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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.00742 |
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| _version_ | 1866917192288698368 |
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| author | Lookman, Turab Liu, YuJie Gao, Zhibin |
| author_facet | Lookman, Turab Liu, YuJie Gao, Zhibin |
| contents | This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00742 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI Lookman, Turab Liu, YuJie Gao, Zhibin Computational Physics This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes. |
| title | Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2601.00742 |