Max C. Gallant
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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: Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions
Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions Open
Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pair-wise reaction approaches have navigated the thermodynamic landscape…
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 ab initio non-crystalline structure database: empowering machine learning to decode diffusivity
The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity Open
Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-dri…
View article: A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions
A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions Open
New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a…
View article: ReactCA: A Cellular Automaton for Predicting Phase Evolution in Solid-State Reactions
ReactCA: A Cellular Automaton for Predicting Phase Evolution in Solid-State Reactions Open
New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a…
View article: pylattica: a package for prototyping lattice models inchemistry and materials science
pylattica: a package for prototyping lattice models inchemistry and materials science Open
pylattica provides a simple and flexible framework for prototyping lattice-based simulations such as atomistic Monte Carlo simulations or cellular automata.It is differentiated from other lattice simulation packages by i) its agnosticism t…
View article: The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
The ab initio amorphous materials database: Empowering machine learning to decode diffusivity Open
Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex…
View article: Jobflow: Computational Workflows Made Simple
Jobflow: Computational Workflows Made Simple Open
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into com…
View article: Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials
Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials Open
Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and second…
View article: Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials
Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials Open
Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and second…
View article: Shape‐Controlled NaTaO<sub>3</sub> by Flux‐Mediated Synthesis
Shape‐Controlled NaTaO<sub>3</sub> by Flux‐Mediated Synthesis Open
NaTaO 3 is a stable and wide bandgap n‐type semiconductor material with many different applications. Here, a flux‐mediated synthesis method is presented for NaTaO 3 resulting in highly distinctive, substrate covering shapes via precursor c…