1
|
Zarrouk T, Ibragimova R, Bartók AP, Caro MA. Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon. J Am Chem Soc 2024; 146:14645-14659. [PMID: 38749497 PMCID: PMC11140750 DOI: 10.1021/jacs.4c01897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024]
Abstract
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.
Collapse
Affiliation(s)
- Tigany Zarrouk
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Rina Ibragimova
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| | - Albert P. Bartók
- Department
of Physics, University of Warwick, Coventry CV4 7AL, U.K.
- Warwick
Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K.
| | - Miguel A. Caro
- Department
of Chemistry and Materials Science, Aalto
University, Espoo 02150, Finland
| |
Collapse
|
2
|
Liu S, Chen X, Liu G. Conjugated polymers for information storage and neuromorphic computing. POLYM INT 2020. [DOI: 10.1002/pi.6017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Shuzhi Liu
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
| | - Xinhui Chen
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
| | - Gang Liu
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
- Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou China
| |
Collapse
|
3
|
Ginnaram S, Qiu JT, Maikap S. Controlling Cu Migration on Resistive Switching, Artificial Synapse, and Glucose/Saliva Detection by Using an Optimized AlO x Interfacial Layer in a-CO x -Based Conductive Bridge Random Access Memory. ACS OMEGA 2020; 5:7032-7043. [PMID: 32258939 PMCID: PMC7114759 DOI: 10.1021/acsomega.0c00795] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 03/05/2020] [Indexed: 05/29/2023]
Abstract
The Cu migration is controlled by using an optimized AlO x interfacial layer, and effects on resistive switching performance, artificial synapse, and human saliva detection in an amorphous-oxygenated-carbon (a-CO x )-based CBRAM platform have been investigated for the first time. The 4 nm-thick AlO x layer in the Cu/AlO x /a-CO x /TiN x O y /TiN structure shows consecutive >2000 DC switching, tight distribution of SET/RESET voltages, a long program/erase (P/E) endurance of >109 cycles at a low operation current of 300 μA, and artificial synaptic characteristics under a small pulse width of 100 ns. After a P/E endurance of >108 cycles, the Cu migration is observed by both ex situ high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy mapping images. Furthermore, the optimized Cu/AlO x /a-CO x /TiN x O y /TiN CBRAM detects glucose with a low concentration of 1 pM, and real-time measurement of human saliva with a small sample volume of 1 μL is also detected repeatedly in vitro. This is owing to oxidation-reduction of Cu electrode, and the switching mechanism is explored. Therefore, this CBRAM device is beneficial for future artificial intelligence application.
Collapse
Affiliation(s)
- Sreekanth Ginnaram
- Thin
Film Nano Tech. Lab., Department of Electronic Engineering, Chang Gung University (CGU), No. 259, Wen-Hwa 1st Rd., Guishan, Taoyuan 33302, Taiwan
| | - Jiantai Timothy Qiu
- Division
of Gynecology-Oncology, Department of Obstetrics/Gynecology, Chang Gung Memorial Hospital (CGMH), No. 5, Fu-Shing St., Taoyuan 333, Taiwan
- Department
of Biomedical Sciences, School of Medicine, Chang Gung University (CGU), No. 259, Wen-Hwa 1st Rd., Guishan, Taoyuan 33302, Taiwan
| | - Siddheswar Maikap
- Thin
Film Nano Tech. Lab., Department of Electronic Engineering, Chang Gung University (CGU), No. 259, Wen-Hwa 1st Rd., Guishan, Taoyuan 33302, Taiwan
- Division
of Gynecology-Oncology, Department of Obstetrics/Gynecology, Chang Gung Memorial Hospital (CGMH), No. 5, Fu-Shing St., Taoyuan 333, Taiwan
- Department
of Obstetrics and Gynecology, Keelung Chang
Gung Memorial Hospital (CGMH), No. 222, Maijin Rd., Anle, Keelung 204, Taiwan
| |
Collapse
|
4
|
Raeber TJ, Barlow AJ, Zhao ZC, McKenzie DR, Partridge JG, McCulloch DG, Murdoch BJ. Sensory gating in bilayer amorphous carbon memristors. NANOSCALE 2018; 10:20272-20278. [PMID: 30362489 DOI: 10.1039/c8nr05328f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Multi-state amorphous carbon-based memory devices have been developed that exhibit both bipolar and unipolar resistive switching behaviour. These modes of operation were implemented independently to access multiple resistance states, enabling higher memory density than conventional binary non-volatile memory technologies. The switching characteristics have been further utilised to study synaptic computational functions that could be implemented in artificial neural networks. Notably, paired-pulse inhibition (PPI) is observed at bio-realistic timescales (<100 ms). Devices displaying this rich synaptic behaviour could function as robust stand-alone synapse-inspired memory or be applied as filters for specialised neuromorphic circuits and sensors.
Collapse
Affiliation(s)
- T J Raeber
- School of Science, RMIT University, VIC 3001, Melbourne, Australia.
| | | | | | | | | | | | | |
Collapse
|