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Related Concept Videos

Sustainable Development01:43

Sustainable Development

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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Sensory Modalities01:15

Sensory Modalities

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
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Design Consideration01:22

Design Consideration

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
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Design Example: Sustainability in Concrete Building01:26

Design Example: Sustainability in Concrete Building

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As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
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Updated: Jun 23, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Sustainable Materials Design With Multi-Modal Artificial Intelligence.

Tianyi Xu1,2, Tianshuo Wei1, Yan Ge3,4

  • 1Department of Data Science, City University of Hong Kong, Hong Kong, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

Multi-modal artificial intelligence (AI) accelerates sustainable materials design by integrating diverse data. This approach addresses critical mineral scarcity and environmental concerns, paving the way for efficient, eco-friendly material discovery.

Keywords:
AI‐driven material discoverymulti‐modal AIsustainable materials

Related Experiment Videos

Last Updated: Jun 23, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Sustainable Engineering

Background:

  • Growing scarcity of critical minerals necessitates sustainable materials.
  • Conventional methods struggle with complex, high-dimensional data for materials design.
  • Environmental concerns like carbon emissions and pollution from smelting demand new approaches.

Purpose of the Study:

  • To review advances in multi-modal artificial intelligence (AI) for sustainable materials design.
  • To explore AI's role in optimizing materials for performance, economics, and environmental impact.
  • To outline a roadmap for accelerating equitable and sustainable materials discovery.

Main Methods:

  • Synthesizing advances in multi-modal AI for materials science.
  • Integrating diverse data types (textual, visual, temporal) for AI-driven design.
  • Reviewing AI applications in alloy discovery, autonomous laboratories, and recyclable material design.

Main Results:

  • Multi-modal AI enables accelerated, data-driven materials design.
  • AI effectively identifies compositions and structures meeting functional and sustainability criteria.
  • Advances cover AI foundations, sustainable alloy discovery, autonomous labs, and recyclable design.

Conclusions:

  • Multi-modal AI offers a transformative pathway for sustainable materials discovery.
  • A roadmap coupling data infrastructure, human validation, and autonomous experimentation is proposed.
  • This integrated approach accelerates equitable and sustainable materials discovery at scale.