Enhancing Lignin-Carbohydrate Complexes Production and Properties With Machine Learning.

Lignin-carbohydrate complexes (LCCs) present a unique opportunity for harnessing the synergy between lignin and carbohydrates for high-value product development. However, producing LCCs in high yields remains a significant challenge. In this study, we address this challenge with a novel approach for the targeted production of LCCs.

Machine-learning accelerated structure search for ligand-protected clusters.

Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method.

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework.

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J.

Data-Driven Compound Identification in Atmospheric Mass Spectrometry.

Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes.

Correction to "Screening Mixed-Metal SnM(III)ChX Chalcohalides for Photovoltaic Applications".

[This corrects the article DOI: 10.1021/acs.chemmater.

Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules.

Low-volatile organic compounds (LVOCs) drive key atmospheric processes, such as new particle formation (NPF) and growth. Machine learning tools can accelerate studies of these phenomena, but extensive and versatile LVOC datasets relevant for the atmospheric research community are lacking. We present the GeckoQ dataset with atomic structures of 31,637 atmospherically relevant molecules resulting from the oxidation of α-pinene, toluene and decane.

Updates to the DScribe library: New descriptors and derivatives.

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe.

Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization.

Finding low-energy conformers of organic molecules is a complex problem due to the flexibilities of the molecules and the high dimensionality of the search space. When such molecules are on nanoclusters, the search complexity is exacerbated by constraints imposed by the presence of the cluster and other surrounding molecules. To address this challenge, we modified our previously developed active learning molecular conformer search method based on Bayesian optimization and density functional theory.

Benchmark of Methods for Core-Level Binding Energies.

The approximation has recently gained increasing attention as a viable method for the computation of deep core-level binding energies as measured by X-ray photoelectron spectroscopy. We present a comprehensive benchmark study of different methodologies (starting point optimized, partial and full eigenvalue-self-consistent, Hedin shift, and renormalized singles) for molecular inner-shell excitations. We demonstrate that all methods yield a unique solution and apply them to the CORE65 benchmark set and ethyl trifluoroacetate.

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and .

We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.

Molecular Conformer Search with Low-Energy Latent Space.

Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data.

Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models.

Abstract: Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs.

Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study.

The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO, and ZrO for the CsPbI perovskite. The optimal interface registries are identified with Bayesian optimization.

All-Electron BSE@ Method for -Edge Core Electron Excitation Energies.

We present an accurate computational approach to calculate absolute -edge core electron excitation energies as measured by X-ray absorption spectroscopy. Our approach employs an all-electron Bethe-Salpeter equation (BSE) formalism based on quasiparticle energies (BSE@) using numeric atom-centered orbitals (NAOs). The BSE@ method has become an increasingly popular method for the computation of neutral valence excitation energies of molecules.

Assessing the Γ Approach: Beyond with Hedin's Full Second-Order Self-Energy Contribution.

We present and benchmark a self-energy approach for quasiparticle energy calculations that goes beyond Hedin's approximation by adding the full second-order self-energy (FSOS-) contribution. The FSOS- diagram involves two screened Coulomb interaction () lines, and adding the FSOS- to the self-energy can be interpreted as first-order vertex correction to (Γ). Our FSOS- implementation is based on the resolution-of-identity technique and exhibits better than () scaling with system size for small- to medium-sized molecules.

Electronic Characterization of a Charge-Transfer Complex Monolayer on Graphene.

Organic charge-transfer complexes (CTCs) formed by strong electron acceptor and strong electron donor molecules are known to exhibit exotic effects such as superconductivity and charge density waves. We present a low-temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) study of a two-dimensional (2D) monolayer CTC of tetrathiafulvalene (TTF) and fluorinated tetracyanoquinodimethane (FTCNQ), self-assembled on the surface of oxygen-intercalated epitaxial graphene on Ir(111) (G/O/Ir(111)). We confirm the formation of the charge-transfer complex by d/d spectroscopy and direct imaging of the singly occupied molecular orbitals.

Real-time time-dependent density functional theory implementation of electronic circular dichroism applied to nanoscale metal-organic clusters.

Electronic circular dichroism (ECD) is a powerful spectroscopy method for investigating chiral properties at the molecular level. ECD calculations with the commonly used linear-response time-dependent density functional theory (LR-TDDFT) framework can be prohibitively costly for large systems. To alleviate this problem, we present here an ECD implementation within the projector augmented-wave method in a real-time-propagation TDDFT framework in the open-source GPAW code.

Improved One-Shot Total Energies from the Linearized GW Density Matrix.

The linearized GW density matrix (γ) is an efficient method to improve the static portion of the self-energy compared to that of ordinary perturbative GW while keeping the single-shot simplicity of the calculation. Previous work has shown that γ gives an improved Fock operator and total energy components that approach the self-consistent GW quality. Here, we test γ for dimer dissociation for the first time by studying N, LiH, and Be.

Surface Functionalization of 2D MXenes: Trends in Distribution, Composition, and Electronic Properties.

Using a multiscale computational scheme, we study the trends in distribution and composition of the surface functional groups -O, -OH, and -F on two-dimensional (2D) transition metal carbides and nitrides (MXenes). We consider TiN, TiN, NbC, NbC, TiC, and TiC to explore MXenes with different chemistry and different number of atomic layers. Using a combination of cluster expansion, Monte Carlo, and density functional theory methods, we study the distribution and composition of functional groups at experimentally relevant conditions.

Atomic and electronic structure of cesium lead triiodide surfaces.

The (001) surface of the emerging photovoltaic material cesium lead triiodide (CsPbI) is studied. Using first-principles methods, we investigate the atomic and electronic structure of cubic (α) and orthorhombic (γ) CsPbI. For both phases, we find that CsI-termination is more stable than PbI-termination.

Efficient Amino Acid Conformer Search with Bayesian Optimization.

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate.

Detecting stable adsorbates of (1)-camphor on Cu(111) with Bayesian optimization.

Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates.

Relativistic correction scheme for core-level binding energies from GW.

We present a relativistic correction scheme to improve the accuracy of 1s core-level binding energies calculated from Green's function theory in the GW approximation, which does not add computational overhead. An element-specific corrective term is derived as the difference between the 1s eigenvalues obtained from the self-consistent solutions to the non- or scalar-relativistic Kohn-Sham equations and the four-component Dirac-Kohn-Sham equations for a free neutral atom. We examine the dependence of this corrective term on the molecular environment and the amount of exact exchange in hybrid exchange-correlation functionals.

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules.

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62.