Embedded Mean-Field Theory for Solution-Phase Transition-Metal Polyolefin Catalysis

Abstract

Decreasing the wall-clock time of quantum mechanics/molecular mechanics (QM/MM) calculations without sacrificing accuracy is a crucial prerequisite for widespread simulation of solution-phase dynamical processes. In this work, we demonstrate the use of embedded mean-field theory (EMFT) as the QM engine in QM/MM molecular dynamics (MD) simulations to examine polyolefin catalysts in solution. We show that employing EMFT in this mode preserves the accuracy of hybrid-functional DFT in the QM region, while providing up to 20-fold reductions in the cost per SCF cycle, thereby increasing the accessible simulation timescales. We find that EMFT reproduces DFT-computed binding energies and optimized bond lengths to within chemical 1

Informing geometric deep learning with electronic interactions to accelerate quantum chemistry

“…Despite only using readily available small-molecule libraries as training data, OrbNet-Equi offers an accuracy alternative to DFT methods on comprehensive main-group quantum chemistry benchmarks at a computation speed on par with semiempirical methods, thus offering a possible replacement for conventional ab initio simulations for general-purpose downstream applications. For example, OrbNet-Equi could immediately facilitate applications, such as screening electrochemical properties of electrolytes for the design of flow batteries (43) and performing accurate direct or hybrid QM/MM simulations for reactions in transition-metal catalysis (42,79). The method can also improve the modeling for complex reactive biochemical processes (80) using multiscale strategies that have been demonstrated in our previous study (44), while conventional ab initio reference calculations can be prohibitively expensive even on a minimal subsystem.…”

“…As a particular case study, we found that OrbNet-Equi/SDC21 substantially improved the prediction accuracy of ionization potentials relative to semiempirical QM methods, even though no radical species were included for training. Thus, our method has the potential to accelerate simulations for challenging problems in organic synthesis (42), battery design (43), and molecular biology (44). Detailed data analysis pinpoints viable future directions to systematically improve its chemical space coverage, opening a plausible pathway toward a generic hybrid physics-ML paradigm for the acceleration of molecular modeling and discovery.…”

Efficiently computing excitations of complex systems: linear-scaling time-dependent embedded mean-field theory in implicit solvent

“…23 Among other advantages over other quantum embedding schemes, EMFT is a mean-field theory, like density functional theory (DFT), so many existing methods that have been built on the foundations of DFT can be easily modified to accommodate EMFT. EMFT has been successfully used several times since its proposal, [24][25][26][27][28][29] largely focused on relatively small molecular systems. In a previous publication, 30 however, the author and co-workers extended the applicability of EMFT to large-scale periodic systems by presenting a novel combination of EMFT and linear-scaling DFT in the code onetep.…”

Efficiently Computing Excitations of Complex Systems: Linear-Scaling Time-Dependent Embedded Mean-Field Theory in Implicit Solvent

“…One of the advantages of EMFT over other quantum embedding schemes is that it is a mean-field theory, like density functional theory (DFT), so many existing methods that have been built on the foundations of DFT can be easily modified to accommodate EMFT. EMFT has been successfully used several times since its proposal, − largely focused on relatively small molecular systems. In a previous publication, however, the author and co-workers extended the applicability of EMFT to large-scale periodic systems by presenting a novel combination of EMFT and linear-scaling DFT in the code ONETEP .…”