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1. Efficient Mean-Field Simulation of Quantum Circuits Inspired by Density Functional Theory

   Bernardi, Marco (June 2023, arXivorg)

   Exact simulations of quantum circuits (QCs) are currently limited to ~50 qubits because the memory and computational cost required to store the QC wave function scale exponentially with qubit number. Therefore, developing efficient schemes for approximate QC simulations is a current research focus. Here we show simulations of QCs with a method inspired by density functional theory (DFT), a widely used approach to study many-electron systems. Our calculations can predict marginal single-qubit probabilities (SQPs) with over 90% accuracy in several classes of QCs with universal gate sets, using memory and computational resources linear in qubit number despite the formal exponential cost of the SQPs. This is achieved by developing a mean-field description of QCs and formulating optimal single- and two-qubit gate functionals – analogs of exchange-correlation functionals in DFT – to evolve the SQPs without computing the QC wave function. Current limitations and future extensions of this formalism are discussed. 

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2. Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules

   https://doi.org/10.1039/d2re00030j  

   Żurański, Andrzej M.; Wang, Jason Y.; Shields, Benjamin J.; Doyle, Abigail G. (May 2022, Reaction Chemistry & Engineering)

   This perspective describes Auto-QChem, an automatic, high-throughput and end-to-end DFT calculation workflow that computes chemical descriptors for organic molecules. Tailored toward users without extensive programming experience, Auto-QChem has facilitated more than 38 000 DFT calculations for 17 000 molecules as of January 2022. Starting from string representations of molecules, Auto-QChem automatically (a) generates conformational ensembles, (b) submits and manages DFT calculations on a high-performance computing (HPC) cluster, (c) extracts production-ready features that are suitable for statistical analysis and machine learning model development, and (d) stores resulting calculations in a cloud-hosted and web-accessible database. We describe in detail the design and implementation of Auto-QChem, as well as its current functionalities. We also review three case studies where Auto-QChem was applied to our recent efforts in combining data science approaches in organic chemistry methodology development: (a) the design of a diverse and unbiased aryl bromide substrate scope for a Ni/photoredox catalyzed alkylation reaction, (b) mechanistic studies on the effect of bioxazoline (BiOx) and biimidazoline (BiIm) ligands on enantioselectivity in a Ni/photoredox catalyzed cross-electrophile coupling of epoxides and aryl iodides, (c) the development of a reaction condition optimization framework using Bayesian optimization. In addition, we discuss limitations and future directions of Auto-QChem and similar automated DFT calculation systems. 

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3. Overcoming the difficulties of predicting conformational polymorph energetics in molecular crystals via correlated wavefunction methods

   https://doi.org/10.1039/C9SC05689K  

   Greenwell, Chandler; McKinley, Jessica L.; Zhang, Peiyu; Zeng, Qun; Sun, Guangxu; Li, Bochen; Wen, Shuhao; Beran, Gregory J. (February 2020, Chemical Science)

   Molecular crystal structure prediction is increasingly being applied to study the solid form landscapes of larger, more flexible pharmaceutical molecules. Despite many successes in crystal structure prediction, van der Waals-inclusive density functional theory (DFT) methods exhibit serious failures predicting the polymorph stabilities for a number of systems exhibiting conformational polymorphism, where changes in intramolecular conformation lead to different intermolecular crystal packings. Here, the stabilities of the conformational polymorphs of o -acetamidobenzamide, ROY, and oxalyl dihydrazide are examined in detail. DFT functionals that have previously been very successful in crystal structure prediction perform poorly in all three systems, due primarily to the poor intramolecular conformational energies, but also due to the intermolecular description in oxalyl dihydrazide. In all three cases, a fragment-based dispersion-corrected second-order Møller–Plesset perturbation theory (MP2D) treatment of the crystals overcomes these difficulties and predicts conformational polymorph stabilities in good agreement with experiment. These results highlight the need for methods which go beyond current-generation DFT functionals to make crystal polymorph stability predictions truly reliable. 

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4. Deriving approximate functionals with asymptotics

   https://doi.org/10.1039/D0FD00057D  

   Burke, Kieron (December 2020, Faraday Discussions)

   Modern density functional approximations achieve moderate accuracy at low computational cost for many electronic structure calculations. Some background is given relating the gradient expansion of density functional theory to the WKB expansion in one dimension, and modern approaches to asymptotic expansions. A mathematical framework for analyzing asymptotic behavior for the sums of energies unites both corrections to the gradient expansion of DFT and hyperasymptotics of sums. Simple examples are given for the model problem of orbital-free DFT in one dimension. In some cases, errors can be made as small as 10 −32 Hartree suggesting that, if these new ingredients can be applied, they might produce approximate functionals that are much more accurate than those in current use. A variation of the Euler–Maclaurin formula generalizes previous results. 

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5. Ru-promoted perovskites as effective redox catalysts for CO ₂ splitting and methane partial oxidation in a cyclic redox scheme

   https://doi.org/10.1039/D2NR04437D  

   Iftikhar, Sherafghan; Martin, William; Wang, Xijun; Liu, Junchen; Gao, Yunfei; Li, Fanxing (July 2022, Nanoscale)

   The current study reports AxA’1-xByB’1-yO3-𝛿 perovskite redox catalysts (RCs) for CO2-splitting and methane partial oxidation (POx) in a cyclic redox scheme. Strontium (Sr) and iron (Fe) were chosen as A and B site elements with A’ being lanthanum (La), samarium (Sm) or yttrium (Y), and B’ being manganese (Mn), or titanium (Ti) to tailor their equilibrium oxygen partial pressures (P_(O_2 ) s) for CO2-splitting and methane partial oxidation. DFT calculations were performed for predictive optimization of the oxide materials whereas experimental investigation confirmed the DFT predicted redox performance. The redox kinetics of the RCs improved significantly by 1 wt.% ruthenium (Ru) impregnation without affecting their redox thermodynamics. Ru impregnated LaFe0.375Mn0.625O3 (A=0, A’=La, B=Fe, and B’=Mn) was the most promising RC in terms of its superior redox performance (CH4/CO2 conversion >90% and CO selectivity~ 95%) at 800oC. Long-term redox testing over Ru impregnated LaFe0.375Mn0.625O3 indicated stable performance during the first 30 cycles following with a ~25% decrease in the activity during the last 70 cycles. Air treatment was effective to reactivate the redox catalyst. Detailed characterizations revealed the underlying mechanism for redox catalyst deactivation and reactivation. This study not only validated a DFT guided mixed oxide design strategy for CO2 utilization but also provides potentially effective approaches to enhance redox kinetics as well as long-term redox catalyst performance. 

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