Artificial Intelligence for Materials Discovery, Development, and Optimization
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".