Accurate SCC-DFTB Parametrization for Bulk Water

Accurate SCC-DFTB Parametrization of Liquid Water with Improved Atomic Charges and Iterative Boltzmann Inversion

This work presents improvements of the description of liquid water within the self-consistent-charge density-functional based tight-binding scheme combining the use of Weighted Mulliken (WMull) charges and optimized O-H repulsive potential through the iterative Boltzmann inversion (IBI) process. The quality of the newly developed models is validated considering pair radial distribution functions (RDFs), as well as other structural, energetic, thermodynamic, and dynamic properties. The use of WMull charges certainly improves the agreement with experimental data, however leading to over-structured RDFs at short distance, that can be further improved by considering an optimized O-H repulsive potential obtained by the IBI process. Three different schemes were used to optimize this potential: (i) optimization including short O-H distances. This led to accurate RDFs as well as improved self-diffusion coefficient and heat of vaporization, while the proton transfer energy barrier is severely deteriorated; (ii) optimization starting at long distance. The proton transfer energy barrier is recovered while the heat of vaporization is deteriorated and the O-H RDF is less accurate at short distance; (iii) optimization within the path-integral molecular dynamics scheme which allows us to exclude nuclear quantum effects from the repulsive potential. The latter potential, in conjunction with the WMull improved atomic charges, provides similar results as (i) for structural, dynamic, and thermodynamic properties while recovering a large part of the proton transfer energy barrier. It therefore offers a good compromise to study both dynamic properties and chemistry within liquid water at a quantum chemical level.

GAMaterial-A genetic-algorithm software for material design and discovery

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti₆ O₁₂ cluster, doping Al in Si₁₁ (4Al@Si₁₁ ) and Na₁₀ supported on graphene (Na₁₀ @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo₈ C₄ cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.

Metallic water: Transient state under ultrafast electronic excitation

The modern means of controlled irradiation by femtosecond lasers or swift heavy ion beams can transiently produce such energy densities in samples that reach collective electronic excitation levels of the warm dense matter state, where the potential energy of interaction of the particles is comparable to their kinetic energies (temperatures of a few eV). Such massive electronic excitation severely alters the interatomic potentials, producing unusual nonequilibrium states of matter and different chemistry. We employ density functional theory and tight binding molecular dynamics formalisms to study the response of bulk water to ultrafast excitation of its electrons. After a certain threshold electronic temperature, the water becomes electronically conducting via the collapse of its bandgap. At high doses, it is accompanied by nonthermal acceleration of ions to a temperature of a few thousand Kelvins within sub-100 fs timescales. We identify the interplay of this nonthermal mechanism with the electron-ion coupling, enhancing the electron-to-ions energy transfer. Various chemically active fragments are formed from the disintegrating water molecules, depending on the deposited dose.

Hydrogen Bonding and Infrared Spectra of Ethyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide/Water Mixtures: A View from Molecular Dynamics Simulations

Simulations of ab initio molecular dynamics have been performed for mixtures of ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM-TFSI) ionic liquid and water. Statistics of donors and acceptors of hydrogen bonds has revealed that with increasing water content, hydrogen bonds between EMIM cations and TFSI anions are replaced by bonds to water molecules. In the mixture of liquids, the total number of bonds (from EMIM cations or water molecules) formed by TFSI acceptors increases. IR spectra obtained from ab initio molecular dynamics trajectories are in good agreement with literature data for ionic liquid/water systems. Analysis of oscillations of individual C-H and O-H bonds has shown correlations between vibrational frequencies and hydrogen bonds formed by an EMIM cation or water molecule and has indicated that the changes in the IR spectrum result from the decreased number of water-water hydrogen bonds in the mixture. The tests of DFTB methodology with tailored parameterizations have yielded reasonably good description of the IR spectrum of bulk water, whereas available parameterizations have failed in satisfactory reproduction of the IR spectrum of EMIM-TFSI/water mixtures in the region above 3000 cm^-1.

A quantum chemical molecular dynamics repository of solvated ions

The importance of ion-solvent interactions in predicting specific ion effects in contexts ranging from viral activity through to electrolyte viscosity cannot be underestimated. Moreover, investigations of specific ion effects in nonaqueous systems, highly relevant to battery technologies, biochemical systems and colloid science, are severely limited by data deficiency. Here, we report IonSolvR – a collection of more than 3,000 distinct nanosecond-scale ab initio molecular dynamics simulations of ions in aqueous and non-aqueous solvent environments at varying effective concentrations. Density functional tight binding (DFTB) is used to detail the solvation structure of up to 55 solutes in 28 different protic and aprotic solvents. DFTB is a fast quantum chemical method, and as such enables us to bridge the gap between efficient computational scaling and maintaining accuracy, while using an internally-consistent simulation technique. We validate the database against experimental data and provide guidance for accessing individual IonSolvR records.

Measurement(s)	solvation structure
Technology Type(s)	quantum chemistry computational method • Molecular Dynamics

A new active learning approach for adsorbate-substrate structural elucidation in silico

Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C₆₀@TiO₂ anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB).

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH₂ as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters

In this work, we explore the possibility of using computationally inexpensive electronic structure methods, such as semiempirical and DFTB calculations, for the search of the global minimum (GM) structure of chemical systems. The basic prerequisite that these inexpensive methods will need to fulfill is that their lowest energy structures can be used as starting point for a subsequent local optimization at a benchmark level that will yield its GM. If this is possible, one could bypass the global optimization at the expensive method, which is currently impossible except for very small molecules. Specifically, we test our methods with clusters of second row elements including systems of several bonding types, such as alkali, metal, and covalent clusters. The results reveal that the DFTB3 method yields reasonable results and is a potential candidate for this type of applications. Even though the DFTB2 approach using standard parameters is proven to yield poor results, we show that a re-parametrization of only its repulsive part is enough to achieve excellent results, even when applied to larger systems outside the training set.

Simulation of Liquids with the Tight-Binding Density-Functional Approach and Improved Atomic Charges

Theoretical description of liquids, especially liquid water, is an ongoing subject with important implications in various domains such as homogeneous catalysis; solvation of molecular, ionic, and biomolecular species; and reactivity. Various formalisms exist to describe liquids, each one displaying its own balance between accuracy and computational cost that defines its range of applications. The present article revisits the ability of the density-functional-based tight-binding (SCC-DFTB) approach to model liquids by focusing on liquid water and liquid benzene under ambient conditions. To do so, we benchmark a recent correction for the SCC-DFTB atomic charges that allows for a drastic improvement of the pair radial distribution functions of liquid water as compared to both experimental data and density-functional theory results performed in the generalized-gradient approximation. We also report the coupling of the deMonNano and i-PI codes to perform path-integral molecular dynamics. This allows us to rationalize the impact of nuclear quantum effects on the SCC-DFTB description of liquid water. This study evidences the rather good ability of SCC-DFTB to describe liquid water and liquid benzene. As the first example of application, we also present results for a benzene molecule solvated in water with the perspectives of further studies devoted to solvent/water interfaces.