Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation
Abstract
Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the experimental synthesis of materials. Rapid progress has been made in the integration of increasing database and improved computing power. Though some reviews present the development from their unique aspects, reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few. Here, the newest development is systematically reviewed in the field of AI empowered materials, reflecting advanced design of the intelligent systems for discovery, synthesis, prediction and validation of materials. First, background and mechanisms are briefed, after which the design for the AI systems with data, machine learning and automated laboratory included is illustrated. Next, strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials, and then, the design thought for future AI systems in material science is pointed out. Finally, some perspectives are put forward.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.