Machine Learning Small Polaron Dynamics.

Polarons are crucial for charge transport in semiconductors, significantly impacting material properties and device performance. The dynamics of small polarons can be investigated using first-principles molecular dynamics. However, the limited timescale of these simulations presents a challenge for adequately sampling infrequent polaron hopping events.

Quantum Delocalization Enables Water Dissociation on Ru(0001).

We revisit the long-standing question of whether water molecules dissociate on the Ru(0001) surface through nanosecond-scale path-integral molecular dynamics simulations on a sizable supercell. This is made possible through the development of an efficient and reliable machine-learning potential with near first-principles accuracy, overcoming the limitations of previous ab initio studies. We show that the quantum delocalization associated with nuclear quantum effects enables rapid and frequent proton transfers between water molecules, thereby facilitating the water dissociation on Ru(0001).

Atomic-Scale View at the Segregation of Alkali Metals toward the KTaO(001) Perovskite Surface.

Perovskites exhibit outstanding performance in applications such as photocatalysis, electrochemistry, or photovoltaics, yet their practical use is hindered by the instability of these materials under operating conditions, specifically caused by the segregation of alkali cations toward the surface. The problem arises from the bulk strain related to different cation sizes, as well as the inherent electrostatic instability of perovskite surfaces. Here, we focus on atomistic details of the surface-driven process of interlayer switching of alkali atoms at the inorganic perovskite surface.

Quantitative Measurement of Cooperative Binding in Partially Dissociated Water Dimers at the Hematite "R-Cut" Surface.

Water-solid interfaces pervade the natural environment and modern technology. On some surfaces, water-water interactions induce the formation of partially dissociated interfacial layers; understanding why is important to model processes in catalysis or mineralogy. The complexity of the partially dissociated structures often makes it difficult to probe them quantitatively.

Derivative learning of tensorial quantities-Predicting finite temperature infrared spectra from first principles.

We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method's effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic-inorganic halide perovskite MAPbI3, where our results consistently align with experimental data.

Toward Large-Scale AFQMC Calculations: Large Time Step Auxiliary-Field Quantum Monte Carlo.

We report modifications of the ph-AFQMC algorithm that allow the use of large time steps and reliable time step extrapolation. Our modified algorithm eliminates size-consistency errors present in the standard algorithm when large time steps are employed. We investigate various methods to approximate the exponential of the one-body operator within the AFQMC framework, distinctly demonstrating the superiority of Krylov methods over the conventional Taylor expansion.

Layer-by-layer phase transformation in TiO revealed by machine-learning molecular dynamics simulations.

Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-TiO exhibits an ultrafast and reversible behavior.

Machine Learning Density Functionals from the Random-Phase Approximation.

Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we use machine learning to map the RPA to a pure Kohn-Sham density functional.

Benchmark Phaseless Auxiliary-Field Quantum Monte Carlo Method for Small Molecules.

We report a scalable Fortran implementation of the phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) and demonstrate its excellent performance and beneficial scaling with respect to system size. Furthermore, we investigate modifications of the phaseless approximation that can help to reduce the overcorrelation problems common to the ph-AFQMC. We apply the method to the 26 molecules in the HEAT set, the benzene molecule, and water clusters.

Suppressed Fluctuations as the Origin of the Static Magnetic Order in Strained Sr_{2}RuO_{4}.

Combining first-principles density-functional calculations and Moriya's self-consistent renormalization theory, we explain the recently reported counterintuitive appearance of an ordered magnetic state in uniaxially strained Sr_{2}RuO_{4} beyond the Lifshitz transition. We show that strain weakens the quantum spin fluctuations, which destroy the static order, more strongly than the tendency to magnetism. A different rate of decrease of the spin fluctuations vs magnetic stabilization energy promotes the onset of a static magnetic order beyond a critical strain.

Approaching the basis-set limit of the dRPA correlation energy with explicitly correlated and projector augmented-wave methods.

The direct random-phase approximation (dRPA) is used to calculate and compare atomization energies for the HEAT set and ten selected molecules of the G2-1 set using both plane waves and Gaussian-type orbitals. We describe detailed procedures to obtain highly accurate and well converged results for the projector augmented-wave method as implemented in the Vienna Ab initio Simulation Package as well as the explicitly correlated dRPA-F12 method as implemented in the TURBOMOLE package. The two approaches agree within chemical accuracy (1 kcal/mol) for the atomization energies of all considered molecules, both for the exact exchange as well as for the RPA.

Comparing GIPAW with numerically exact chemical shieldings: The role of two-center contributions to the induced current.

In this study, we benchmark density functional theory gauge-including projector-augmented-wave (GIPAW) chemical shieldings against molecular shieldings for which basis set completeness has been achieved [Jensen et al., Phys. Chem.

Pressure-Induced Excitations in the Out-of-Plane Optical Response of the Nodal-Line Semimetal ZrSiS.

The anisotropic optical response of the layered, nodal-line semimetal ZrSiS at ambient and high pressure is investigated by frequency-dependent reflectivity measurements for the polarization along and perpendicular to the layers. The highly anisotropic optical conductivity is in very good agreement with results from density-functional theory calculations and confirms the anisotropic character of ZrSiS. Whereas the in-plane optical conductivity shows only modest pressure-induced changes, we found strong effects on the out-of-plane optical conductivity spectrum of ZrSiS, with the appearance of two prominent excitations.