Kristin A. Persson
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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: 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: Ultrafast synthesis of ternary oxides via a design-test-make-analyze workflow
Ultrafast synthesis of ternary oxides via a design-test-make-analyze workflow Open
Data-driven methodologies are poised to revolutionize inorganic materials discovery. However, they often face challenges arising from discrepancies between theoretical prediction and experimental validation. In this work, we present an end…
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: Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT
Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT Open
NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by d…
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: A framework to evaluate machine learning crystal stability predictions
A framework to evaluate machine learning crystal stability predictions Open
The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for…
View article: Machine learned potential for high-throughput phonon calculations of metal—organic frameworks
Machine learned potential for high-throughput phonon calculations of metal—organic frameworks Open
Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and m…
View article: Disordered Rocksalts as High‐Energy and Earth‐Abundant Li‐Ion Cathodes
Disordered Rocksalts as High‐Energy and Earth‐Abundant Li‐Ion Cathodes Open
To address the growing demand for energy and support the shift toward transportation electrification and intermittent renewable energy, there is an urgent need for low‐cost, energy‐dense electrical storage. Research on Li‐ion electrode mat…
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: ZnGa₂Te₄ Thin-Film Photocathodes for Photoelectrochemical CO₂ Reduction
ZnGa₂Te₄ Thin-Film Photocathodes for Photoelectrochemical CO₂ Reduction Open
Photoelectrochemical (PEC) carbon dioxide reduction reaction (CO₂RR) has been considered as a promising route to convert and store solar energy into chemical fuels. It is crucial to find suitable photoelectrode materials that are photo-cat…
View article: Systematic computational study of oxide adsorption properties for applications in photocatalytic CO2 reduction
Systematic computational study of oxide adsorption properties for applications in photocatalytic CO2 reduction Open
While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understa…
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: HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions
HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions Open
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately…
View article: Designing Advanced Electrolytes for High‐Voltage High‐Capacity Disordered Rocksalt Cathodes
Designing Advanced Electrolytes for High‐Voltage High‐Capacity Disordered Rocksalt Cathodes Open
Lithium (Li)‐excess transition metal oxide materials which crystallize in the cation‐disordered rock salt (DRX) structure are promising cathodes for realizing low‐cost, high‐energy‐density Li batteries. However, the state‐of‐the‐art electr…
View article: Structural origin of disorder-induced ion conduction in NaFePO4 cathode materials
Structural origin of disorder-induced ion conduction in NaFePO4 cathode materials Open
Most modern battery technologies depend on solid-state crystalline cathode materials. However, some of these materials are constrained by the low ionic conductivity of their most stable phases. An example of this is maricite (NaFePO4). Int…
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: An ensemble method for identifying consistent models in interpretable machine learning
An ensemble method for identifying consistent models in interpretable machine learning Open
Machine learning (ML) is becoming indispensable for accelerating understanding and design in science. However, limited availability of data in the physical sciences necessarily directs data-driven methods towards feature-based and interpre…
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: MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models Open
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials…
View article: Towards MatCore: A Unified Metadata Standard for Materials Science
Towards MatCore: A Unified Metadata Standard for Materials Science Open
The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accom…
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: Circularity in polydiketoenamine thermoplastics via control over reactive chain conformation
Circularity in polydiketoenamine thermoplastics via control over reactive chain conformation Open
Controlling the reactivity of bonds along polymer chains enables both functionalization and deconstruction with relevance to chemical recycling and circularity. Because the substrate is a macromolecule, however, understanding the effects o…
View article: Mechanisms Underpinning Heterogeneous Deconstruction of Circular Polymers: Insight from Magnetic Resonance Methodologies
Mechanisms Underpinning Heterogeneous Deconstruction of Circular Polymers: Insight from Magnetic Resonance Methodologies Open
Circular plastics thrive on the ability to chemically recycle polymers into reusable monomers, ideally closing the loop from manufacturing to the end of life. Mechanisms for polymer deconstruction are complex, involving diffusion and trans…
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…