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Machine learning and artificial neural network accelerated computational discoveries in materials science

https://doi.org/10.1002/wcms.1450
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Machine learning based interatomic potential for amorphous carbon
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inferred← citeshttps://doi.org/10.3389/fphy.2023.1125548openalex-snapshot
Efficient numerical simulation of atmospheric pulsed discharges by introducing deep learning
inferred← citeshttps://doi.org/10.3389/frsus.2023.1210209openalex-snapshot
Prospects of artificial intelligence in the development of sustainable separation processes
inferred← citeshttps://doi.org/10.3390/app14156590openalex-snapshot
Artificial Intelligence in Biomaterials: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/app15073920openalex-snapshot
AI-Powered Prediction of Dental Space Maintainer Needs Using X-Ray Imaging: A CNN-Based Approach for Pediatric Dentistry
inferred← citeshttps://doi.org/10.3390/app151810001openalex-snapshot
Machine Learning Approaches for FDM-Based 3D Printing: A Literature Review
inferred← citeshttps://doi.org/10.3390/app152111680openalex-snapshot
Dual Intelligent Prediction of Strength and Energy Absorption Performance of Rubber-Modified Concrete via Machine Learning and Metaheuristic Optimization Algorithms
inferred← citeshttps://doi.org/10.3390/catal11091129openalex-snapshot
Molecular Dynamics and Machine Learning in Catalysts
inferred← citeshttps://doi.org/10.3390/cryst11091039openalex-snapshot
Crystal-Site-Based Artificial Neural Networks for Material Classification
inferred← citeshttps://doi.org/10.3390/cryst12091324openalex-snapshot
Feature Fusion Deep Learning Model for Defects Prediction in Crystal Structures
inferred← citeshttps://doi.org/10.3390/engproc2023059021openalex-snapshot
Role of Computational Material Science in Improving the Properties of Piezoelectric Smart Materials: A Review
inferred← citeshttps://doi.org/10.3390/forecast6020016openalex-snapshot
Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization
inferred← citeshttps://doi.org/10.3390/ijms22031392openalex-snapshot
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction
inferred← citeshttps://doi.org/10.3390/ma17174176openalex-snapshot
Materials Properties Prediction (MAPP): Empowering the Prediction of Material Properties Solely Based on Chemical Formulas
inferred← citeshttps://doi.org/10.3390/math9222958openalex-snapshot
Modeling and Forecasting Cases of RSV Using Artificial Neural Networks
inferred← citeshttps://doi.org/10.3390/met11111832openalex-snapshot
Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering
inferred← citeshttps://doi.org/10.3390/molecules27113574openalex-snapshot
Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications
inferred← citeshttps://doi.org/10.3390/nano12172957openalex-snapshot
A Review of Performance Prediction Based on Machine Learning in Materials Science
inferred← citeshttps://doi.org/10.3390/physchem5010004openalex-snapshot
On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties
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AN APPROACH TOWARD PREDICTION OF SM-CO ALLOY’S MAXIMUM ENERGY PRODUCT USING FEATURE BAGGING TECHNIQUE
inferred← citeshttps://doi.org/10.3991/ijim.v19i09.54205openalex-snapshot
Development of a Mobile Application with Artificial Intelligence for Mexican Sign Language Recognition
inferred← citeshttps://doi.org/10.4018/979-8-3693-7974-5.ch014openalex-snapshot
Advancing Material Property Prediction With AI and Deep Learning Technologies
inferred← citeshttps://doi.org/10.46289/coen001openalex-snapshot
Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry.
inferred← citeshttps://doi.org/10.68159/e174870223openalex-snapshot
Conceptual Foundations for Value-Sensitive Design of Materials AI Systems
inferred← citeshttps://doi.org/10.7498/aps.71.20221555openalex-snapshot
Numerical study of discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning
inferred← citeshttps://doi.org/10.7498/aps.72.20221555openalex-snapshot
Numerical study on discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning
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