Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385454PMC
http://dx.doi.org/10.1093/nsr/nwac111DOI Listing

Publication Analysis

Top Keywords

inverse design
8
design deep
4
deep generative
4
generative models
4
models step
4
step materials
4
materials discovery
4
discovery data-driven
4
data-driven inverse
4
design inorganic
4

Similar Publications

Multi-objective design of multi-material truss lattices utilizing graph neural networks.

Sci Rep

January 2025

Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, 8092, Zurich, Switzerland.

The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.

View Article and Find Full Text PDF

Photoassisted lithium-sulfur (Li-S) batteries offer a promising approach to enhance the catalytic transformation kinetics of polysulfide. However, the development is greatly hindered by inadequate photo absorption and severe photoexcited carriers recombination. Herein, a photonic crystal sulfide heterojunction structure is designed as a bifunctional electrode scaffold for photoassisted Li-S batteries.

View Article and Find Full Text PDF

Steam Generator Maintenance Robot Design and Obstacle Avoidance Path Planning.

Sensors (Basel)

January 2025

College of Resource Environmental and Safety Engineering, University of South China, Hengyang 421001, China.

To solve the issue of inconvenient and dangerous manual operation during the installation and removal of the main pipe plugging plate in the steam generator in nuclear power plants, a ten-degree-of-freedom plugging robot was designed in the present study that includes a collaborative robotic arm coupled with a servo electric cylinder. By establishing a joint coordinate system for the robot model, a D-H parameter model for the plate plugging robot was established, and the forward and inverse kinematics were solved. The volume level approximate convex decomposition algorithm was used to fit the steam generator model with a convex packet, and an experimental simulation platform was constructed.

View Article and Find Full Text PDF

Topology Design of Soft Phononic Crystals for Tunable Band Gaps: A Deep Learning Approach.

Materials (Basel)

January 2025

School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

The phononic crystals composed of soft materials have received extensive attention owing to the extraordinary behavior when undergoing large deformations, making it possible to provide tunable band gaps actively. However, the inverse designs of them mainly rely on the gradient-driven or gradient-free optimization schemes, which require sensitivity analysis or cause time-consuming, lacking intelligence and flexibility. To this end, a deep learning-based framework composed of a conditional variational autoencoder and multilayer perceptron is proposed to discover the mapping relation from the band gaps to the topology layout applied with prestress.

View Article and Find Full Text PDF

Circulating lipids and changes in lipid profiles have long been associated with the development of stroke but causal relationships remain unclear.In this study, we aimed to assess the causal relationships between lipid species and multiple stroke phenotypes to inform stroke prevention and treatment strategies. We conducted a two-sample Mendelian randomization analysis using data from genome-wide association studies.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!