Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases

While density functional theory (DFT) is often an accurate and efficient methodology for evaluating molecular properties such as energies and multipole moments, this approach often yields larger errors for response properties such as the dipole polarizability (alpha), which describes the tendency of a molecule to form an induced dipole moment in the presence of an electric field. In this work, we provide static alpha tensors (and other molecular properties such as total energy components, dipole and quadrupole moments, etc.) computed using quantum chemical (QC) and DFT methodologies for all 7,211 molecules in the QM7b database. We also provide the same quantities for the 52 molecules in the AlphaML showcase database, which includes the DNA/RNA nucleobases, uncharged amino acids, several open-chain and cyclic carbohydrates, five popular pharmaceutical molecules, and 23 isomers of C8Hn. All QC calculations were performed using linear-response coupled-cluster theory including single and double excitations (LR-CCSD), a sophisticated approach for electron correlation, and the d-aug-cc-pVDZ basis set to mitigate basis set incompleteness error. DFT calculations employed the B3LYP and SCAN0 hybrid functionals, in conjunction with d-aug-cc-pVDZ (B3LYP and SCAN0) and d-aug-cc-pVTZ (B3LYP).

Applications of Artificial Intelligence to Computational Chemistry

Nicholas John Browning

The calculation of the electronic structure of chemical systems, necessitates computationally expensive approximations to the time-independent electronic Schrödinger equation in order to yield static properties in good agreement with experimental results. These methods can also be coupled with molecular dynamics, to provide a first principles description of thermodynamic properties, dynamics and chemical reactions. Evidently, the cost of the underlying electronic structure method limits the time frame over which a system can be studied, and hence, certain chemical processes may be out-of-reach using a particular method. Furthermore, when one is interested in designing new molecules with interesting properties, a systematic enumeration of an inordinately large chemical space is typically required. The combination of both expensive electronic structure calculations and large chemical spaces results in an insurmountable barrier in computational cost. The application of artificial intelligence (AI) in computational chemistry has, over the past 20 years, seen an explosion in interest and scope with respect to these two issues. Intelligent algorithms capable of efficiently sampling chemical spaces, coupled with machine learning (ML) techniques to cheapen the calculation of electronic structure evaluations, enable both rapid throughput to search for new molecules with particular properties and in the case of ML, an increase in the timescales that can be simulated via molecular dynamics. In this thesis, computer programs have been developed that enable the application of AI algorithms to chemical and biological problems. In particular, a versatile evolutionary algorithm toolbox called EVOLVE has been developed. As a first test-case study, genetic algorithms were used to efficiently sample the vast chemical sequence space of an isolated ¿-helical peptide, from which insights are gained to rationalise the stability of particular genetically optimised peptides in a variety of implicit solvent environments. Genetic algorithms were then applied to the compositional optimisation of training sets used in machine learning models of molecular properties. The resulting optimal training sets are shown to significantly reduce out-of-sample errors on all thermodynamic and electronic properties considered. Furthermore, they reveal that there are systematic trends in the distribution of these optimally-representative molecules. Inspired by the success of machine learning, an ML-enhanced multiple time step approach for performing accurate ab initio molecular dynamics was developed. Two schemes representing different force partitioning were investigated. In the first scheme, the ML method provides an estimation of the slow (high level) force components acting on a system, while in the second, the ML forces are added to the fast (low level) components and a high level ab initio method is used to correct the error induced by the ML model. In both schemes significant overall speedups are obtained with respect to standard Velocity-Verlet integration, all-the-while maintaining the accuracy of the high level ab initio method.

EPFL2019

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Frédéric Célerse, Veronika Juraskova, Rubén Laplaza Solanas

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl−–THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

2022

Applications of Artificial Intelligence to Computational Chemistry

Nicholas John Browning

EPFL2019