Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning

Machine learning for composite materials

(2019) Chun-Teh Chen et al.   MRS Communications

Using convolutional neural networks to predict composite properties beyond the elastic limit

(2019) Charles Yang et al.   MRS Communications

Predicting interfacial thermal resistance by machine learning

(2019) Yen-Ju Wu et al.   npj Computational Materials

A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence

(2019) Chi-Hua Yu et al.   ACS Nano

Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill

(2018) Chongchong Qi et al.   CONSTRUCTION AND BUILDING MATERIALS

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

Nanophotonic particle simulation and inverse design using artificial neural networks

(2018) John Peurifoy et al.   Science Advances

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

(2018) Grace X. Gu et al.   Materials Horizons

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning

(2018) Paul Z. Hanakata et al.   PHYSICAL REVIEW LETTERS

Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

(2018) Zijiang Yang et al.   ACTA MATERIALIA

Giga-voxel computational morphogenesis for structural design

(2017) Niels Aage et al.   NATURE

Evaluating the Visualization of What a Deep Neural Network Has Learned

(2017) Wojciech Samek et al.   IEEE Transactions on Neural Networks and Learning Systems

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Materials science with large-scale data and informatics: Unlocking new opportunities

(2016) Joanne Hill et al.   MRS BULLETIN

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Materials Informatics: The Materials “Gene” and Big Data

(2015) Krishna Rajan   Annual Review of Materials Research

Topology Optimization in Aircraft and Aerospace Structures Design

(2015) Ji-Hong Zhu et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

Machine-learning approaches in drug discovery: methods and applications

(2015) Antonio Lavecchia   DRUG DISCOVERY TODAY

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Deep learning

(2015) Yann LeCun et al.   NATURE

Bridging topology optimization and additive manufacturing

(2015) Tomás Zegard et al.   STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS