scholarly journals Design high-entropy carbide ceramics from machine learning

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Jun Zhang ◽  
Biao Xu ◽  
Yaoxu Xiong ◽  
Shihua Ma ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Rational Design ◽  
Single Phase ◽  
Transition Metal Carbide ◽  
High Entropy ◽  
Application Potential ◽  
High Prediction ◽  
Chemical Descriptors ◽  
Carbide Ceramics

AbstractHigh-entropy ceramics (HECs) have shown great application potential under demanding conditions, such as high stresses and temperatures. However, the immense phase space poses great challenges for the rational design of new high-performance HECs. In this work, we develop machine-learning (ML) models to discover high-entropy ceramic carbides (HECCs). Built upon attributes of HECCs and their constituent precursors, our ML models demonstrate a high prediction accuracy (0.982). Using the well-trained ML models, we evaluate the single-phase probability of 90 HECCs that are not experimentally reported so far. Several of these predictions are validated by our experiments. We further establish the phase diagrams for non-equiatomic HECCs spanning the whole composition space by which the single-phase regime can be easily identified. Our ML models can predict both equiatomic and non-equiatomic HECs based solely on the chemical descriptors of constituent transition-metal-carbide precursors, which paves the way for the high-throughput design of HECCs with superior properties.

Low‐temperature sintering of single‐phase, high‐entropy carbide ceramics

2019 ◽  
Vol 102 (12) ◽  
pp. 7217-7224 ◽  
Author(s):  
Lun Feng ◽  
William G. Fahrenholtz ◽  
Gregory E. Hilmas
Keyword(s):  
Low Temperature ◽  
Single Phase ◽  
Low Temperature Sintering ◽  
High Entropy ◽  
Carbide Ceramics

scholarly journals Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

2019 ◽  
Vol 5 (11) ◽  
pp. eaay4275 ◽  
Author(s):  
Wenbo Sun ◽  
Yujie Zheng ◽  
Ke Yang ◽  
Qi Zhang ◽  
Akeel A. Shah ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Molecular Design ◽  
Structure Property ◽  
Photovoltaic Properties ◽  
Chemical Structures ◽  
Fast Screening ◽  
Organic Photovoltaic Materials ◽  
High Prediction ◽  
The Relationship

In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field.

Strength of single‐phase high‐entropy carbide ceramics up to 2300°C

2020 ◽  
Vol 104 (1) ◽  
pp. 419-427 ◽  
Author(s):  
Lun Feng ◽  
Wei‐Ting Chen ◽  
William G. Fahrenholtz ◽  
Gregory E. Hilmas
Keyword(s):  
Single Phase ◽  
High Entropy ◽  
Carbide Ceramics

scholarly journals Local Lattice Distortion in High-Entropy Carbide Ceramics

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1399
Author(s):  
Huijuan Ge ◽  
Chengfeng Cui ◽  
Hongquan Song ◽  
Fuyang Tian
Keyword(s):  
Single Phase ◽  
Lattice Distortion ◽  
Formation Enthalpy ◽  
Atomic Radius ◽  
Alloying Element ◽  
Local Lattice Distortion ◽  
High Entropy ◽  
Local Lattice ◽  
Binary Carbide ◽  
Carbide Ceramics

Using the ab initio calculations, we study the lattice distortion of HfNbTaTiVC5, HfNbTaTiZrC5 and MoNbTaTiVC5 high-entropy carbide (HEC) ceramics. Results indicate that the Bader atomic radius and charge transfer in HECs is very close to those from binary carbide. The degree of lattice distortion strongly depends on the alloying element. The Bader atomic radius can excellently describe the lattice distortion in HEC. Further, the corresponding atomic radius and formation enthalpy of binary carbides may be indicators to predict the single-phase HECs.

scholarly journals Preparation of Single-phase High Entropy Carbides by a Modified Citric Acid Complexing Method

