86 citations · 174 across the 29 of their papers we have counts for
7 papers · 1 filter
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Sihoon Choi, Xiaohan Yu +6
New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly fr…
Kohn-Sham accuracy from orbital-free density functional theory via -machine learning
Shashikant Kumar, Xin Jing, John E. Pask +2
We present a -machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned…
Model-based design of temporal analysis of products (TAP) reactors: A simulated case study in oxidative propane dehydrogenation
Adam C. Yonge, Gabriel S. Gusmão, Rebecca Fushimi +1
Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity…
Self-consistent convolutional density functional approximations: Application to adsorption at metal surfaces
Sushree Jagriti Sahoo, Qimen Xu, Xiangyun Lei +5
The exchange-correlation (XC) functional in density functional theory is used to approximate multi-electron interactions. A plethora of different functionals is available, but near…
The phase stability of large-size nanoparticle alloy catalysts at ab initio quality using a nearsighted force-training approach
Cheng Zeng, Sushree Jagriti Sahoo, Andrew J. Medford +1
CoPt nanoparticle catalysts are integral to commercial fuel cells. Such systems are prohibitive to fully characterize with electronic structure calculations. Machine-learned potent…
Version 2.0.0 -- SPARC: Simulation Package for Ab-initio Real-space Calculations
Boqin Zhang, Xin Jing, Qimen Xu +8
SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn-Sham density functional theory calculations. Version 2.0.0 of the s…
Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems
Gabriel S. Gusmão, Andrew J. Medford
Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse P…