cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:d2-9f
title: Optoelectronic simulation and optimization of all perovskites tandem solar cells employing electrodeposited copper oxide as hole transport layer

description: Tandem solar cells are highly promising photovoltaic device that can potentially beat the maximum power conversion efficiency achieved so far in a single junction silicon solar cell by mitigating both the thermalization and transmission losses commonly encountered in a single junction solar cell. Among several different components of a tandem solar cell, hole-transport layer (HTL) plays an important role. Present day state of the art HTL layers are limited in number and sometimes highly expensive. In this work, we explore the feasibility of using electrodeposited Cu₂O and mixed phase (Cu₂O+CuO) Cu-O film as HTL in all-perovskite tandem solar cells and a detail optical, compositional and morphological analysis was performed. To access its performance as HTL in tandem devices, here we adapted an optoelectronic simulation approach using SCAPS-1D software tool and transfer matrix simulation routine where the parameters were either measured experimentally or carefully optimized to replicate the performance under realistic testing conditions. Photovoltaic parameters for single-junction cells with Cu₂O-HTL was found to be less sensitive on the electron affinity of the Cu₂O as opposed to that of Cu-O in a Cu-O-HTL based single junction cells. The highest efficiency predicted in our simulation is 24.95% in a 2-terminal tandem device with Cu₂O-HTL and electron affinity of 3.8 eV whereas with similar device architecture, in a 4-terminal tandem device, the highest efficiency can reach up to 35%.
DOI: 10.24435/materialscloud:jn-js
title: Mechanism of charge transport in lithium thiophosphate

description: Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li₃PS₄ are far to be fully understood, the role of PS₄ dynamics in charge transport still being controversial. We build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r²SCAN, and PBE0) to tackle this problem in all known phases of Li₃PS₄ (α, β and γ), for large system sizes and timescales. We discuss the physical origin of the observed superionic behavior of Li₃PS₄: the activation of PS₄ flipping drives a structural transition to a highly conductive phase, characterized by an increase of Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. We also rule out any paddle-wheel effects of PS₄ tetrahedra in the superionic phases–previously claimed to enhance Li-ion diffusion–due to the orders-of-magnitude difference between the rate of PS₄ flips and Li-ion hops at all temperatures below melting. 
This archive provides all the relevant data and input files that were used to fit the ML interatomic potentials used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction, the validation of the ML models and the calculation of the electronic band structure of the β and γ structure. Furthermore, it provides input files of all the molecular dynamics trajectories needed to investigate Li-ion diffusion properties of Li₃PS₄ and the rotational dynamics of PS₄ tetrahedra. Finally, it provides the raw data to reproduce the figures of the manuscript associated with this archive.
DOI: 10.24435/materialscloud:qy-gv
title: Mechanism of charge transport in lithium thiophosphate

description: Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li₃PS₄ are far to be fully understood, the role of PS₄ dynamics in charge transport still being controversial. We build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r²SCAN, and PBE0) to tackle this problem in all known phases of Li₃PS₄ (α, β and γ), for large system sizes and timescales. We discuss the physical origin of the observed superionic behavior of Li₃PS₄: the activation of PS₄ flipping drives a structural transition to a highly conductive phase, characterized by an increase of Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. We also rule out any paddle-wheel effects of PS₄ tetrahedra in the superionic phases–previously claimed to enhance Li-ion diffusion–due to the orders-of-magnitude difference between the rate of PS₄ flips and Li-ion hops at all temperatures below melting. 
This archive provides all the relevant data and input files that were used to fit the ML interatomic potentials used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction, the validation of the ML models and the calculation of the electronic band structure of the β and γ structure. Furthermore, it provides input files of all the molecular dynamics trajectories needed to investigate Li-ion diffusion properties of Li₃PS₄ and the rotational dynamics of PS₄ tetrahedra. Finally, it provides the raw data to reproduce the figures of the manuscript associated with this archive.
DOI: 10.24435/materialscloud:6n-zd
title: Water slowing down drives the occurrence of the low temperature dynamical transition in microgels

description: The protein dynamical transition marks an increase in atomic mobility and the onset of anharmonic motions at a critical temperature, which is considered relevant for protein functionality. This phenomenon is ubiquitous, regardless of protein composition, structure and biological function and typically occurs at large protein content, to avoid water crystallization. Recently, a dynamical transition has also been reported in non-biological macromolecules, such as poly(N-isopropyl acrylamide) (PNIPAM) microgels, bearing many similarities to proteins. While the generality of this phenomenon is well-established, the role of water in the transition remains a subject of debate. In this study, we use atomistic molecular dynamics simulations and elastic incoherent neutron scattering (EINS) experiments with selective deuteration to investigate the microscopic origin of the dynamical transition and distinguish water and PNIPAM roles. While a standard analysis of EINS experiments would suggest that the dynamical transition occurs in PNIPAM and water at a similar temperature, simulations reveal a different perspective, also qualitatively supported by experiments. From room temperature down to about 180 K, PNIPAM exhibits only modest changes of dynamics, while water, being mainly hydration water under the probed extreme confinement, significantly slows down and undergoes a mode-coupling transition from diffusive to activated. Our findings therefore challenge the traditional view of the dynamical transition, demonstrating that it occurs in proximity of the water mode-coupling transition, shedding light on the intricate interplay between polymer and water dynamics.
DOI: 10.24435/materialscloud:a2-z5
title: Water slowing down drives the occurrence of the low temperature dynamical transition in microgels

