rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: AI-Powered Innovation in Materials Science
    sub title: The Role of Language Models in Discovery and Design
  name:
    name part: Jiang, Xue.
    role:
      role term: creator
  name:
    name part: Su, Yanjing.
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    place:
      place term: Newark
    publisher: John Wiley & Sons, Incorporated
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (594 p.)
  abstract: Accelerate materials innovation using language models and machine learning methods Language models and machine learning are transforming how researchers discover, design, and optimize advanced materials.
  table of contents: Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1: The Revolution of AI for Materials -- 1.1 Introduction -- 1.2 What Is AI4Mater? -- 1.2.1 Definition -- 1.2.2 History -- 1.2.3 Motivation -- 1.3 Foundations and Frontiers -- 1.4 Previous Works -- 1.4.1 Materials Data Infrastructure -- 1.4.2 Machine Learning in Materials -- 1.4.3 Autonomous Experiments -- 1.4.4 Intelligent Computation -- 1.4.5 Intelligent Manufacture -- References -- Chapter 2: Fundamentals of Language Models and NLP -- 2.1 Introduction -- 2.2 Historical Evolution of NLP -- 2.2.1 Statistical Language Models
  table of contents: 2.2.2 Machine Learning Models -- 2.2.3 Deep Learning Models -- 2.2.4 Pre-training and LLMs -- 2.3 Core Architectures in Modern NLP -- 2.3.1 Language Models -- 2.3.2 Encoder-decoder -- 2.3.3 Transformers -- 2.4 Training and Optimization Methods for LLMs -- 2.4.1 Pretraining Strategy -- 2.4.2 Fine-tuning Strategy -- 2.4.3 RAG -- 2.4.4 Agent -- 2.4.5 Reinforcement Learning -- 2.4.6 Corpus Building -- 2.5 Major Language Model Families -- 2.5.1 Word2Vector -- 2.5.2 BERT -- 2.5.3 GPT -- 2.5.4 T5 -- 2.6 Practical Tools and Libraries for NLP -- 2.6.1 Hugging Face -- 2.6.2 PyTorch -- 2.6.3 NLTK
  table of contents: 4.2.1 Foundations of Unsupervised Word Embeddings for Materials Science -- 4.2.2 Encoding Scientific Knowledge Through Semantic Relationships -- 4.2.3 Element Embedding Spaces and Periodic Table Correlations -- 4.2.4 Discovering and Predicting Materials with Word Embeddings -- 4.2.5 Historical Validation and Temporal Trends in Discovery Prediction -- 4.2.6 Unconventional Discoveries and Knowledge Beyond Composition -- 4.2.7 Conclusion -- 4.3 Context Similarity for Designing UHEAs -- 4.3.1 From Linguistics to Materials Science -- 4.3.2 Element Embeddings and Chemical Intuition
  table of contents: 4.3.3 From Similarity Scores to Alloy Discovery -- 4.3.4 Correlation with Thermodynamic Predictors -- 4.3.5 Designing Lightweight HEAs -- 4.3.6 Integration with ICME and KGs -- 4.4 Conclusion -- References -- Chapter 5: Materials Transformer-based Models -- 5.1 Introduction -- 5.2 Encoder-based Models for Materials -- 5.2.1 BatteryBERT -- 5.2.2 MatSciBERT -- 5.3 Decoder-based Models for Materials -- 5.3.1 Chemistry Assistant: A Decoder-based Model for MOF Synthesis Text Mining and Prediction -- 5.3.2 NatureLM: Unlocking the Language of Nature for Scientific Discovery -- 5.4 Conclusion
  note: Description based upon print version of record.
  note: References -- Chapter 3: Reinforcement Learning in Materials -- 3.1 Introduction -- 3.1.1 The Basic Concepts of RL -- 3.1.2 The Development History of RL -- 3.2 Key Algorithm -- 3.2.1 Bellman Equation -- 3.2.2 Value-based Algorithm -- 3.2.3 Policy-based Algorithm -- 3.2.4 AC Methods -- 3.3 Typical Applications of RL -- 3.3.1 Applications of RL in Materials Science -- 3.3.2 Applications of RL in LLMs -- References -- Chapter 4: Materials Word Embedding Models -- 4.1 Introduction -- 4.2 Unsupervised Word Embeddings Capture Latent Knowledge
  note: References
  subject:
    topic: Materials
    topic: Technological innovations
  subject:
    topic: Artificial intelligence
    topic: Engineering applications
  classification: TA403.6
  classification: 540
  related item:
    title info:
      title: AI-Powered Innovation in Materials Science
    name:
      name part: Jiang, Xue
    origin info:
      publisher: Newark : John Wiley & Sons, Incorporated,c2026
  identifier: 9783527855827
  identifier: 3527855823
  identifier: 3527356355
  identifier: 9783527356355
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 260328
    record change date: 20260507160914.1
    record identifier: in00005780974
    language of cataloging:
      language term: eng