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Machine Learning for Materials Design and Discovery

This Special Topic aims to provide a valuable and timely forum where scientists and practitioners in the field of materials informatics share their most recent findings and disruptive ideas, reveal new advances to improve the fundamental understanding of machine learning approaches for accelerated materials design and discovery, and identify challenges as well as the next critical steps going forward. Journal of Applied Physics has interest to expand in this area and aims to be the home for original and timely research reports on this important topic.

Guest Editors: Rama Vasudevan, Ghanshyam Pilania, and Prasanna V. Balachandran

EDITORIALS

Machine learning for materials design and discovery

Rama Vasudevan; Ghanshyam Pilania; Prasanna V. Balachandran

https://doi.org/10.1063/5.0043300

Soft Matter, Fluids, and Biophysics

Bond order predictions using deep neural networks

Sergey Magedov; Christopher Koh; Walter Malone; Nicholas Lubbers; Benjamin Nebgen

https://doi.org/10.1063/5.0016011

Thin Films, Interfaces, and Surfaces

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

Anna Costine; Paige Delsa; Tianxi Li; Petra Reinke; Prasanna V. Balachandran

https://doi.org/10.1063/5.0017507

Photonics, Plasmonics, Photovoltaics, Lasers, Optical Materials and Phenomena

Predictive modeling approaches in laser-based material processing

Maria-Christina Velli; George D. Tsibidis; Alexandros Mimidis; Evangelos Skoulas; Yannis Pantazis; Emmanuel Stratakis

https://doi.org/10.1063/5.0018235

Emerging, Interdisciplinary, and Other Fields of Applied Physics

Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging

Alexander Scheinker; Reeju Pokharel

https://doi.org/10.1063/5.0014725

Emerging, Interdisciplinary, and Other Fields of Applied Physics

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

Bin Zheng; Jing Yang; Bin Liang; Jian-chun Cheng

https://doi.org/10.1063/5.0012392

Emerging, Interdisciplinary, and Other Fields of Applied Physics

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

W. Ma; E. J. Kautz; A. Baskaran; A. Chowdhury; V. Joshi; B. Yener; D. J. Lewis

https://doi.org/10.1063/5.0013720

Emerging, Interdisciplinary, and Other Fields of Applied Physics

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

Liyuan Huang; Chen Ling

https://doi.org/10.1063/5.0012411

Physics of Materials, Including Electrical, Thermal, Mechanical and Other Properties

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

Eric W. Jacobs; Charles Yang; Kahraman G. Demir; Grace X. Gu

https://doi.org/10.1063/5.0015648

Physics of Materials, Including Electrical, Thermal, Mechanical and Other Properties

Machine learning formation enthalpies of intermetallics

Zhaohan Zhang; Mu Li; Katharine Flores; Rohan Mishra

https://doi.org/10.1063/5.0012323

Thin Films, Interfaces, and Surfaces

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

Ibrahim Olanrewaju Alade; Mohd Amiruddin Abd Rahman; Amjed Hassan; Tawfik A. Saleh

https://doi.org/10.1063/5.0008977

Physics of Materials, Including Electrical, Thermal, Mechanical and Other Properties

Augmenting machine learning of energy landscapes with local structural information

Shreyas J. Honrao; Stephen R. Xie; Richard G. Hennig

https://doi.org/10.1063/5.0012407

Emerging, Interdisciplinary, and Other Fields of Applied Physics

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

Sae Dieb; Zhilong Song; Wan-Jian Yin; Masashi Ishii

https://doi.org/10.1063/5.0012351

Physics of Materials, Including Electrical, Thermal, Mechanical and Other Properties

Application of machine learning methods for predicting new superhard materials

Efim Mazhnik; Artem R. Oganov

https://doi.org/10.1063/5.0012055

Dielectrics, Ferroelectrics, and Multiferroics

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

Rama K. Vasudevan; Kyle P. Kelley; Eugene Eliseev; Stephen Jesse; Hiroshi Funakubo; Anna Morozovska; Sergei V. Kalinin

https://doi.org/10.1063/5.0005323

Emerging, Interdisciplinary, and Other Fields of Applied Physics

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

Danny E. P. Vanpoucke; Onno S. J. van Knippenberg; Ko Hermans; Katrien V. Bernaerts; Siamak Mehrkanoon

https://doi.org/10.1063/5.0012285

Physics of Materials, Including Electrical, Thermal, Mechanical and Other Properties

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

Maxim Ziatdinov; Dohyung Kim; Sabine Neumayer; Liam Collins; Mahshid Ahmadi; Rama K. Vasudevan; Stephen Jesse; Myung Hyun Ann; Jong H. Kim; Sergei V. Kalinin

https://doi.org/10.1063/5.0013847

TUTORIALS