Unleashing the Power of Artificial Intelligence in Materials Design
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/16/17/5927 |
| _version_ | 1797582208346619904 |
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| author | Silvia Badini Stefano Regondi Raffaele Pugliese |
| author_facet | Silvia Badini Stefano Regondi Raffaele Pugliese |
| author_sort | Silvia Badini |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement. |
| first_indexed | 2024-03-10T23:18:47Z |
| format | Article |
| id | doaj.art-ab67528ca8e2409f8b484e6104046b82 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T23:18:47Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-ab67528ca8e2409f8b484e6104046b822023-11-19T08:27:51ZengMDPI AGMaterials1996-19442023-08-011617592710.3390/ma16175927Unleashing the Power of Artificial Intelligence in Materials DesignSilvia Badini0Stefano Regondi1Raffaele Pugliese2NeMO Lab, ASST GOM Niguarda Cà Granda Hospital, 20162 Milan, ItalyNeMO Lab, ASST GOM Niguarda Cà Granda Hospital, 20162 Milan, ItalyNeMO Lab, ASST GOM Niguarda Cà Granda Hospital, 20162 Milan, ItalyThe integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.https://www.mdpi.com/1996-1944/16/17/5927artificial intelligencemachine learningmaterials designmaterials informaticsmaterials properties prediction |
| spellingShingle | Silvia Badini Stefano Regondi Raffaele Pugliese Unleashing the Power of Artificial Intelligence in Materials Design Materials artificial intelligence machine learning materials design materials informatics materials properties prediction |
| title | Unleashing the Power of Artificial Intelligence in Materials Design |
| title_full | Unleashing the Power of Artificial Intelligence in Materials Design |
| title_fullStr | Unleashing the Power of Artificial Intelligence in Materials Design |
| title_full_unstemmed | Unleashing the Power of Artificial Intelligence in Materials Design |
| title_short | Unleashing the Power of Artificial Intelligence in Materials Design |
| title_sort | unleashing the power of artificial intelligence in materials design |
| topic | artificial intelligence machine learning materials design materials informatics materials properties prediction |
| url | https://www.mdpi.com/1996-1944/16/17/5927 |
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