AI-driven materials design: a mini-review

• URL: http://arxiv.org/abs/2502.02905v1
• Date: Wed, 05 Feb 2025 05:59:15 GMT
• Title: AI-driven materials design: a mini-review
• Authors: Mouyang Cheng, Chu-Liang Fu, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nguyen Tuan Hung, Mingda Li,
• Abstract summary: We summarize key computational advancements for materials design over the past few decades. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials.
• Score: 1.2773749417703923
• License:
• Abstract: Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance.

Related papers

• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs) We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data [42.45821602529994]
  Computational modeling and machine learning methods are employed for the design of materials. Physical mechanisms, cost of first-principles calculations, and the dispersity of data pose challenges to both physics-based and data-driven materials modeling. We propose a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design.
  arXiv  Detail & Related papers  (2024-12-23T05:06:19Z)
• AI-driven inverse design of materials: Past, present and future [5.813167950821478]
  Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers.
  arXiv  Detail & Related papers  (2024-11-14T13:25:04Z)
• Generative AI Models for Different Steps in Architectural Design: A Literature Review [14.910709576423576]
  It is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present.
  arXiv  Detail & Related papers  (2024-03-30T13:25:11Z)
• Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
  This survey offers a comprehensive overview of learning-based methods in computer-aided design. It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
  arXiv  Detail & Related papers  (2024-02-27T17:11:35Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Review of Large Vision Models and Visual Prompt Engineering [50.63394642549947]
  Review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models.
  arXiv  Detail & Related papers  (2023-07-03T08:48:49Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions [0.0]
  We review advances in AI-driven materials-by-design and their applications to energetic materials. We evaluate methods in the literature in terms of their capacity to learn from a small/limited number of data. We suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning.
  arXiv  Detail & Related papers  (2022-11-15T14:41:11Z)
• Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering [0.0]
  High-performance computing has made it possible to test deep learning (DL) models with significant parameters. generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials. The use of AI to analyze the results from existing analytical instruments is also discussed.
  arXiv  Detail & Related papers  (2022-09-15T04:21:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.