Disorder in energy materials and strategies to model it
The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Advances in Physics: X |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23746149.2020.1848458 |
| _version_ | 1819009310216159232 |
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| author | Jose Carlos Madrid Madrid Kulbir Kaur Ghuman |
| author_facet | Jose Carlos Madrid Madrid Kulbir Kaur Ghuman |
| author_sort | Jose Carlos Madrid Madrid |
| collection | DOAJ |
| description | The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various degrees of disorder resulting in pathways required for optimizing their efficiencies. This article provides a critical overview of such computational advancements specifically for designing realistic materials with various types of disorders for sustainable energy applications such as catalysts and electrochemical devices. The ultimate goal is to gain a thorough knowledge of the traditional approaches (implemented via tools such as density functional theory, and molecular dynamics) as well as modern approaches such as machine learning that exist for modeling the disorder present in materials, thereby identify the future opportunities for energy materials design and discovery. |
| first_indexed | 2024-12-21T00:54:20Z |
| format | Article |
| id | doaj.art-0ce9c76c781f4ff2be82d1a010b46bd8 |
| institution | Directory Open Access Journal |
| issn | 2374-6149 |
| language | English |
| last_indexed | 2024-12-21T00:54:20Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Advances in Physics: X |
| spelling | doaj.art-0ce9c76c781f4ff2be82d1a010b46bd82022-12-21T19:21:20ZengTaylor & Francis GroupAdvances in Physics: X2374-61492021-01-016110.1080/23746149.2020.18484581848458Disorder in energy materials and strategies to model itJose Carlos Madrid Madrid0Kulbir Kaur Ghuman1Institut National De La RecherchéInstitut National De La RecherchéThe functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various degrees of disorder resulting in pathways required for optimizing their efficiencies. This article provides a critical overview of such computational advancements specifically for designing realistic materials with various types of disorders for sustainable energy applications such as catalysts and electrochemical devices. The ultimate goal is to gain a thorough knowledge of the traditional approaches (implemented via tools such as density functional theory, and molecular dynamics) as well as modern approaches such as machine learning that exist for modeling the disorder present in materials, thereby identify the future opportunities for energy materials design and discovery.http://dx.doi.org/10.1080/23746149.2020.1848458computational material scienceclassical and quantum calculationsdisordered materialsenergy materialscatalysis and electrochemical devices |
| spellingShingle | Jose Carlos Madrid Madrid Kulbir Kaur Ghuman Disorder in energy materials and strategies to model it Advances in Physics: X computational material science classical and quantum calculations disordered materials energy materials catalysis and electrochemical devices |
| title | Disorder in energy materials and strategies to model it |
| title_full | Disorder in energy materials and strategies to model it |
| title_fullStr | Disorder in energy materials and strategies to model it |
| title_full_unstemmed | Disorder in energy materials and strategies to model it |
| title_short | Disorder in energy materials and strategies to model it |
| title_sort | disorder in energy materials and strategies to model it |
| topic | computational material science classical and quantum calculations disordered materials energy materials catalysis and electrochemical devices |
| url | http://dx.doi.org/10.1080/23746149.2020.1848458 |
| work_keys_str_mv | AT josecarlosmadridmadrid disorderinenergymaterialsandstrategiestomodelit AT kulbirkaurghuman disorderinenergymaterialsandstrategiestomodelit |