52 citations · 167 across the 9 of their papers we have counts for
9 papers
Efficient Materials Informatics between Rockets and Electrons
Adam M. Krajewski
The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously emb…
Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures
Adam M. Krajewski, Jonathan W. Siegel, Zi-Kui Liu
Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vect…
MaterialsMap: A CALPHAD-Based Tool to Design Composition Pathways through feasibility map for Desired Dissimilar Materials, demonstrated with RSW Joining of Ag-Al-Cu
Hui Sun, Bo Pan, Zhening Yang +6
Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase…
nimCSO: A Nim package for Compositional Space Optimization
Adam M. Krajewski, Arindam Debnath, Wesley F. Reinhart +2
nimCSO is a high-performance tool implementing several methods for selecting components (data dimensions) in compositional datasets, which optimize the data availability and densit…
Efficient Generation of Grids and Traversal Graphs in Compositional Spaces towards Exploration and Path Planning
Adam M. Krajewski, Allison M. Beese, Wesley F. Reinhart +1
Many disciplines of science and engineering deal with problems related to compositions, ranging from chemical compositions in materials science to portfolio compositions in economi…
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, Johan Bergsma, Andrius Merkys +56
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enh…
Comparing Forward and Inverse Design Paradigms: A Case Study on Refractory High-Entropy Alloys
Arindam Debnath, Lavanya Raman, Wenjie Li +7
The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates t…
Generative deep learning as a tool for inverse design of high-entropy refractory alloys
Arindam Debnath, Adam M. Krajewski, Hui Sun +9
Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages…
Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks
Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu +1
In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voron…