Predicting mechanical fields near cracks using a progressive transformer diffusion model and exploration of generalization capacity

Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input

(2022) Yu-Chuan Hsu et al.   APL Materials

FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs

(2022) Markus J. Buehler   Materials Today

Data-Driven Strategies for Accelerated Materials Design

(2021) Robert Pollice et al.   ACCOUNTS OF CHEMICAL RESEARCH

Deep learning model to predict complex stress and strain fields in hierarchical composites

(2021) Zhenze Yang et al.   Science Advances

LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

(2021) Aidan P. Thompson et al.   COMPUTER PHYSICS COMMUNICATIONS

End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures

(2021) Zhenze Yang et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Machine learning-based microstructure prediction during laser sintering of alumina

(2021) Jianan Tang et al.   Scientific Reports

Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks

(2021) Zhenze Yang et al.   Frontiers in Materials

Mechanical MNIST: A benchmark dataset for mechanical metamodels

(2020) Emma Lejeune   Extreme Mechanics Letters

A concurrent multiscale study of dynamic fracture

(2020) Qi Tong et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

The machine learning revolution in materials?

(2019) Kristofer G. Reyes et al.   MRS BULLETIN

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Can machine learning find extraordinary materials?

(2019) Steven K. Kauwe et al.   COMPUTATIONAL MATERIALS SCIENCE

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Atomically Sharp Crack Tips in Monolayer MoS2 and Their Enhanced Toughness by Vacancy Defects

(2016) Shanshan Wang et al.   ACS Nano

Atomistic modeling at experimental strain rates and timescales

(2016) Xin Yan et al.   JOURNAL OF PHYSICS D-APPLIED PHYSICS

Fracture mechanics of monolayer molybdenum disulfide

(2015) Xiaonan Wang et al.   NANOTECHNOLOGY

Calculation and Visualization of Atomistic Mechanical Stresses in Nanomaterials and Biomolecules

(2014) Andrew T. Fenley et al.   PLoS One

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Category Theory Based Solution for the Building Block Replacement Problem in Materials Design

(2012) Tristan Giesa et al.   ADVANCED ENGINEERING MATERIALS

Hydration of Calcium Oxide Surface Predicted by Reactive Force Field Molecular Dynamics

(2012) Hegoi Manzano et al.   LANGMUIR

A Connection Between Score Matching and Denoising Autoencoders

(2011) Pascal Vincent   NEURAL COMPUTATION

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

(2011) David I. Spivak et al.   PLoS One

Interface structure and mechanics between graphene and metal substrates: a first-principles study

(2010) Zhiping Xu et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Parametrization of a reactive many-body potential for Mo–S systems

(2009) Tao Liang et al.   PHYSICAL REVIEW B