Bayesian optimization with adaptive surrogate models for automated experimental design
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
A perspective on Bayesian methods applied to materials discovery and design
For more than two decades, there has been increasing interest in developing frameworks for
the accelerated discovery and design of novel materials that could enable promising and …
the accelerated discovery and design of novel materials that could enable promising and …
Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys
Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and
exploit materials design spaces. To date, most BO approaches to materials design have …
exploit materials design spaces. To date, most BO approaches to materials design have …
Bayesian optimization with active learning of design constraints using an entropy-based approach
The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
Autonomous materials discovery and manufacturing (AMDM): A review and perspectives
STS Bukkapatnam - IISE Transactions, 2023 - Taylor & Francis
This article presents an overview of the emerging themes in Autonomous Materials
Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing …
Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing …
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys
D Khatamsaz, R Neuberger, AM Roy… - npj Computational …, 2023 - nature.com
The design of materials and identification of optimal processing parameters constitute a
complex and challenging task, necessitating efficient utilization of available data. Bayesian …
complex and challenging task, necessitating efficient utilization of available data. Bayesian …
Bayesian analysis of parametric uncertainties and model form probabilities for two different crystal plasticity models of lamellar grains in α+ β titanium alloys
The properties of individual phases and complex interactions of phases that affect
mechanical behavior of metastable lamellar and Widmanstaetten morphologies of α+ β …
mechanical behavior of metastable lamellar and Widmanstaetten morphologies of α+ β …
Bayesian optimization objective-based experimental design
M Imani, SF Ghoreishi - 2020 American control conference …, 2020 - ieeexplore.ieee.org
Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …
a wide range of domains including real experimental settings (eg, material discovery or drug …
Adaptive active subspace-based efficient multifidelity materials design
D Khatamsaz, A Molkeri, R Couperthwaite, J James… - Materials & Design, 2021 - Elsevier
Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …
property (PSP) relationships to produce materials with targeted properties. Recently, we …
On the importance of microstructure information in materials design: PSP vs PP
A Molkeri, D Khatamsaz, R Couperthwaite, J James… - Acta Materialia, 2022 - Elsevier
The focus of goal-oriented materials design is to find the necessary chemistry/processing
conditions to achieve the desired properties. In this setting, a material's microstructure is …
conditions to achieve the desired properties. In this setting, a material's microstructure is …