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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning
Author(s) Anus Manzoor, Dilpuneet S. Aidhy
On-Site Speaker (Planned) Anus Manzoor
Abstract Scope Despite a well-recognized contribution of vibrational entropy (Svib) in the phase stability of alloys, it remains a peripheral quantity due to its high computational cost. In this work, using a combination of density functional theory (DFT) calculations and machine learning (ML), we show that the expensive Svib computations can be completely circumvented. This is possible because there exists a unique force constant (FC) – bond length relationship for every A-A and A-B bond and the influence of the alloy composition on FCs can be captured with the change in bond lengths only. The DFT database coupled with ML model allows to predict FCs between any two elements which in turn enables predicting Svib of any complex alloy thereby significantly reducing the computational costs. This work opens a new avenue to predict Svib of complex HEAs thereby making Svib as readily available as the mixing enthalpy.
Proceedings Inclusion? Planned:
Keywords Machine Learning, High-Entropy Alloys, Computational Materials Science & Engineering

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS) - A Case Study of the Oxidation Kinetics of NiCr-based Alloys
Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys
Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys
Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties
Domain Knowledge-informed, Process-mapping AI Graph for Designing Fe-based Alloys
Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy
Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection
Exploring the Compositional Space of High Entropy Alloys via Sequential Learning
Knowledge-driven Platform for Federated Multimodal Big Data Storage & Analytics
Machine Learning Augmented Predictive & Generative Models for Rupture Life in High Temperature Alloys
Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys
Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning
Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning
Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning
Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)
Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures
Uncertainty Reduction for Calculated Phase Equilibria

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