44 citations · 139 across the 20 of their papers we have counts for
20 papers
Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials
Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini +1
The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system's Hamiltonian to sample the phase space neede…
Atomic structure generation from reconstructing structural fingerprints
Victor Fung, Shuyi Jia, Jiaxin Zhang +3
Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help…
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks
Massimiliano Lupo Pasini, Junqi Yin
We propose a stable, parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs) under the constraint of a fixed computational budget. Diffe…
Stable Anderson Acceleration for Deep Learning
Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak +1
Anderson acceleration (AA) is an extrapolation technique designed to speed-up fixed-point iterations like those arising from the iterative training of DL models. Training DL models…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
Neural network based order parameter for phase transitions and its applications in high-entropy alloys
Junqi Yin, Zongrui Pei, Michael Gao
Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters…
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin +2
We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discri…
Distributed Training and Optimization Of Neural Networks
Jean-Roch Vlimant, Junqi Yin
Deep learning models are yielding increasingly better performances thanks to multiple factors. To be successful, model may have large number of parameters or complex architectures…
Data optimization for large batch distributed training of deep neural networks
Shubhankar Gahlot, Junqi Yin, Mallikarjun Shankar
Distributed training in deep learning (DL) is common practice as data and models grow. The current practice for distributed training of deep neural networks faces the challenges of…
Integrating Deep Learning in Domain Sciences at Exascale
Rick Archibald, Edmond Chow, Eduardo D'Azevedo +8
This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simu…
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach
Xianglin Liu, Jiaxin Zhang, Junqi Yin +3
High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is i…
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
Aymen Al Saadi, Dario Alfe, Yadu Babuji +33
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive…
Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening
Mark P. Oxley, Junqi Yin, Nikolay Borodinov +6
Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron mic…
Fast and stable deep-learning predictions of material properties for solid solution alloys
Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin +3
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The m…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
A scalable constructive algorithm for the optimization of neural network architectures
Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li +1
We propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to…
Robust data-driven approach for predicting the configurational energy of high entropy alloys
Jiaxin Zhang, Xianglin Liu, Sirui Bi +3
High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially charact…
Defining Big Data Analytics Benchmarks for Next Generation Supercomputers
Drew Schmidt, Junqi Yin, Michael Matheson +2
The design and construction of high performance computing (HPC) systems relies on exhaustive performance analysis and benchmarking. Traditionally this activity has been geared excl…
Square Lattice Gases with Two- and Three-body Interactions Revisited
Junqi Yin, D. P. Landau
Monte Carlo simulations have been used to study the phase diagrams for square Ising-lattice gas models with two-body and three-body interactions for values of interaction parameter…
Phase diagram and critical behavior of the square-lattice Ising model with competing nearest- and next-nearest-neighbor interactions
Junqi Yin, D. P. Landau
Using the parallel tempering algorithm and GPU accelerated techniques, we have performed large-scale Monte Carlo simulations of the Ising model on a square lattice with antiferroma…