Journal article
Artificial Intelligence for Materials Discovery, Development, and Optimization
ACS nano, v 19(30), pp 27116-27158
25 Jul 2025
PMID: 40711807
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This review highlights the recent transformative impact of artificial intelligence (AI), machine learning (ML), and deep learning (DL) on materials science, emphasizing their applications in materials discovery, development, and optimization. AI-driven methods have revolutionized materials discovery through structure generation, property prediction, high-throughput (HT) screening, and computational design while advancing development with improved characterization and autonomous experimentation. Optimization has also benefited from AI's ability to enhance materials design and processes. The review will introduce fundamental AI and ML concepts, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (RL), alongside advanced DL models such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), graph neural networks (GNNs), generative models, and Transformer-based models, which are critical for analyzing complex material data sets. It also covers core topics in materials informatics, including structure-property relationships, material descriptors, quantitative structure-property relationships (QSPR), and strategies for managing missing data and small data sets. Despite these advancements, challenges such as inconsistent data quality, limited model interpretability, and a lack of standardized data-sharing frameworks persist. Future efforts will focus on improving robustness, integrating causal reasoning and physics-informed AI, and leveraging multimodal models to enhance scalability and transparency, unlocking new opportunities for more advanced materials discovery, development, and optimization. Furthermore, the integration of quantum computing with AI will enable faster and more accurate results, and ethical frameworks will ensure responsible human-AI collaboration, addressing concerns of bias, transparency, and accountability in decision-making.
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Details
- Title
- Artificial Intelligence for Materials Discovery, Development, and Optimization
- Creators
- Benediktus Madika - Korea Advanced Institute of Science and TechnologyAditi Saha - Korea Advanced Institute of Science and TechnologyChaeyul Kang - Korea Advanced Institute of Science and TechnologyBatzorig Buyantogtokh - Korea Advanced Institute of Science and TechnologyJoshua Agar - Drexel University, Mechanical Engineering and MechanicsChris M. Wolverton - Northwestern UniversityPeter Voorhees - Northwestern UniversityPeter Littlewood - University of ChicagoSergei Kalinin - University of Tennessee at KnoxvilleSeungbum Hong - Korea Advanced Institute of Science and Technology
- Publication Details
- ACS nano, v 19(30), pp 27116-27158
- Publisher
- Amer Chemical Soc
- Number of pages
- 43
- Grant note
- RS-2023-00247245 / National Research Foundation of Korea National Research Foundation of Korea (NRF) - Korean government (MSIT); National Research Foundation of Korea; Ministry of Science & ICT (MSIT), Republic of Korea
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:001537081400001
- Scopus ID
- 2-s2.0-105013157302
- Other Identifier
- 991022065134604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Chemistry, Physical
- Materials Science, Multidisciplinary
- Nanoscience & Nanotechnology