Boris Kozinsky
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View article: Nonequilibrium quantum dynamics in SrTiO <sub>3</sub> under impulsive THz radiation with machine learning
Nonequilibrium quantum dynamics in SrTiO <sub>3</sub> under impulsive THz radiation with machine learning Open
Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. The microscopic processes governing the formation of these states remain largely unknown, due to the inherent challenges in acces…
View article: Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices Open
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electrons, and atoms over vast spatiotemporal scales on Exaflop/s …
View article: Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts
Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts Open
The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd8Au92 nanoparticles supported on silica us…
View article: Quantum cooling below absolute zero
Quantum cooling below absolute zero Open
Similar to other perovskites in its family, SrTiO$_3$ exhibits a significant softening of the ferroelectric mode with decreasing temperature, a behavior that typically heralds the onset of a ferroelectric transition. However, this material…
View article: High-performance training and inference for deep equivariant interatomic potentials
High-performance training and inference for deep equivariant interatomic potentials Open
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic modeling tasks like molecular dynamics and high-th…
View article: Atomistic simulations of out-of-equilibrium quantum nuclear dynamics
Atomistic simulations of out-of-equilibrium quantum nuclear dynamics Open
The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these experiments is extremely challenging due to the complex nonl…
View article: Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics Open
In solid acid solid electrolytes CsH$_2$PO$_4$ and CsHSO$_4$, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion rotation. However, the precise role of polyanion rotation …
View article: Room-Temperature Decomposition of the Ethaline Deep Eutectic Solvent
Room-Temperature Decomposition of the Ethaline Deep Eutectic Solvent Open
Environmentally benign, nontoxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. We examine ethaline, a 2:1 molar ratio of ethylene glycol and ch…
View article: Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics Open
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machi…
View article: The design space of E(3)-equivariant atom-centred interatomic potentials
The design space of E(3)-equivariant atom-centred interatomic potentials Open
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has …
View article: Long-Range Surface Forces in Salt-in-Ionic Liquids
Long-Range Surface Forces in Salt-in-Ionic Liquids Open
Ionic liquids (ILs) are a promising class of electrolytes with a unique combination of properties, such as extremely low vapor pressures and nonflammability. Doping ILs with alkali metal salts creates an electrolyte that is of interest for…
View article: Programming liquid crystal elastomers for multistep ambidirectional deformability
Programming liquid crystal elastomers for multistep ambidirectional deformability Open
Ambidirectionality, which is the ability of structural elements to move beyond a reference state in two opposite directions, is common in nature. However, conventional soft materials are typically limited to a single, unidirectional deform…
View article: Room-temperature decomposition of the ethaline deep eutectic solvent
Room-temperature decomposition of the ethaline deep eutectic solvent Open
Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here, we examine one particular green solvent, ethaline, a 2:1 …
View article: Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining Open
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to variation of general system parameters. T…
View article: Ultrafast quantum dynamics in $\mathbf{\mathrm{SrTiO_3}}$ under impulsive THz radiation
Ultrafast quantum dynamics in $\mathbf{\mathrm{SrTiO_3}}$ under impulsive THz radiation Open
Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. Despite significant advances, the microscopic processes governing the formation of these states remain largely unknown. This stud…
View article: Atomistic simulations of out-of-equilibrium quantum nuclear dynamics
Atomistic simulations of out-of-equilibrium quantum nuclear dynamics Open
The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these experiments is extremely challenging due to the complex nonl…
View article: Surface roughening in nanoparticle catalysts
Surface roughening in nanoparticle catalysts Open
Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and characterize their structures and atomic sites that are corre…
View article: Atomistic evolution of active sites in multi-component heterogeneous catalysts
Atomistic evolution of active sites in multi-component heterogeneous catalysts Open
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-component systems have been shown to result in higher product yield…
View article: Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining Open
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to variation of general system parameters. T…
View article: A Recipe for Charge Density Prediction
A Recipe for Charge Density Prediction Open
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet exi…
View article: Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set Open
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d -block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predic…
View article: Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
Addressing the Band Gap Problem with a Machine-Learned Exchange Functional Open
The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT to predicting electronic properties, the band gap problem i…
View article: Unified Differentiable Learning of Electric Response
Unified Differentiable Learning of Electric Response Open
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we…
View article: Long-Range Interactions in Salt-in-Ionic Liquids
Long-Range Interactions in Salt-in-Ionic Liquids Open
Ionic liquids (ILs) are a promising class of electrolytes owing to a unique combination of properties, such as extremely low vapour pressures, non-flammability and being universal solvents. Doping ILs with alkali metal salts creates an ele…
View article: Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials Open
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical …