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Meeting MS&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Presentation Title Searching for New Ferroelectric Materials Browsing a High-throughput Phonon Database
Author(s) Maksim Markov, Louis Alaerts, Henrique Pereira Coutada Miranda, Guido Petretto, Wei Chen, Janine George, Eric Bousquet, Philippe Ghosez, Gian-Marco Rignanese, Geoffroy Hautier
On-Site Speaker (Planned) Geoffroy Hautier
Abstract Scope Ferroelectric materials are of great fundamental and applied interests with wide applications in many technologies such as electric capacitors, piezoelectric sensors, non-volatile memory devices, or energy converters. In this work, we search for new ferroelectrics by using high-throughput computing and a recently built database of more than 2,000 phonons. Browsing the phonon database, we identify materials exhibiting dynamically unstable polar phonon modes, a signature of a potential ferroelectric. We discuss the structure and chemistries emerging from high-throughput approaches and highlight the challenges in finding ferroelectric materials. We then focus on one new family of ferroelectric materials discovered through high-throughput screening: the anti-Ruddlesden-Popper phases of formula A4X2O with A: a +2 alkali-earth or rare-earth element and X: a −3 anion Bi, Sb, As and P. We show that significant ferroelectricity is present in Ba4Sb2O and demonstrate that Eu4Sb2O is a rare example of a material combining coupled ferroelectricity and ferromagnetism.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects
Accelerating Discovery in Computational Materials Science Using CAMD
Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics
Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
Considerations for Interpretability, Reliability, And Data-efficiency in Machine Learning Properties of Solid-state Materials
Data Science as Bridge – Materials Characterization and Modeling
Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
Designing Alloys with Process-mapping AI Pre-trained on Empirical Knowledge
Developing Physics-based Descriptors for Property Prediction in Oxide Glasses
Learning Synthesis: Engineering Metal Nanoclusters for Specific Material Properties
Machine Learning in 2D Materials: Benchmarking Crystal Graph Based Convolutional Neural Network (CGCNN) for Open Databases
Machine Learning to Predict Mechanical Properties of Steel Alloys Based on Chemical Composition and Heat Treatment Process
Materials Graph Ontology for Improving the Standardization and Utilization of Materials Data
Molecular Dynamics Simulation Using Lagrangian Neural Networks
Multi-target Prediction of Concrete Engineering Properties Based on a Single Deep Learning Model
P3-18: Rashba Spin Splitting and Photocatalytic Properties of GeC−MSSe (M=Mo, W) Van Der Waals Heterostructures
P3-19: Thermo-mechanical Property Prediction of High-temperature Materials Using a Python Based Interface With Quantum Espresso
Predicting Glass Behaviour from Optical Microscopy Images Using Interpretable Machine Learning
Scalable Gaussian Processes for Predicting the Optical, Physical, Thermal, and Mechanical Properties of Inorganic Glasses Using Compositions for Large Datasets
Searching for New Ferroelectric Materials Browsing a High-throughput Phonon Database
Semantic Segmentation of Plasma Transferred Arc Additively Manufactured NiBSi-WC Optical Microscopy Images Using a Convolutional Neural Network
Slip Band Characterization with Microtensile Testing Using Digital Image Processing
There is No Time for Science as Usual
Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression

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