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De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
Nature has severely outpaced humans in developing multifunctional, hierarchical materials that access impressive material properties, …
Rachel K. Luu, Sofia Arevalo, Wei Lu, Bo Ni, Zhenze Yang, Sabrina C. Shen, Jaime Berkovich, Yu-Chuan Hsu, Stone Zan, Markus J. Buehler
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De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
Solid polymer electrolytes hold significant promise as materials for next-generation batteries due to their superior safety …
Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh
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De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
In this work, we introduce a polymer discovery platform designed to identify polymers with tailored properties efficiently, exemplified …
Xiangyun Lei, Weike Ye, Zhenze Yang, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh
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A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information
Solving materials engineering tasks is often hindered by limited information, such as in inverse problems with only boundary data …
Zhenze Yang, Markus J. Buehler
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Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information
Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model
We report a method to generate de novo protein designs through a generative adversarial neural network, MolShapeGAN, that can rapidly …
Zhenze Yang, Yu-Chuan Hsu, Markus J. Buehler
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Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model
Fracture at the two-dimensional limit
More than a century ago, A.A. Griffith published the seminal paper establishing the foundational framework for fracture mechanics. The …
Bo Ni, Doug Steinbach, Zhenze Yang, Andrew Lew, Boyu Zhang, Qiyi Fang, Markus J. Buehler, Jun Lou
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Fracture at the two-dimensional limit
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage …
Zhenze Yang, Markus J. Buehler
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Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning
A new method using reinforcement learning for designing bioinspired composite materials is proposed. While bioinspired design of …
Chi-Hua Yu, Bor-Yann Tseng, Zhenze Yang, Cheng-Che Tung, Elena Zhao, Zhi-Fan Ren, Sheng-Sheng Yu, Po-Yu Chen, Chuin-Shan Chen, Markus J. Buehler
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Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning
Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks
Spider webs feature advanced structural performance due to the evolutionary success of over more than 3 × 109 years, including …
Wei Lu, Zhenze Yang, Markus J. Buehler
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Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks
High-Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning
3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent …
Zhenze Yang, Markus J. Buehler
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High-Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning