Deep Reinforcement Learning for Digital Materials Design

[1]  Jonathan A. Fan,et al.  Global optimization of dielectric metasurfaces using a physics-driven neural network , 2019, Nano letters.

[2]  Demis Hassabis,et al.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.

[3]  Lin Cheng,et al.  Coupling lattice structure topology optimization with design-dependent feature evolution for additive manufactured heat conduction design , 2018 .

[4]  O. Sigmund Materials with prescribed constitutive parameters: An inverse homogenization problem , 1994 .

[5]  Xin-Lin Gao,et al.  Evaluation of effective elastic properties of 3D printable interpenetrating phase composites using the meshfree radial point interpolation method , 2018 .

[6]  Timon Rabczuk,et al.  A multi-material level set-based topology optimization of flexoelectric composites , 2018, 1901.10752.

[7]  Xiaodong Li,et al.  In situ real time defect detection of 3D printed parts , 2017 .

[8]  Thomas J. Wallin,et al.  Machine learning generative models for automatic design of multi-material 3D printed composite solids , 2020 .

[9]  R. Bellman A Markovian Decision Process , 1957 .

[10]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[11]  Anders Logg,et al.  Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book , 2012 .

[12]  Kahraman G. Demir,et al.  Machine Learning for Advanced Additive Manufacturing , 2020, Matter.

[13]  Xuanhe Zhao,et al.  Designing complex architectured materials with generative adversarial networks , 2020, Science Advances.

[14]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[15]  David Sell,et al.  Large-Angle, Multifunctional Metagratings Based on Freeform Multimode Geometries. , 2017, Nano letters.

[16]  Yanyu Chen,et al.  Harnessing out-of-plane deformation to design 3D architected lattice metamaterials with tunable Poisson’s ratio , 2017, Scientific Reports.

[17]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[18]  Xu Zhang,et al.  Machine learning: Accelerating materials development for energy storage and conversion , 2020, InfoMat.

[19]  Michael Schukat,et al.  Deep Reinforcement Learning: An Overview , 2016, IntelliSys.

[20]  S. Dai,et al.  Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning , 2019, ACS Materials Letters.

[21]  Grace X. Gu,et al.  Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning , 2020, Advanced science.

[22]  Grace X. Gu,et al.  Physics-informed deep learning for digital materials , 2021 .

[23]  Yong Huang,et al.  Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations , 2015 .

[24]  K. Tai,et al.  Structural topology design optimization using Genetic Algorithms with a bit-array representation , 2005 .

[25]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[26]  Markus J. Buehler,et al.  De novo composite design based on machine learning algorithm , 2018 .

[27]  Hod Lipson,et al.  Design and analysis of digital materials for physical 3D voxel printing , 2009 .

[28]  Xingyi Huang,et al.  Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods , 2019, Composites Science and Technology.

[29]  Olexandr Isayev,et al.  Deep reinforcement learning for de novo drug design , 2017, Science Advances.

[30]  Ruiqi Guo,et al.  Accelerating Mems Design Process Through Machine Learning from Pixelated Binary Images , 2021, 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS).

[31]  Jiaqi Jiang,et al.  Deep neural networks for the evaluation and design of photonic devices , 2020, Nature Reviews Materials.

[32]  Wei Chen,et al.  Highly Efficient Light-Trapping Structure Design Inspired By Natural Evolution , 2013, Scientific Reports.

[33]  Zhizhou Zhang,et al.  Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials , 2020, Advanced Theory and Simulations.

[34]  Markus J. Buehler,et al.  Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment , 2018 .

[35]  An Chen,et al.  A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts , 2020 .