Aaron D. Kaplan
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View article: Cross-functional transferability in foundation machine learning interatomic potentials
Cross-functional transferability in foundation machine learning interatomic potentials Open
The rapid development of foundation potentials (FPs) in machine learning interatomic potentials demonstrates the possibility for generalizable learning of the universal potential energy surface. The accuracy of FPs can be further improved …
View article: An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li<sub>7</sub>P<sub>3</sub>S<sub>11</sub>
An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li<sub>7</sub>P<sub>3</sub>S<sub>11</sub> Open
Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, par…
View article: Accelerating Crystal Structure Prediction with Machine Learning Forcefields
Accelerating Crystal Structure Prediction with Machine Learning Forcefields Open
Long-standing methods in materials simulation can now generally predict crystalline structure for near-/stable materials with high accuracy, and independently of local materials chemistry. However, these methods, particularly density funct…
View article: Toward High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules
Toward High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules Open
Efficient energy storage systems are crucial to address the intermittency of renewable energy sources. As multivalent batteries, Zn-ion batteries (ZIBs), while inherently low voltage, offer a promising low-cost alternative to Li-ion batter…
View article: MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials Open
We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium…
View article: Accelerated data-driven materials science with the Materials Project
Accelerated data-driven materials science with the Materials Project Open
The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than…
View article: Accelerated discovery of cost-effective photoabsorber materials for near-infrared (λ=1600 nm) photodetector applications
Accelerated discovery of cost-effective photoabsorber materials for near-infrared (λ=1600 nm) photodetector applications Open
Current infrared sensing devices are based on costly materials with relatively few viable alternatives known. To identify promising candidate materials for infrared photodetection, we have developed a high-throughput screening methodology …
View article: Towards High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules
Towards High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules Open
Efficient energy storage systems are crucial to address the intermittency of renewable energy sources. As multivalent batteries, Zn-ion batteries (ZIBs), while inherently low voltage, offer a promising low cost alternative to Li-ion batter…
View article: Cross-functional transferability in universal machine learning interatomic potentials
Cross-functional transferability in universal machine learning interatomic potentials Open
The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy surface. In principle, the accuracy of uMLIPs can be further …
View article: Noncollinear ground states of solids with a source-free exchange correlation functional
Noncollinear ground states of solids with a source-free exchange correlation functional Open
In this paper, we expand upon the source-free (SF) exchange correlation (XC) functional developed by Sangeeta Sharma and coworkers to plane-wave density functional theory (DFT) based on the projector augmented wave (PAW) method. This const…
View article: A Foundational Potential Energy Surface Dataset for Materials
A Foundational Potential Energy Surface Dataset for Materials Open
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density function…
View article: Atomate2: Modular workflows for materials science
Atomate2: Modular workflows for materials science Open
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. W…
View article: Atomate2: modular workflows for materials science
Atomate2: modular workflows for materials science Open
We present atomate2, a composable and interoperable workflow engine that extends its predecessor by leveraging the jobflow library and supporting a wide range of calculators (DFT and MLIPs) for dynamic, high-throughput workflow orchestrati…
View article: Correction: Atomate2: modular workflows for materials science
Correction: Atomate2: modular workflows for materials science Open
Correction for “Atomate2: modular workflows for materials science” by Alex M. Ganose et al. , Digital Discovery , 2025, 4 , 1944–1973, https://doi.org/10.1039/D5DD00019J.
View article: The Interplay Between Electron Localization, Magnetic Order, and Jahn-Teller Distortion that Dictates LiMnO$_2$ Phase Stability
The Interplay Between Electron Localization, Magnetic Order, and Jahn-Teller Distortion that Dictates LiMnO$_2$ Phase Stability Open
The development of Mn-rich cathodes for Li-ion batteries promises to alleviate supply chain bottlenecks in battery manufacturing. Challenges in Mn-rich cathodes arise from Jahn-Teller (JT) distortions of Mn$^{3+}$, Mn migration, and phase …
View article: How Does HF-DFT Achieve Chemical Accuracy for Water Clusters?
