logo
ResearchBunny Logo
Machine learning-based discovery of vibrationally stable materials

Engineering and Technology

Machine learning-based discovery of vibrationally stable materials

S. A. Tawfik, M. Rashid, et al.

This paper presents a groundbreaking machine learning approach developed by Sherif Abdulkader Tawfik and colleagues to predict the vibrational stability of materials, a key aspect in material synthesizability. The classifier they created acts as a swift alternative to traditional computational methods, promising to enhance material database searches significantly.

00:00
00:00
Playback language: English
Abstract
This paper introduces a machine learning approach to predict the vibrational stability of materials, a crucial factor in material synthesizability. Using a dataset of approximately 3100 materials, the authors trained a classifier to distinguish between vibrationally stable and unstable materials. This classifier offers a significantly faster alternative to computationally expensive first-principles calculations, potentially serving as a valuable filtering tool for online material databases.
Publisher
npj Computational Materials
Published On
Jan 11, 2023
Authors
Sherif Abdulkader Tawfik, Mahad Rashid, Sunil Gupta, Salvy P. Russo, Tiffany R. Walsh, Svetha Venkatesh
Tags
machine learning
vibrational stability
materials
classifier
synthesizability
computational methods

Related Publications

Explore these studies to deepen your understanding of the subject.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
Medicine and Health

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

P. Amiri, M. Montazeri, et al.

Machine learning-based prediction of COVID-19 diagnosis based on symptoms
Medicine and Health

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Y. Zoabi, S. Deri-rozov, et al.

Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
Engineering and Technology

Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries

K. Li, J. Wang, et al.

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning
Engineering and Technology

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

C. S. Ha, D. Yao, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny