1
|
Tang J, Xiong Y, Ye L, Li Y, Li W, Yu P. Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses. ACS APPLIED MATERIALS & INTERFACES 2024; 16:52692-52702. [PMID: 39312640 DOI: 10.1021/acsami.4c11926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.
Collapse
Affiliation(s)
- Jie Tang
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| | - YuanQiang Xiong
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| | - LiYu Ye
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| | - YuHang Li
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| | - WanJun Li
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| | - Peng Yu
- Chongqing Key Laboratory of Photo-Electric Functional Materials and Laser Technology, College of Physics and Electronic Engineering, Chongqing Normal University, Chongqing 401331, China
| |
Collapse
|
2
|
Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
Collapse
Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| |
Collapse
|
3
|
Chen Z, Chen C, Yang G, He X, Chi X, Zeng Z, Chen X. Research integrity in the era of artificial intelligence: Challenges and responses. Medicine (Baltimore) 2024; 103:e38811. [PMID: 38968491 PMCID: PMC11224801 DOI: 10.1097/md.0000000000038811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/13/2024] [Indexed: 07/07/2024] Open
Abstract
The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.
Collapse
Affiliation(s)
- Ziyu Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Changye Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Guozhao Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Xiangpeng He
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Xiaoxia Chi
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Zhuoying Zeng
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
- Chemical Analysis & Physical Testing Institute, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Xuhong Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| |
Collapse
|
4
|
Sun W, You X, Zhao X, Zhang X, Yang C, Zhang F, Yu J, Yang K, Wang J, Xu F, Chang Y, Qu B, Zhao X, He Y, Wang Q, Chen J, Qing G. Precise Capture and Dynamic Release of Circulating Liver Cancer Cells with Dual-Histidine-Based Cell Imprinted Hydrogels. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402379. [PMID: 38655900 DOI: 10.1002/adma.202402379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Indexed: 04/26/2024]
Abstract
Circulating tumor cells (CTCs) detection presents significant advantages in diagnosing liver cancer due to its noninvasiveness, real-time monitoring, and dynamic tracking. However, the clinical application of CTCs-based diagnosis is largely limited by the challenges of capturing low-abundance CTCs within a complex blood environment while ensuring them alive. Here, an ultrastrong ligand, l-histidine-l-histidine (HH), specifically targeting sialylated glycans on the surface of CTCs, is designed. Furthermore, HH is integrated into a cell-imprinted polymer, constructing a hydrogel with precise CTCs imprinting, high elasticity, satisfactory blood compatibility, and robust anti-interference capacities. These features endow the hydrogel with excellent capture efficiency (>95%) for CTCs in peripheral blood, as well as the ability to release CTCs controllably and alive. Clinical tests substantiate the accurate differentiation between liver cancer, cirrhosis, and healthy groups using this method. The remarkable diagnostic accuracy (94%), lossless release of CTCs, material reversibility, and cost-effectiveness ($6.68 per sample) make the HH-based hydrogel a potentially revolutionary technology for liver cancer diagnosis and single-cell analysis.
Collapse
Affiliation(s)
- Wenjing Sun
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, P. R. China
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Xin You
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Xinjia Zhao
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Xiaoyu Zhang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Chunhui Yang
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Fusheng Zhang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| | - Jiaqi Yu
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| | - Kaiguang Yang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Jixia Wang
- Ganjiang Chinese Medicine Innovation Center, Nanchang, 330000, P. R. China
| | - Fangfang Xu
- Ganjiang Chinese Medicine Innovation Center, Nanchang, 330000, P. R. China
| | - Yongxin Chang
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
| | - Boxin Qu
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Xinmiao Zhao
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, P. R. China
| | - Yuxuan He
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, P. R. China
| | - Qi Wang
- Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, 116023, P. R. China
| | - Jinghua Chen
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, P. R. China
| | - Guangyan Qing
- State Key Laboratory of Medical Proteomics, National Chromatographic R&A Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, P. R. China
- College of Chemistry and Chemical Engineering, Wuhan Textile University, Wuhan, 430200, P. R. China
| |
Collapse
|
5
|
Assi DS, Huang H, Karthikeyan V, Theja VCS, de Souza MM, Roy VAL. Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306254. [PMID: 38532608 DOI: 10.1002/adma.202306254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 03/06/2024] [Indexed: 03/28/2024]
Abstract
Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.
