Machine learning for materials design and discovery

Lattice constant prediction of A2XY6 cubic crystals (A = K, Cs, Rb, TI; X = tetravalent cation; Y = F, Cl, Br, I) using computational intelligence approach

(2020) Ibrahim Olanrewaju Alade et al.   JOURNAL OF APPLIED PHYSICS

Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

(2020) James E. Saal et al.   Annual Review of Materials Research

Opportunities and Challenges for Machine Learning in Materials Science

(2020) Dane Morgan et al.   Annual Review of Materials Research

Machine Learning for Materials Scientists: An introductory guide towards best practices

(2020) Anthony Yu-Tung Wang et al.   CHEMISTRY OF MATERIALS

Deep learning in heterogeneous materials: Targeting the thermo-mechanical response of unidirectional composites

(2020) Qiang Chen et al.   JOURNAL OF APPLIED PHYSICS

Computer-aided detection and morphological characterization of nanotube layers using scanning electron microscopy images

(2020) Adrian Ciobanu et al.   JOURNAL OF APPLIED PHYSICS

Extreme learning machine and support vector regression wear loss predictions for magnesium alloys coated using various spray coating methods

(2020) Turan Gurgenc et al.   JOURNAL OF APPLIED PHYSICS

A machine learning-based model to estimate the density of nanofluids of nitrides in ethylene glycol

(2020) Mirza Sahaluddin et al.   JOURNAL OF APPLIED PHYSICS

Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds

(2020) Claudia Mangold et al.   JOURNAL OF APPLIED PHYSICS

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

(2020) Jorge Arturo Hernandez Zeledon et al.   JOURNAL OF APPLIED PHYSICS

Role of uncertainty estimation in accelerating materials development via active learning

(2020) Yuan Tian et al.   JOURNAL OF APPLIED PHYSICS

Machine learning 5d-level centroid shift of Ce3+ inorganic phosphors

(2020) Ya Zhuo et al.   JOURNAL OF APPLIED PHYSICS

Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework

(2020) D. P. Santos et al.   JOURNAL OF APPLIED PHYSICS

Refractive index prediction models for polymers using machine learning

(2020) Jordan P. Lightstone et al.   JOURNAL OF APPLIED PHYSICS

Feature engineering of material structure for AI-based materials knowledge systems

(2020) Surya R. Kalidindi   JOURNAL OF APPLIED PHYSICS

Machine learning substitutional defect formation energies in ABO3 perovskites

(2020) Vinit Sharma et al.   JOURNAL OF APPLIED PHYSICS

Classification of platinum nanoparticle catalysts using machine learning

(2020) A. J. Parker et al.   JOURNAL OF APPLIED PHYSICS

A machine learning based approach for phononic crystal property discovery

(2020) Seid M. Sadat et al.   JOURNAL OF APPLIED PHYSICS

Spectral neural network potentials for binary alloys

(2020) David Zagaceta et al.   JOURNAL OF APPLIED PHYSICS

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

(2020) Maxim Ziatdinov et al.   JOURNAL OF APPLIED PHYSICS

Small data materials design with machine learning: When the average model knows best

(2020) Danny E. P. Vanpoucke et al.   JOURNAL OF APPLIED PHYSICS

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

(2020) Rama K. Vasudevan et al.   JOURNAL OF APPLIED PHYSICS

Adaptive machine learning for efficient materials design

(2020) Prasanna V. Balachandran   MRS BULLETIN

Vibrational detection of delamination in composites using a combined finite element analysis and machine learning approach

(2020) Eric W. Jacobs et al.   JOURNAL OF APPLIED PHYSICS

Machine learning formation enthalpies of intermetallics

(2020) Zhaohan Zhang et al.   JOURNAL OF APPLIED PHYSICS

Optimization of depth-graded multilayer structure for x-ray optics using machine learning

(2020) Sae Dieb et al.   JOURNAL OF APPLIED PHYSICS

Augmenting machine learning of energy landscapes with local structural information

(2020) Shreyas J. Honrao et al.   JOURNAL OF APPLIED PHYSICS

Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression

(2020) Ibrahim Olanrewaju Alade et al.   JOURNAL OF APPLIED PHYSICS

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

(2020) W. Ma et al.   JOURNAL OF APPLIED PHYSICS

Application of machine learning methods for predicting new superhard materials

(2020) Efim Mazhnik et al.   JOURNAL OF APPLIED PHYSICS

Practicing deep learning in materials science: An evaluation for predicting the formation energies

(2020) Liyuan Huang et al.   JOURNAL OF APPLIED PHYSICS

Inverse design of acoustic metamaterials based on machine learning using a Gauss–Bayesian model

(2020) Bin Zheng et al.   JOURNAL OF APPLIED PHYSICS

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

(2020) Callum J. Court et al.   Journal of Chemical Information and Modeling

Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films

(2020) Anna Costine et al.   JOURNAL OF APPLIED PHYSICS

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

(2019) Turab Lookman et al.   npj Computational Materials

Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy

(2019) Nikolay Borodinov et al.   npj Computational Materials

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data

(2019) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

Deep materials informatics: Applications of deep learning in materials science

(2019) Ankit Agrawal et al.   MRS Communications

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

(2019) Jonathan Schmidt et al.   npj Computational Materials

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Uncovering electron scattering mechanisms in NiFeCoCrMn derived concentrated solid solution and high entropy alloys

(2018) Sai Mu et al.   npj Computational Materials

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

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

(2013) Anubhav Jain et al.   APL Materials