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Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the field of biochemistry. This study addresses the challenge of comprehending protein-protein interactions (PPI) and developing predicitive models for proteins and organic compounds, with a specific focus on quantifying their binding affinity. Here, we introduce the active Bayesian Committee Machine (BCM) potential, specifically designed to predict oxygen-containing organic compounds within eight groups of CHO. The BCM potential adopts a committee-based approach to tackle scalability issues associated with kernel regressors, particularly when dealing with large datasets. Its adaptable structure allows for efficient and cost-effective expansion, maintaing both transferability and scalability. Through systematic benchmarking, we position the sparse BCM potential as a promising contender in the pursuit of a universal machine learning potential.

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation. To solve these problems, machine learning interatomic potential (ML-IAP) technology is attracting attention as an alternative. ML-IAP not only has high accuracy by faithfully expressing the density functional theory (DFT), but also has the advantage of low computational cost. However, there is a problem that the potential energy changes significantly depending on the environment of each atom, and expansion to a wide range of compounds within a single model is still difficult to build in the case of a kernel-based model. To solve this problem, we would like to develop a universal ML-IAP using this active Bayesian Committee Machine (BCM) potential methodology for carbon-nitrogen-hydrogen (CNH) with various compositions. ML models are trained and generated through first-principles calculations and molecular dynamics simulations for molecules with only CNH. Using long amine structures to test an ML model trained only with short chains, the results show excellent consistency with DFT calculations. Consequently, machine learning-based models for organic molecules not only demonstrate the ability to accurately describe various physical properties but also hold promise for investigating a broad spectrum of diverse materials systems.

We solve for the elementary excitation in infinite quasi-1D quantum lattices by extending the recently developed infinite quasi-1D entanglement perturbation theory. The wave function of an excited state is variationally determined by optimizing superposition of cluster operation, each of which is composed of simultaneous on-site operation inside a block of lattice sites, on the ground state in a form of plane wave. The excitation energy with respect to the wave number gives the spectra for an elementary excitation. Our method is artificial broadening free and is adaptive for various quasi-particle pictures. Using the triplet spectrum, the application to $\infty$-by-$N$ antiferromagnetic spin-$\frac{1}{2}$ ladders for $N=2, 4, 6, 8$, and $10$ confirms a previous report that there is a quantum dimensional transition, namely, the lattice transits from quasi-1D to 2D at a finite critical value $N_c=10$. The massless triplet dispersion at $\left( \pi, \pi \right)$ sees a vanishing gap. Our results detect the anomaly at $\left(\pi,0\right)$ in the triplet spectrum, agreeing well with the inelastic neutron scattering measurement of a macroscopic sample. Surprisingly, our results also reveal a gapless and massive 1D singlet dispersion channel that is much lower than the triplet excitation. We note, however, the dimensional transition is determined by the massless triplet dispersion., Comment: 5 pages, 4 figures, Supplemental Information

We report the prediction and observation of supra-binary ferroelectricity in a ferroelectric nanowire (FNW) covered with a semi-cylindrical gate that provides an anisotropic electric field in the FNW. There are gate-voltage-driven transitions between four polarization phases in FNW's cross section, dubbed axial-up, axial-down, radial-in and radial-out. They are determined by the interplay between the topological depolarization energy and the free energy induced by an anisotropic external electric field, in clear distinction from the conventional film-based binary ferroelectricity. When the FNW is mounted on a biased graphene nanoribbon (GNR), these transitions induce exotic current-voltage hysteresis in the FNW-GNR transistor. Our discovery suggests new operating mechanisms of ferroelectric devices. In particular, it enables intrinsic multi-bit information manipulation in parallel to the binary manipulation employed in data storage devices., Comment: 4 figures, 7 supporting figures, 2 animations

QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit (CPU) and graphical processing unit (GPU) systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://www.qmcpack.org .

We propose that spatial density matrices, which are singularly important in the study of quantum entanglement, encode the electronic fluctuations and correlations responsible for covalent bonding. From these density matrices, we develop tools that allow us to analyse how many body wave functions can be broken up into real space pieces. We apply these tools to the first row dimers, and in particular, we address the conflicting evidence in the literature about the presence of an inverted fourth bond and anti-ferromagnetic correlations in the $\text{C}_2$ molecule. Our results show that many body effects enhance anti-ferromagnetic fluctuations but are not related to the formation of an inverted fourth bond. We identify two inverted bonds in the $\text{C}_2$ molecule and establish their correspondence to the bonds in the $\text{Be}_2$ molecule. Additionally, we provide a new interpretation of the Mayer index, introduce partial bonds to fix deficiencies in molecular orbital theory, and prove the Hartree-Fock wave function for C$_{2}$ is not a triple bond. Our results suggest that entanglement-based methods can lead to a more realistic treatment of molecular and extended systems than possible before., Comment: 22 pages, 3 figures

Several algorithms have been proposed to calculate the spatial entanglement spectrum from high order Renyi entropies. In this work we present an alternative approach for computing the entanglement spectrum with quantum Monte Carlo for both continuum and lattice Hamiltonians. This method provides direct access to the matrix elements of the spatially reduced density matrix and we determine an estimator that can be used in variational Monte Carlo as well as other Monte Carlo methods. The algorithm is based on using a generalization of the Swap operator, which can be extended to calculate a general class of density matrices that can include combinations of spin, space, particle and even momentum coordinates. We demonstrate the method by applying it to the Hydrogen and Nitrogen molecules and describe for the first time how the spatial entanglement spectrum encodes a covalent bond that includes all the many body correlations., Comment: 6 pages, 3 figures