Deep learning framework for material design space exploration using active transfer learning and data augmentation

Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy

(2021) Qi-Jun Hong et al.   npj Computational Materials

Data-Driven Extrapolation Via Feature Augmentation Based on Variably Scaled Thin Plate Splines

(2021) Rosanna Campagna et al.   JOURNAL OF SCIENTIFIC COMPUTING

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

(2020) Chun‐Teh Chen et al.   Advanced Science

Inverse design of porous materials using artificial neural networks

(2020) Baekjun Kim et al.   Science Advances

Designing an Adhesive Pillar Shape with Deep Learning-Based Optimization

(2020) Yongtae Kim et al.   ACS Applied Materials & Interfaces

A machine learning approach to fracture mechanics problems

(2020) Xing Liu et al.   ACTA MATERIALIA

Deep learning for plasticity and thermo-viscoplasticity

(2020) Diab W. Abueidda et al.   INTERNATIONAL JOURNAL OF PLASTICITY

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

(2019) Turab Lookman et al.   npj Computational Materials

A three-dimensional fracture pattern diagram of staggered platelet structures

(2019) Youngsoo Kim et al.   COMPOSITE STRUCTURES

Designing tough isotropic structural composite using computation, 3D printing and testing

(2019) Yongtae Kim et al.   COMPOSITES PART B-ENGINEERING

Graded-material design based on phase-field and topology optimization

(2019) Massimo Carraturo et al.   COMPUTATIONAL MECHANICS

Predicting superhard materials via a machine learning informed evolutionary structure search

(2019) Patrick Avery et al.   npj Computational Materials

The optimization via response surface method for micro hydrogen gas actuator

(2019) Zhixiong Li et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning

(2019) Sirawit Pruksawan et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

(2019) Zichao Long et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep learning predicts path-dependent plasticity

(2019) M. Mozaffar et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

(2018) Jon Paul Janet et al.   Journal of Physical Chemistry Letters

Machine learning directed search for ultraincompressible, superhard materials

(2018) Aria Mansouri Tehrani et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Molecular generative model based on conditional variational autoencoder for de novo molecular design

(2018) Jaechang Lim et al.   Journal of Cheminformatics

Nanophotonic particle simulation and inverse design using artificial neural networks

(2018) John Peurifoy et al.   Science Advances

Phase field modeling of crack propagation under combined shear and tensile loading with hybrid formulation

(2018) Heeyeong Jeong et al.   COMPUTATIONAL MATERIALS SCIENCE

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

Double-slit photoelectron interference in strong-field ionization of the neon dimer

(2018) Maksim Kunitski et al.   Nature Communications

Deep-learning-based inverse design model for intelligent discovery of organic molecules

(2018) Kyungdoc Kim et al.   npj Computational Materials

Novel phase diagram behavior and materials design in heterostructural semiconductor alloys

(2017) Aaron M. Holder et al.   Science Advances

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Bone-Inspired Materials by Design: Toughness Amplification Observed Using 3D Printing and Testing 

(2016) Flavia Libonati et al.   ADVANCED ENGINEERING MATERIALS

Topology Optimization for Architected Materials Design

(2016) Mikhail Osanov et al.   Annual Review of Materials Research

Discovery of pyridine-based agrochemicals by using Intermediate Derivatization Methods

(2016) Ai-Ying Guan et al.   BIOORGANIC & MEDICINAL CHEMISTRY

Machine-learning approaches in drug discovery: methods and applications

(2015) Antonio Lavecchia   DRUG DISCOVERY TODAY

Topological design of microstructures of multi-phase materials for maximum stiffness or thermal conductivity

(2014) A. Radman et al.   COMPUTATIONAL MATERIALS SCIENCE

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Prediction-based regularization using data augmented regression

(2010) Giles Hooker et al.   STATISTICS AND COMPUTING