Computational screening methodology identifies effective solvents for CO2 capture.

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO2 emissions from fossil fuel-driven power and industrial plants. The industry standard technology for CO2 capture is chemical absorption with aqueous alkanolamines, which are often being mixed with an activator, piperazine, to increase the overall CO2 absorption rate. Inefficiency of the process due to the parasitic energy required for thermal regeneration of the solvent drives the search for new tertiary amines with better kinetics. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO2 faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

Quantitative Kinetic Model of CO2 Absorption in Aqueous Tertiary Amine Solvents.

Aqueous tertiary amine solutions are increasingly used in industrial CO2 capture operations because they are more energy-efficient than primary or secondary amines and demonstrate higher CO2 absorption capacity. Yet, tertiary amine solutions have a significant drawback in that they tend to have lower CO2 absorption rates. To identify tertiary amines that absorb CO2 faster, it would be efficacious to have a quantitative and predictive model of the rate-controlling processes. Despite numerous attempts to date, this goal has been elusive. The present computational approach achieves this goal by focusing on the reaction of CO2 with OH- forming HCO3-. The performance of the resulting model is demonstrated for a consistent experimental data set of the absorption rates of CO2 for 24 different aqueous tertiary amine solvents. The key to the new model's success is the manner in which the free energy barrier for the reaction of CO2 with OH- is evaluated from the differences among the solvation free energies of CO2, OH-, and HCO3-, while the pKa of the amines controls the concentration of OH-. These solvation energies are obtained from molecular dynamics simulations. The experimental value of the free energy of reaction of CO2 with pure water is combined with information about measured rates of absorption of CO2 in an aqueous amine solvent in order to calibrate the absorption rate model. This model achieves a relative accuracy better than 0.1 kJ mol-1 for the free energies of activation for CO2 absorption in aqueous amine solutions and 0.07 g L-1 min-1 for the absorption rate of CO2. Such high accuracies are necessary to predict the correct experimental ranking of CO2 absorption rates, thus providing a quantitative approach of practical interest.

Highly efficient evaluation of diffusion networks in Li ionic conductors using a 3D-corrugation descriptor.

A highly efficient computational approach for the screening of Li ion conducting materials is presented and its performance is demonstrated for olivine-type oxides and thiophosphates. The approach is based on a topological analysis of the electrostatic (Coulomb) potential obtained from a single density functional theory calculation augmented by a Born-Mayer-type repulsive term between Li ions and the anions of the material. This 3D-corrugation descriptor enables the automatic determination of diffusion pathways in one, two, and three dimensions and reproduces migration barriers obtained from density functional theory calculations using nudged elastic band method within approximately 0.1 eV. Importantly, it correlates with Li ion conductivity. This approach thus offers an efficient tool for evaluating, ranking, and optimizing materials with high Li-ion conductivity.

Ab initio parameterization of a charge optimized many-body forcefield for Si-SiO2: Validation and thermal transport in nanostructures.

In an effort to extend the reach of current ab initio calculations to simulations requiring millions of configurations for complex systems such as heterostructures, we have parameterized the third-generation Charge Optimized Many-Body (COMB3) potential using solely ab initio total energies, forces, and stress tensors as input. The quality and the predictive power of the new forcefield are assessed by computing properties including the cohesive energy and density of SiO2 polymorphs, surface energies of alpha-quartz, and phonon densities of states of crystalline and amorphous phases of SiO2. Comparison with data from experiments, ab initio calculations, and molecular dynamics simulations using published forcefields including BKS (van Beest, Kramer, and van Santen), ReaxFF, and COMB2 demonstrates an overall improvement of the new parameterization. The computed temperature dependence of the thermal conductivity of crystalline alpha-quartz and the Kapitza resistance of the interface between crystalline Si(001) and amorphous silica is in excellent agreement with experiment, setting the stage for simulations of complex nanoscale heterostructures.

Ab initio calculations for industrial materials engineering: successes and challenges.

Computational materials science based on ab initio calculations has become an important partner to experiment. This is demonstrated here for the effect of impurities and alloying elements on the strength of a Zr twist grain boundary, the dissociative adsorption and diffusion of iodine on a zirconium surface, the diffusion of oxygen atoms in a Ni twist grain boundary and in bulk Ni, and the dependence of the work function of a TiN-HfO(2) junction on the replacement of N by O atoms. In all of these cases, computations provide atomic-scale understanding as well as quantitative materials property data of value to industrial research and development. There are two key challenges in applying ab initio calculations, namely a higher accuracy in the electronic energy and the efficient exploration of large parts of the configurational space. While progress in these areas is fueled by advances in computer hardware, innovative theoretical concepts combined with systematic large-scale computations will be needed to realize the full potential of ab initio calculations for industrial applications.