Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
Hierarchical materials from high information content macromolecular building blocks: construction, dynamic interventions, and prediction
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …
Because hierarchy gives rise to unique properties and functions, many have sought …
Machine-learning interatomic potentials for materials science
Y Mishin - Acta Materialia, 2021 - Elsevier
Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
Machine learning for materials scientists: an introductory guide toward best practices
This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …
machine learning-centered research. We cover broad guidelines and best practices …
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
Materials informatics employs machine learning (ML) models to map the relationship
between a targeted property and various materials descriptors, providing new avenues to …
between a targeted property and various materials descriptors, providing new avenues to …
Modeling solid solution strengthening in high entropy alloys using machine learning
Solid solution strengthening (SSS) influences the exceptional mechanical properties of
single-phase high entropy alloys (HEAs). Thus, given the vast compositional space …
single-phase high entropy alloys (HEAs). Thus, given the vast compositional space …
Text-mined dataset of inorganic materials synthesis recipes
Materials discovery has become significantly facilitated and accelerated by high-throughput
ab-initio computations. This ability to rapidly design interesting novel compounds has …
ab-initio computations. This ability to rapidly design interesting novel compounds has …
Machine learning assisted composition effective design for precipitation strengthened copper alloys
Optimizing the composition and improving the conflicting mechanical and electrical
properties of multiple complex alloys has always been difficult by traditional trial-and-error …
properties of multiple complex alloys has always been difficult by traditional trial-and-error …
Machine learning in polymer informatics
W Sha, Y Li, S Tang, J Tian, Y Zhao, Y Guo, W Zhang… - InfoMat, 2021 - Wiley Online Library
Polymers have been widely used in energy storage, construction, medicine, aerospace, and
so on. However, the complexity of chemical composition and morphology of polymers has …
so on. However, the complexity of chemical composition and morphology of polymers has …
Deep learning analysis on microscopic imaging in materials science
Microscopic imaging providing the real-space information of matter, plays an important role
for understanding the correlations between structure and properties in the field of materials …
for understanding the correlations between structure and properties in the field of materials …