2021 ◽  
Author(s):  
Hu-Lin Liu ◽  
Feng-Zhen DANG ◽  
De-Wei NI ◽  
Chang-Qing LIU ◽  
Yun-Long XUE ◽  
...  
Keyword(s):  
Citric Acid ◽  
Single Phase ◽  
Raw Materials ◽  
Low Cost ◽  
Transition Metal Carbide ◽  
Synthesis Process ◽  
Step Process ◽  
High Entropy ◽  
One Step ◽  
Sluggish Diffusion

Abstract We developed a new method to synthesize single-phase transition metal carbide powders by combining citric acid complexing method and ball-milling dispersion. High-entropy carbides (Zr0.25Ti0.25Ta0.25Nb0.25)C (4TmC), (Zr0.2Ti0.2Ta0.2Nb0.2Hf0.2)C (5TmC-H) and (Zr0.2Ti0.2Ta0.2Nb0.2Mo0.2)C (5TmC-M) were successfully fabricated by this method using low-cost raw materials. The element and phase composition and microstructures of the obtained carbide powders were investigated. The relationships of synthesis process and temperature with chemical composition were also discussed. (Zr0.25Ti0.25Ta0.25Nb0.25)C can be obtained by a one-step process at 1550 °C, while (Zr0.2Ti0.2Ta0.2Nb0.2Hf0.2)C and (Zr0.2Ti0.2Ta0.2Nb0.2Mo0.2)C are fabricated by a two-step process of carbothermal reduction followed by solid solution at the temperatures not lower than 1850 °C and 1650 °C. The higher synthesis temperatures of the five-component carbides are attributed to the obvious sluggish diffusion effect induced by the larger lattice distortions. The particle sizes of (Zr0.25Ti0.25Ta0.25Nb0.25)C, (Zr0.2Ti0.2Ta0.2Nb0.2Hf0.2)C and (Zr0.2Ti0.2Ta0.2Nb0.2Mo0.2)C powders are 118.2±26.1 nm (at 1550 °C), 284.8±73.7 nm (at 1850 °C) and 65.5±13.9 nm (at 1750 °C), respectively.

scholarly journals Novel P2-Type Layered Medium-Entropy Ceramics Oxide as Cathode Material for Sodium Ion Batteries

2021 ◽  
Author(s):  
S.H. Luo ◽  
Sheng-xue Yan ◽  
Liu Yang ◽  
Jian Feng ◽  
Peng-wei Li ◽  
...  
Keyword(s):  
Electrode Material ◽  
High Performance ◽  
Rational Design ◽  
Sodium Ion ◽  
Sodium Ion Batteries ◽  
Cycle Stability ◽  
Energy Storage Devices ◽  
Solid State Method ◽  
Voltage Range ◽  
High Entropy

Abstract Similar to high-entropy oxides (HEOs), medium-entropy oxides (MEOs) are a new type of single-phase solid solution material. As a positive electrode material for sodium ion batteries (SIBs), it has been rarely reported. Here, we first proposed the concept of the application of MEOs in SIBs. P2-type 3-cation oxide Na2/3Ni1/3Mn1/3Fe1/3O2 (NaNMF) and 4-cation oxide Na2/3Ni1/3Mn1/3Fe1/3−xAlxO2 (NaNMFA) were prepared by solid-state method, rather than the doping technology. In addition, the importance of the concept of entropy stabilization in material performance and battery cycling was demonstrated by testing 3-cation oxide (NaNMF) and 4-cation oxide in (NaNMFA) the same system. As a result, NaNMFA can provide a reversible capacity of about 125.6 mAh g–1 in the voltage range of 2-4.2 V, and has enhanced cycle stability. The capacity and decay law of the mid-entropy oxide battery indicate that the configuration entropy (1.28R (NaNMFA) > 1.10R (NaNMF)) of the cationic system is the main factor affecting the structural stability and cycle stability of the electrode material. This work emphasizes that the rational design of MEOs with novel structures and different electrochemically active elements may be an effective strategy for exploring high-performance SIBs cathode materials in next-generation energy storage devices.

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