1.8k citations · 3.6k across the 19 of their papers we have counts for
20 papers
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Zijiang Yang, Dipendra Jha, Arindam Paul +3
Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in s…
Building Halo Merger Trees from the Q Continuum Simulation
Esteban Rangel, Nicholas Frontiere, Salman Habib +4
Cosmological N-body simulations rank among the most computationally intensive efforts today. A key challenge is the analysis of structure, substructure, and the merger history for…
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
Arindam Paul, Mojtaba Mozaffar, Zijiang Yang +4
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM…
Improving MPI Collective I/O Performance With Intra-node Request Aggregation
Qiao Kang, Sunwoo Lee, Kai-yuan Hou +4
Two-phase I/O is a well-known strategy for implementing collective MPI-IO functions. It redistributes I/O requests among the calling processes into a form that minimizes the file a…
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Dipendra Jha, Logan Ward, Zijiang Yang +5
Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applyi…