How Does HF-DFT Achieve Chemical Accuracy for Water Clusters? Open
Bolstered by recent calculations of exact functional-driven errors (FEs) and density-driven errors (DEs) of semilocal density functionals in the water dimer binding energy [Kanungo, B. J. Phys. Chem. Lett. 2024, 15, 323-328], we investigat…
View article: How does HF-DFT achieve chemical accuracy for water clusters?
How does HF-DFT achieve chemical accuracy for water clusters? Open
Bolstered by recent calculations of exact functional-driven errors (FEs) and density-driven errors (DEs) of semi-local density functionals in the water dimer binding energy [Kanungo et al., J. Phys. Chem. Lett. 2023, 15, 323], we investiga…
View article: Symmetry breaking and self-interaction correction in the chromium atom and dimer
Symmetry breaking and self-interaction correction in the chromium atom and dimer Open
Density functional approximations to the exchange–correlation energy can often identify strongly correlated systems and estimate their energetics through energy-minimizing symmetry-breaking. In particular, the binding energy curve of the s…
View article: Optoelectronic properties of bent two-dimensional materials from first-principles methods combined with machine learning
Optoelectronic properties of bent two-dimensional materials from first-principles methods combined with machine learning Open
A material’s interaction with light is highly relevant in the design of nanoelectronic devices such as photodiodes, solar cells, photocatalytic cells, phototransistors, and photodetectors. The interaction of a material with light can be al…
View article: Unconventional Error Cancellation Explains the Success of Hartree–Fock Density Functional Theory for Barrier Heights
Unconventional Error Cancellation Explains the Success of Hartree–Fock Density Functional Theory for Barrier Heights Open
Energy barriers, which control the rates of chemical reactions, are seriously underestimated by computationally efficient semilocal approximations for the exchange-correlation energy. The accuracy of a semilocal density functional approxim…
View article: <i>CoeffNet</i> : predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network
<i>CoeffNet</i> : predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network Open
CoeffNet uses coefficients of molecular orbitals of reactants and products to predict activation barriers.
View article: A foundation model for atomistic materials chemistry
A foundation model for atomistic materials chemistry Open
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. O…
View article: Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange–correlation kernels
Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange–correlation kernels Open
Time-dependent density functional theory within the linear response regime provides a solid mathematical framework to capture excitations. The accuracy of the theory, however, largely depends on the approximations for the exchange–correlat…
View article: Realistic non-collinear ground states of solids with source-free exchange correlation functional
Realistic non-collinear ground states of solids with source-free exchange correlation functional Open
In this work, we extend the source-free (SF) exchange correlation (XC) functional developed by Sangeeta Sharma and co-workers to plane-wave density functional theory (DFT) based on the projector augmented wave (PAW) method. This constraint…
View article: Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange-correlation kernels
Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange-correlation kernels Open
Time-dependent density functional theory (TDDFT) within the linear response regime provides a solid mathematical framework to capture excitations. The accuracy of the theory, however, largely depends on the approximations for the exchange-…
View article: CoeffNet: Predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network
CoeffNet: Predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network Open
Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density fun…
View article: Predicting the properties of NiO with density functional theory: Impact of exchange and correlation approximations and validation of the r2SCAN functional
Predicting the properties of NiO with density functional theory: Impact of exchange and correlation approximations and validation of the r2SCAN functional Open
Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in deve…
View article: QMC-consistent static spin and density local field factors for the uniform electron gas
QMC-consistent static spin and density local field factors for the uniform electron gas Open
Analytic mathematical models for the static spin ($G_-$) and density ($G_+$) local field factors for the uniform electron gas (UEG) as functions of wavevector and density are presented. These models closely fit recent quantum Monte Carlo (…
View article: Symmetry Breaking with the SCAN Density Functional Describes Strong Correlation in the Singlet Carbon Dimer
Symmetry Breaking with the SCAN Density Functional Describes Strong Correlation in the Singlet Carbon Dimer Open
The SCAN (strongly constrained and appropriately normed) meta-generalized gradient approximation (meta-GGA), which satisfies all 17 exact constraints that a meta-GGA can satisfy, accurately describes equilibrium bonds that are normally cor…