Towards accurate processing-structure-property links using deep learning

2022 ◽  
Vol 211 ◽  
pp. 114478
Author(s):  
Michiel Larmuseau ◽  
Koenraad Theuwissen ◽  
Kurt Lejaeghere ◽  
Lode Duprez ◽  
Tom Dhaene ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Property

scholarly journals Deep Learning Insights into Lanthanides Complexation Chemistry

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3237
Author(s):  
Artem A. Mitrofanov ◽  
Petr I. Matveev ◽  
Kristina V. Yakubova ◽  
Alexandru Korotcov ◽  
Boris Sattarov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stability Constants ◽  
Black Box ◽  
Lanthanide Ions ◽  
Structure Property ◽  
Target Property ◽  
Mutual Location ◽  
Molecule Structure ◽  
Complexation Chemistry ◽  
Main Influence

Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.

scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Deep learning based predictive modeling for structure-property linkages

Materialia ◽  
2019 ◽  
Vol 8 ◽  
pp. 100435
Author(s):  
Anuradha Beniwal ◽  
Ritesh Dadhich ◽  
Alankar Alankar
Keyword(s):  
Deep Learning ◽  
Predictive Modeling ◽  
Structure Property

scholarly journals Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhao Fan ◽  
Evan Ma
Keyword(s):  
Deep Learning ◽  
Mechanical Response ◽  
Materials Science ◽  
Learning Algorithm ◽  
Amorphous Solids ◽  
Valuable Insight ◽  
Structure Property ◽  
Three Dimensional Model ◽  
Static Structure ◽  
Structure Representation

AbstractIt has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses.

scholarly journals Deep learning to establish structure property relationships of impact copolymers from AFM phase images

2021 ◽  
Author(s):  
Dalia Yablon ◽  
Ishita Chakraborty ◽  
Hillary Passino ◽  
Krishnan Iyer ◽  
Antonios Doufas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Phase Images

scholarly journals Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

2018 ◽  
Vol 151 ◽  
pp. 278-287 ◽  
Author(s):  
Zijiang Yang ◽  
Yuksel C. Yabansu ◽  
Reda Al-Bahrani ◽  
Wei-keng Liao ◽  
Alok N. Choudhary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
High Contrast ◽  
Structure Property

Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

2019 ◽  
Vol 166 ◽  
pp. 335-345 ◽  
Author(s):  
Zijiang Yang ◽  
Yuksel C. Yabansu ◽  
Dipendra Jha ◽  
Wei-keng Liao ◽  
Alok N. Choudhary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Elastic Deformation ◽  
Three Dimensional ◽  
Learning Approaches ◽  
High Contrast ◽  
Structure Property
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