description: The protein dynamical transition marks an increase in atomic mobility and the onset of anharmonic motions at a critical temperature, which is considered relevant for protein functionality. This phenomenon is ubiquitous, regardless of protein composition, structure and biological function and typically occurs at large protein content, to avoid water crystallization. Recently, a dynamical transition has also been reported in non-biological macromolecules, such as poly(N-isopropyl acrylamide) (PNIPAM) microgels, bearing many similarities to proteins. While the generality of this phenomenon is well-established, the role of water in the transition remains a subject of debate. In this study, we use atomistic molecular dynamics simulations and elastic incoherent neutron scattering (EINS) experiments with selective deuteration to investigate the microscopic origin of the dynamical transition and distinguish water and PNIPAM roles. While a standard analysis of EINS experiments would suggest that the dynamical transition occurs in PNIPAM and water at a similar temperature, simulations reveal a different perspective, also qualitatively supported by experiments. From room temperature down to about 180 K, PNIPAM exhibits only modest changes of dynamics, while water, being mainly hydration water under the probed extreme confinement, significantly slows down and undergoes a mode-coupling transition from diffusive to activated. Our findings therefore challenge the traditional view of the dynamical transition, demonstrating that it occurs in proximity of the water mode-coupling transition, shedding light on the intricate interplay between polymer and water dynamics.
DOI: 10.24435/materialscloud:vc-ch
title: Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics

description: Interfacial processes involving metal (oxyhydr)oxide phases are important for the mobility and bioavailability of nutrients and contaminants in soils, sediments, and water. Consequently, these processes influence ecosystem health and functioning, and have shaped the biological and environmental co-evolution of Earth over geologic time. Here we employ reactive molecular dynamics simulations, supported by synchrotron X-ray spectroscopy to study the molecular-scale interfacial processes that influence surface complexation in ferrihydrite-water systems containing aqueous molybdate. We validate the utility of this approach by calculating surface complexation models directly from simulations. The reactive force-field captures the realistic dynamics of surface restructuring, surface charge equilibration, and the evolution of the interfacial water hydrogen bond network in response to adsorption and proton transfer. We find that upon hydration and adsorption, ferrihydrite restructures into a more disordered phase through surface charge equilibration, as revealed by simulations and high-resolution X-ray diffraction. We observed how this restructuring leads to a different interfacial hydrogen bond network compared to bulk water by monitoring water dynamics. Using umbrella sampling, we constructed the free energy landscape of aqueous molybdate adsorption at various concentrations and the deprotonation of the ferrihydrite surface. The results demonstrate excellent agreement with the values reported by experimental surface complexation models. These findings are important as reactive molecular dynamics opens new avenues to study mineral-water interfaces, enriching and refining surface complexation models beyond their foundational assumptions.
DOI: 10.24435/materialscloud:4d-nn
title: Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics

description: Interfacial processes involving metal (oxyhydr)oxide phases are important for the mobility and bioavailability of nutrients and contaminants in soils, sediments, and water. Consequently, these processes influence ecosystem health and functioning, and have shaped the biological and environmental co-evolution of Earth over geologic time. Here we employ reactive molecular dynamics simulations, supported by synchrotron X-ray spectroscopy to study the molecular-scale interfacial processes that influence surface complexation in ferrihydrite-water systems containing aqueous molybdate. We validate the utility of this approach by calculating surface complexation models directly from simulations. The reactive force-field captures the realistic dynamics of surface restructuring, surface charge equilibration, and the evolution of the interfacial water hydrogen bond network in response to adsorption and proton transfer. We find that upon hydration and adsorption, ferrihydrite restructures into a more disordered phase through surface charge equilibration, as revealed by simulations and high-resolution X-ray diffraction. We observed how this restructuring leads to a different interfacial hydrogen bond network compared to bulk water by monitoring water dynamics. Using umbrella sampling, we constructed the free energy landscape of aqueous molybdate adsorption at various concentrations and the deprotonation of the ferrihydrite surface. The results demonstrate excellent agreement with the values reported by experimental surface complexation models. These findings are important as reactive molecular dynamics opens new avenues to study mineral-water interfaces, enriching and refining surface complexation models beyond their foundational assumptions.
DOI: 10.24435/materialscloud:fq-k5
title: Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

description: Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example  underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.
DOI: 10.24435/materialscloud:a5-s5
title: Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

description: Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example  underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.
DOI: 10.24435/materialscloud:v1-52
title: Hamiltonian transformation for accurate and efficient band structure interpolation

description: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.
DOI: 10.24435/materialscloud:89-2k
title: Ab-initio simulation of liquid water without artificial high temperature

description: Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water's structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.
DOI: 10.24435/materialscloud:fg-rc
title: Ab-initio simulation of liquid water without artificial high temperature

description: Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water's structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.
DOI: 10.24435/materialscloud:y0-tj
title: Hamiltonian transformation for accurate and efficient band structure interpolation

description: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.
DOI: 10.24435/materialscloud:1e-zn
title: Hamiltonian transformation for accurate and efficient band structure interpolation

description: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.