Collapse
Affiliation(s)
- Dani S Assi
- School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong, China
| | - Hongli Huang
- Electronics and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, U.K
| | - Vaithinathan Karthikeyan
- School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong, China
| | - Vaskuri C S Theja
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Maria Merlyne de Souza
- Electronics and Electrical Engineering, The University of Sheffield, Sheffield, S3 7HQ, U.K
| | - Vellaisamy A L Roy
- School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong, China
| |
Collapse
|
6
|
Aldossary A, Campos-Gonzalez-Angulo JA, Pablo-García S, Leong SX, Rajaonson EM, Thiede L, Tom G, Wang A, Avagliano D, Aspuru-Guzik A. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402369. [PMID: 38794859 DOI: 10.1002/adma.202402369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
Collapse
Affiliation(s)
- Abdulrahman Aldossary
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | | | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
| | - Shi Xuan Leong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Ella Miray Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Luca Thiede
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Andrew Wang
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Davide Avagliano
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada
- Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada
| |
Collapse
|
7
|
Khan MK, Raza M, Shahbaz M, Hussain I, Khan MF, Xie Z, Shah SSA, Tareen AK, Bashir Z, Khan K. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem 2024; 12:1408740. [PMID: 38882215 PMCID: PMC11176507 DOI: 10.3389/fchem.2024.1408740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.
Collapse
Affiliation(s)
- Mahroza Kanwal Khan
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Mohsin Raza
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Muhammad Shahbaz
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Iftikhar Hussain
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, United States
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
| | - Zhongjian Xie
- Shenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China
| | - Syed Shoaib Ahmad Shah
- Department of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Ayesha Khan Tareen
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
| | - Zoobia Bashir
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Karim Khan
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| |
Collapse
|
8
|
Zhu C, Bamidele EA, Shen X, Zhu G, Li B. Machine Learning Aided Design and Optimization of Thermal Metamaterials. Chem Rev 2024; 124:4258-4331. [PMID: 38546632 PMCID: PMC11009967 DOI: 10.1021/acs.chemrev.3c00708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 04/11/2024]
Abstract
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
Collapse
Affiliation(s)
- Changliang Zhu
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Emmanuel Anuoluwa Bamidele
- Materials
Science and Engineering Program, University
of Colorado, Boulder, Colorado 80309, United States
| | - Xiangying Shen
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Guimei Zhu
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
| | - Baowen Li
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
- Department
of Physics, Southern University of Science
and Technology, Shenzhen 518055, P.R. China
- Shenzhen
International Quantum Academy, Shenzhen 518048, P.R. China
- Paul M. Rady
Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder 80309, United States
| |
Collapse
|
9
|
Wang Z, Chen A, Tao K, Han Y, Li J. MatGPT: A Vane of Materials Informatics from Past, Present, to Future. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306733. [PMID: 37813548 DOI: 10.1002/adma.202306733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/05/2023] [Indexed: 10/17/2023]
Abstract
Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.
Collapse
Affiliation(s)
- Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - An Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kehao Tao
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| |
Collapse
|
10
|
Park S, Lee D, Kang J, Choi H, Park JH. Laterally gated ferroelectric field effect transistor (LG-FeFET) using α-In 2Se 3 for stacked in-memory computing array. Nat Commun 2023; 14:6778. [PMID: 37880220 PMCID: PMC10600126 DOI: 10.1038/s41467-023-41991-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 09/26/2023] [Indexed: 10/27/2023] Open
Abstract
In-memory computing is an attractive alternative for handling data-intensive tasks as it employs parallel processing without the need for data transfer. Nevertheless, it necessitates a high-density memory array to effectively manage large data volumes. Here, we present a stacked ferroelectric memory array comprised of laterally gated ferroelectric field-effect transistors (LG-FeFETs). The interlocking effect of the α-In2Se3 is utilized to regulate the channel conductance. Our study examined the distinctive characteristics of the LG-FeFET, such as a notably wide memory window, effective ferroelectric switching, long retention time (over 3 × 104 seconds), and high endurance (over 105 cycles). This device is also well-suited for implementing vertically stacked structures because decreasing its height can help mitigate the challenges associated with the integration process. We devised a 3D stacked structure using the LG-FeFET and verified its feasibility by performing multiply-accumulate (MAC) operations in a two-tier stacked memory configuration.
