Sub-photon accuracy noise reduction of a single shot coherent diffraction pattern with an atomic model trained autoencoder.

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning.

Compensated Ferrimagnets with Colossal Spin Splitting in Organic Compounds.

The study of the magnetic order has recently been invigorated by the discovery of exotic collinear antiferromagnets with time-reversal symmetry breaking. Examples include altermagnets and compensated ferrimagnets, which show spin splittings of the electronic band structures even at zero net magnetization, leading to several unique transport phenomena, notably spin-current generation. Altermagnets demonstrate anisotropic spin splitting, such as d-wave, in momentum space, whereas compensated ferrimagnets exhibit isotropic spin splitting. However, methods to realize compensated ferrimagnets are limited. Here, we demonstrate a method to realize a fully compensated ferrimagnet with isotropic spin splitting utilizing the dimer structures inherent in organic compounds. Moreover, based on ab initio calculations, we find that this compensated ferrimagnet can be realized in the recently discovered organic compound (EDO-TTF-I)_{2}ClO_{4}. Our findings provide an unprecedented strategy for using the dimer degrees of freedom in organic compounds to realize fully compensated ferrimagnets with colossal spin splitting.

Single-crystalline oligomer-based conductors modeling the doped poly(3,4-ethylenedioxythiophene) family.

Conductive polymers with highly conjugated systems, such as the doped poly(3,4-ethylenedioxythiophene) (PEDOT) family, are commonly used in organic electronics. However, their structural inhomogeneity with various chain lengths makes it difficult to control their conductivities and structural details. On the other hand, low-molecular-weight materials have well-defined structures but relatively narrow conjugate areas with a limited range of Coulomb repulsion between carriers (Ueff), which hamper the flexible control of conductivities. To bridge this gap, we developed oligomer-based conductors, which are intermediate materials between polymers and low-molecular-weight materials. Using a library of single-crystal charge-transfer salts of oligo(3,4-ethylenedioxythiophene) (oligoEDOT) analogs that model the doped PEDOT family, we have investigated the structure-determining factors affecting their conductivities, such as counter anion variations, lengths of oligomer donor, and band fillings. Through the screening study, we developed oligoEDOT analogs with tunable room temperature conductivities by several orders of magnitude, including a metallic state above room temperature. In this study, we consistently evaluated the electronic structural insights by first-principles calculations and revealed that Ueff is the dominant factor that determines the relationship between the structures and conductivities. The unique features of oligoEDOT conductor systems with widely variable Ueff can differentiate these systems from strongly electron-correlated systems.

Metallic State of a Mixed-Sequence Oligomer Salt That Models Doped PEDOT Family.

Modern organic conductors are typically low-molecular-weight or polymer-based materials. Low-molecular-weight materials can be characterized using crystallographic information, allowing structure-conductivity relationships to be established and conduction mechanisms to be understood. However, controlling their conductive properties through molecular structural modulation is often challenging because of their relatively narrow conjugate areas. In contrast, polymer-based materials have highly π-conjugated structures with wide molecular-weight distributions, and their structural inhomogeneity makes characterizing their structures difficult. Thus, we focused on the less-explored intermediate, i.e., single-molecular-weight oligomers that model doped poly(3,4-ethylenedioxythiophene) (PEDOT). The dimer and trimer models provided clear structures; however, the short oligomers led to much lower conductivities (<10-3 S cm-1) than that of doped PEDOT. Herein, we elongated the oligomer to a tetramer through geometrical tuning based on a mixed sequence. The "P-S-S-P" sequence (S: 3,4-ethylenedithiothiophene; P: 3,4-(2',2'-dimethypropylenedioxy)thiophene) with twisted S-S enhanced the solubility and chemical stability. The subsequent oxidation process planarized the oligomer and expanded the conjugate area. Interestingly, the sequence involving sterically bulky outer P units allowed the doped oligomer to form a pitched π-stack in the single-crystal form. This enabled the inclusion of excess counter anions, which modulated the band filling. The combined effects of conjugate area expansion and band-filling modulation significantly increased the room-temperature conductivity to 36 S cm-1. This is the highest value reported for a single-crystalline oligomer conductor. Furthermore, a metallic state was observed above room temperature in a single-crystalline oligoEDOT for the first time. This unique mixed-sequence strategy for oligomer-based conductors enabled the precise control of conductive properties.

Comprehensive Ab Initio Investigation of the Phase Diagram of Quasi-One-Dimensional Molecular Solids.

