Machine learning for condensed matter physics

E Bedolla, LC Padierna… - Journal of Physics …, 2020 - iopscience.iop.org
Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …

Digitalization of the agro-food sector for achieving sustainable development goals: a review

A Sridhar, M Ponnuchamy, PS Kumar… - Sustainable Food …, 2023 - pubs.rsc.org
Food security and agricultural sustainability are essential for an equitable and healthy
society. Increasing food demand, growing inequalities, climate change and pandemics are …

Clustering by fast search and find of density peaks

A Rodriguez, A Laio - science, 2014 - science.org
Cluster analysis is aimed at classifying elements into categories on the basis of their
similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern …

SOD‐YOLO: a small target defect detection algorithm for wind turbine blades based on improved YOLOv5

R Zhang, C Wen - Advanced Theory and Simulations, 2022 - Wiley Online Library
Early and effective detection of wind turbine blade (WTB) surface defects can avoid complex
and expensive repair problems and serious safety hazards. The traditional target detection …

iPhos‐PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory

WR Qiu, BQ Sun, X Xiao, D Xu… - Molecular …, 2017 - Wiley Online Library
Protein phosphorylation plays a critical role in human body by altering the structural
conformation of a protein, causing it to become activated/deactivated, or functional …

[PDF][PDF] A survey on unsupervised machine learning algorithms for automation, classification and maintenance

M Khanum, T Mahboob, W Imtiaz, HA Ghafoor… - International Journal of …, 2015 - Citeseer
The paper is comprehensive survey of methodologies and techniques used for
Unsupervised Machine Learning that are used for learn complex, highly non-linear models …

Systems metabolic engineering meets machine learning: a new era for data‐driven metabolic engineering

KV Presnell, HS Alper - Biotechnology journal, 2019 - Wiley Online Library
The recent increase in high‐throughput capacity of 'omics datasets combined with advances
and interest in machine learning (ML) have created great opportunities for systems …

A transfer learning approach for improved classification of carbon nanomaterials from TEM images

Q Luo, EA Holm, C Wang - Nanoscale Advances, 2021 - pubs.rsc.org
The extensive use of carbon nanomaterials such as carbon nanotubes/nanofibers
(CNTs/CNFs) in industrial settings has raised concerns over the potential health risks …

Using large datasets to understand nanotechnology

K Paunovska, D Loughrey, CD Sago… - Advanced …, 2019 - Wiley Online Library
Advances in sequencing technologies have made studying biological processes with
genomics, transcriptomics, and proteomics commonplace. As a result, this suite of …

Thickness determination of ultrathin 2D materials empowered by machine learning algorithms

Y Mao, L Wang, C Chen, Z Yang… - Laser & Photonics …, 2023 - Wiley Online Library
The number of layers of 2D materials is of great significance for regulating the performance
of nanoelectronic devices and optoelectronic devices, where the optimal thickness of the …