Collapse
Affiliation(s)
- Sangyong Park
- Flash Technology Development Team, R&D Center, Device Solutions, Samsung Electronics Co. Ltd, Hwasung, 18448, Korea
- Department of Semiconductor and Display Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Dongyoung Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Juncheol Kang
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Hojin Choi
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea.
- SKKU Advanced Institute of Nano-Technology (SAINT), Sungkyunkwan University (SKKU), Suwon, Korea.
- Department of Semiconductor Convergence Engineering, Sungkyunkwan University (SKKU), Suwon, Korea.
| |
Collapse
|
11
|
Reichstein J, Müssig S, Wintzheimer S, Mandel K. Communicating Supraparticles to Enable Perceptual, Information-Providing Matter. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2306728. [PMID: 37786273 DOI: 10.1002/adma.202306728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Indexed: 10/04/2023]
Abstract
Materials are the fundament of the physical world, whereas information and its exchange are the centerpieces of the digital world. Their fruitful synergy offers countless opportunities for realizing desired digital transformation processes in the physical world of materials. Yet, to date, a perfect connection between these worlds is missing. From the perspective, this can be achieved by overcoming the paradigm of considering materials as passive objects and turning them into perceptual, information-providing matter. This matter is capable of communicating associated digitally stored information, for example, its origin, fate, and material type as well as its intactness on demand. Herein, the concept of realizing perceptual, information-providing matter by integrating customizable (sub-)micrometer-sized communicating supraparticles (CSPs) is presented. They are assembled from individual nanoparticulate and/or (macro)molecular building blocks with spectrally differentiable signals that are either robust or stimuli-susceptible. Their combination yields functional signal characteristics that provide an identification signature and one or multiple stimuli-recorder features. This enables CSPs to communicate associated digital information on the tagged material and its encountered stimuli histories upon signal readout anywhere across its life cycle. Ultimately, CSPs link the materials and digital worlds with numerous use cases thereof, in particular fostering the transition into an age of sustainability.
Collapse
Affiliation(s)
- Jakob Reichstein
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
| | - Stephan Müssig
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
| | - Susanne Wintzheimer
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
- Fraunhofer-Institute for Silicate Research ISC, Neunerplatz 2, D-97082, Würzburg, Germany
| | - Karl Mandel
- Department of Chemistry and Pharmacy, Inorganic Chemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstraße 1, D-91058, Erlangen, Germany
- Fraunhofer-Institute for Silicate Research ISC, Neunerplatz 2, D-97082, Würzburg, Germany
| |
Collapse
|
12
|
Wu M, Tikhonov E, Tudi A, Kruglov I, Hou X, Xie C, Pan S, Yang Z. Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2300848. [PMID: 36929243 DOI: 10.1002/adma.202300848] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/03/2023] [Indexed: 05/17/2023]
Abstract
The development of a data-driven science paradigm is greatly revolutionizing the process of materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the birefringent phase-matching ability to deep-ultraviolet (UV) region is of vital significance for the field of laser technologies. Herein, a target-driven materials design framework combining high-throughput calculations (HTC), crystal structure prediction, and interpretable machine learning (ML) is proposed to accelerate the discovery of deep-UV NLO materials. Using a dataset generated from HTC, an ML regression model for predicting birefringence is developed for the first time, which exhibits a possibility of achieving fast and accurate prediction. Essentially, crystal structures are adopted as the only known input of this model to establish a close structure-property relationship mapping birefringence. Utilizing the ML-predicted birefringence which can affect the shortest phase-matching wavelength, a full list of potential chemical compositions based on an efficient screening strategy is identified. Further, eight structures with good stability are discovered to show potential applications in the deep-UV region, owing to their promising NLO-related properties. This study provides a new insight into the discovery of NLO materials and this design framework can identify desired materials with high performances in the broad chemical space at a low computational cost.
Collapse
Affiliation(s)
- Mengfan Wu
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Evgenii Tikhonov
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
| | - Abudukadi Tudi
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ivan Kruglov
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
| | - Xueling Hou
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Congwei Xie
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
| | - Shilie Pan
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihua Yang
- Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| |
Collapse
|