An ab initio investigation of the family of molecular compounds TM_{2}X is conducted, where TM is either TMTSF or TMTTF and X takes centrosymmetric monovalent anions. By deriving the extended Hubbard-type Hamiltonians from first-principles band calculations and evaluating not only the intermolecular transfer integrals but also the Coulomb parameters, we discuss their material dependence in the unified phase diagram. Furthermore, we apply the many-variable variational Monte Carlo method to accurately determine the symmetry-breaking phase transitions, and show the development of the charge and spin orderings. We show that the material-dependent parameter can be taken as the correlation effect, represented by the value of the screened on-site Coulomb interaction U relative to the intrachain transfer integrals, for the comprehensive understanding of the spin and charge ordering in this system.

Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning.

We propose a scheme for ab initio configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of relaxed structures from the perfect lattice with configurational disorder instead of the usual way of training to predict energies as functions of continuous atom coordinates. An active learning scheme is employed to obtain a training set containing configurations of thermodynamic relevance. This enables bypassing of the structural relaxation procedure that is necessary when applying conventional NNP approaches to the lattice configuration problem. The idea is demonstrated on the calculation of the temperature dependence of the degree of A/B site inversion in three spinel oxides, MgAl2O4, ZnAl2O4, and MgGa2O4. The present scheme may serve as an alternative to cluster expansion for "difficult" systems, e.g., complex bulk or interface systems with many components and sublattices that are relevant to many technological applications today.

Orbital hybridization of donor and acceptor to enhance the conductivity of mixed-stack complexes.

Mixed-stack complexes which comprise columns of alternating donors and acceptors are organic conductors with typically poor electrical conductivity because they are either in a neutral or highly ionic state. This indicates that conductive carriers are insufficient or are mainly localized. In this study, mixed-stack complexes that uniquely exist at the neutral-ionic boundary were synthesized by combining donors (bis(3,4-ethylenedichalcogenothiophene)) and acceptors (fluorinated tetracyanoquinodimethanes) with similar energy levels and orbital symmetry between the highest occupied molecular orbital of the donor and the lowest unoccupied molecular orbital of the acceptor. Surprisingly, the orbitals were highly hybridized in the single-crystal complexes, enhancing the room-temperature conductivity (10-4-0.1 S cm-1) of mixed-stack complexes. Specifically, the maximum conductivity was the highest reported for single-crystal mixed-stack complexes under ambient pressures. The unique electronic structures at the neutral-ionic boundary exhibited structural perturbations between their electron-itinerant and localized states, causing abrupt temperature-dependent changes in their electrical, optical, dielectric, and magnetic properties.

Precise Control of the Molecular Arrangement of Organic Semiconductors for High Charge Carrier Mobility.

Organic semiconductors are well-known to exhibit high charge carrier mobility based on their spread of the π-orbital. In particular, the π-orbital overlap between neighboring molecules significantly affects their charge carrier mobility. This study elucidated the direct effect of subtle differences in the π-orbital overlap on charge carrier mobility, by precisely controlling only molecular arrangements without any chemical modifications. We synthesized disulfonic acid composed of a [1]benzothieno[3,2-b][1]benzothiophene (BTBT) moiety, and prepared organic salts with four butylamine isomers. Regardless of the type of butylamine combined, electronic states of the constituent BTBT derivative were identical, and all BTBT arrangements were edge-to-face herringbone-type. However, depending on the difference of steric hindrance, center-to-center distances and dihedral angles between neighboring BTBT moieties slightly varied. Despite a similar arrangement, the photoconductivity of four organic salts differed by a factor of approximately two. Additionally, theoretical charge carrier mobilities from their crystal structures exhibited a strong correlation with their photoconductivity.

Tuning the magnetic dimensionality by charge ordering in the molecular TMTTF salts.

We theoretically investigate the interplay between charge ordering and magnetic states in quasi-one-dimensional molecular conductors TMTTF(2)X, motivated by the observation of a complex variation of competing and/or coexisting phases. We show that the ferroelectric-type charge order increases two-dimensional antiferromagnetic spin correlation, whereas in the one-dimensional regime two different spin-Peierls states are stabilized. By using first-principles band calculations for the estimation for the transfer integrals and comparing our results with the experiments, we identify the controlling parameters in the experimental phase diagram to be not only the interchain transfer integrals but also the amplitude of the charge order.

Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data.

A data-science approach to solving the ill-conditioned inverse problem for analytical continuation is proposed. The root of the problem lies in the fact that even tiny noise of imaginary-time input data has a serious impact on the inferred real-frequency spectra. By means of a modern regularization technique, we eliminate redundant degrees of freedom that essentially carry the noise, leaving only relevant information unaffected by the noise. The resultant spectrum is represented with minimal bases and thus a stable analytical continuation is achieved. This framework further provides a tool for analyzing to what extent the Monte Carlo data need to be accurate to resolve details of an expected spectral function.