86 citations · 174 across the 29 of their papers we have counts for
31 papers
Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation
Abir Haque, Edmond Chow, Shikhar Shah +3
We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory.…
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu +12
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and imp…
Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
Logan M. Brabson, Andrew J. Medford, David S. Sholl
Direct air capture (DAC) with solid sorbents such as metal-organic frameworks (MOFs) is a promising approach for negative carbon emissions. Computational materials screening can he…
Ab initio study of strain-driven vacancy clustering in aluminum
Sayan Bhowmik, Abhiraj Sharma, Andrew J. Medford +2
We present a first principles investigation of strain-driven vacancy clustering in aluminum. Specifically, we perform Kohn-Sham density functional theory calculations to study the…
SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations
Tian Tian, Lucas R Timmerman, Shashikant Kumar +3
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations…
Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks
Dingqi Nai, Gabriel S. Gusmão, Zachary A. Kilwein +2
The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically…
Spectral scheme for atomic structure calculations in density functional theory
Sayan Bhowmik, John E. Pask, Andrew J. Medford +1
We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation o…
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
Lucas R. Timmerman, Shashikant Kumar, Phanish Suryanarayana +1
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the…
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…
Soft and transferable pseudopotentials from multi-objective optimization
Mostafa Faghih Shojaei, John E. Pask, Andrew J. Medford +1
Ab initio pseudopotentials are a linchpin of modern molecular and condensed matter electronic structure calculations. In this work, we employ multi-objective optimization to maximi…
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Yuge Hu, Joseph Musielewicz, Zachary Ulissi +1
Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess the level of confidence during prediction, as ML models are not inherently physical and…
Ab-initio investigation of finite size effects in rutile titania nanoparticles with semilocal and nonlocal density functionals
Sushree Jagriti Sahoo, Xin Jing, Phanish Suryanarayana +1
In this work, we employ hybrid and generalized gradient approximation (GGA) level density functional theory (DFT) calculations to investigate the convergence of surface properties…
A Priori Calibration of Transient Kinetics Data via Machine Learning
M. Ross Kunz, Adam Yonge, Rakesh Batchu +5
The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residenc…
Application of DFTB and machine learning to evaluate the stability of biomass intermediates on the Rh(111) surface
Chaoyi Chang, Andrew J. Medford
Biomass compounds adsorbed on surfaces are challenging to study due to the large number of possible species and adsorption geometries. In this work, possible intermediates of eryth…
A Universal Framework for Featurization of Atomistic Systems
Xiangyun Lei, Andrew J. Medford
Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum mo…
Kinetics-Informed Neural Networks
Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha +1
Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the r…
Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning
M. Ross Kunz, Adam Yonge, Zongtang Fang +4
Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials.…
Population Susceptibility Variation and Its Effect on Contagion Dynamics
Christopher Rose, Andrew J. Medford, C. Franklin Goldsmith +3
Susceptibility governs the dynamics of contagion. The classical SIR model is one of the simplest compartmental models of contagion spread, assuming a single shared susceptibility l…