation and benchmarking, and uncertainty quantification of predictions. These topics are often overlooked by chemists, but are crucial for comparing model performance and developing successful machine learning applications in chemistry.
TRENDS IN CHEMISTRY
(2021)
Meeting Abstract
Chemistry, Multidisciplinary
Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Marcus Hanwell, Chris Harris, Alessandro Genova, Muammar El Khatib, Mojtaba Haghighatlari, Johannes Hachmann, Wibe Dejong
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Article
Chemistry, Multidisciplinary
Mohammad Atif Faiz Afzal, Aditya Sonpal, Mojtaba Haghighatlari, Andrew J. Schultz, Johannes Hachmann
Meeting Abstract
Chemistry, Multidisciplinary
Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Mojtaba Haghighatlari, Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Mojtaba Haghighatlari, Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2019)
Article
Chemistry, Physical
Mohammad Atif Faiz Afzal, Johannes Hachmann
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2019)
Meeting Abstract
Chemistry, Multidisciplinary
Johannes Hachmann
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
Review
Biotechnology & Applied Microbiology
Xiaoping Guan, Jinhao Bai, Jingchang Zhang, Ning Yang
Summary: This paper presents an overview of multiphase flow characteristics and modeling methods in polymer electrolyte membrane water electrolysis (PEMWE), emphasizing the significance of regulating the microstructure of the transport layer and distinguishing the effects of multiphase flow in the flow channel from those in the transport layer.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2024)
Article
Biotechnology & Applied Microbiology
Natasha J. Chrisandina, Shivam Vedant, Eleftherios Iakovou, Efstratios N. Pistikopoulos, Mahmoud M. El-Halwagi
Summary: This paper provides a brief review of recent progress in conceptual frameworks and quantitative metrics for analyzing and designing resilience-aware process systems, as well as discussing key research opportunities in the field of process system resilience, specifically the challenges in integrating resilience throughout the life cycle and across the spatiotemporal scales.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2024)
Article
Biotechnology & Applied Microbiology
Ya-Nan Yang, Jie Jin, Li-Tao Zhu, Yin-Ning Zhou, Zheng-Hong Luo
Summary: This article reviews the application of thermal runaway criteria, with emphasis on the significance of the divergence criterion. The general application procedures of the divergence criterion are illustrated through examples in process safety assessment, process parameter optimization, and process monitoring and control.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2024)
Article
Biotechnology & Applied Microbiology
Edirisooriya Mudiyanselage Nimanthi Thiloka Edirisooriya, Huiyao Wang, Sankha Banerjee, Karl Longley, William Wright, Walter Mizuno, Pei Xu
Summary: This study evaluates the economic feasibility of using alternative water sources for agriculture and identifies strategies to address the challenges. In the Southwest United States, the reuse of filtered disinfected municipal wastewater is the most cost-effective option. Using alternative water for irrigation faces challenges such as high costs, energy demand, concentrate disposal, and soil salinity management. Economic feasibility can be improved by implementing renewable energy-powered decentralized desalination systems.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2024)
Article
Biotechnology & Applied Microbiology
Manoj Kolel-Veetil
Summary: Recent advances in nonthermal plasma technology have led to remarkable progress in the remediation of PFAS, occurring at the water-bubble/air interface and increasingly in the bubble interiors, with simultaneous presence of multiple oxidative and reductive species.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2024)
