Chemical Calculations Research Articles

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning.

Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.

Inhibitory Potential of the Truncated Isoforms on Glutamate Transporter Oligomerization Identified by Computational Analysis of Gene-Centric Isoform Maps.

Glutamate transporters play a key role in central nervous system physiology by maintaining excitatory neurotransmitter homeostasis. Biological assemblies of the transporters, consisting of cyclic homotrimers, emerge as a crucial aspect of glutamate transporter modulation. Hence targeting heteromerization promises an effective approach for modulator design. On the other hand, the dynamic nature of transcription allows for the generation of transporter isoforms in structurally distinct manners. The potential isoforms were identified through the analysis of computationally generated gene-centric isoform maps. The conserved features of isoform sequences were revealed by computational chemistry methods and subsequent structural analysis of AlphaFold2 predictions. Truncated isoforms were further subjected to a wide range of docking analyses, 50ns molecular dynamics simulations, and evolutionary coupling analyses. Energetic landscapes of isoform-canonical transporter complexes suggested an inhibitory potential of truncated isoforms on glutamate transporter bio-assembly. Moreover, isoforms that mimic the trimerization domain (in particular, TM2 helices) exhibited stronger interactions with canonical transporters, underscoring the role of transmembrane helices in isoform interactions. Additionally, self-assembly dynamics observed in truncated isoforms mimicking canonical TM5 helices indicate a potential protective role against unwanted interactions with canonical transporters. Our computational studies on glutamate transporters offer insights into the roles of alternative splicing on protein interactions and identifies potential drug targets for physiological or pathological processes.

MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs.

In the past decade, high-throughput computational studies of materials have increased significantly mainly due to advances in computer capabilities and have attracted a great deal of interest. In the field of metal-organic frameworks (MOFs), over a million hypothetical MOFs have been designed in silico, yet only a small fraction of these have been synthesized. For validating the computational-hypothetical results and accelerating the progress in the field, there is a pressing need for distinguishing MOFs that are more likely to be synthesized for real-life applications. This study presents a comprehensive investigation into the synthesizability likelihood of MOFs, utilizing a novel computational approach based on the disparities in energy and geometry between the linker conformation within the MOF structure and its isolated, free-gas state since both of these have been proven to be critical factors influencing MOF synthesis. Our user-friendly tool streamlines synthesizability evaluation, requiring minimal expertise in computational chemistry. By deconstructing over 40,000 MOFs from databases, including QMOF, CoRE MOF, and ToBaCCo, we analyze key parameters defining the linker strain within the MOF unit cell. Our results indicate that QMOF and CoRE MOF contain more promising candidates for synthesis, while ToBaCCo exhibits a relatively poor synthesizability likelihood due to unoptimized materials. Through extensive analysis, we identify optimal linker candidates for highly synthesizable MOFs. Consistent trends in energy distribution across databases that are confirmed by high Pearson and Spearman coefficients suggest the potential for omitting optimization calculations, significantly reducing computational costs. This study underscores the importance of linker deformation and energy disparities and enhances our understanding of synthetic accessibility in MOF research, offering valuable insights for future advancements in the field.

The Paradox of Global Multireference Diagnostics.

Modern computational chemistry methods are a useful tool for modeling many chemical systems, but they are challenged by multireference species (e.g., transition metals). A variety of diagnostics have been formulated to identify such cases. They are typically developed by analyzing multireference characters of small molecules, and many provide an average picture of the entire system. We caution the use of such diagnostics for large systems because large systems may include parts with varying degrees of multireference characters. Specifically, a small but highly multireference component may yield a large error in absolute terms, which may be masked in an average value over the entire molecule. As the calculation of molecular relative energies often concerns errors in absolute terms, such a false sense of safety may be detrimental. A prospective means to tackle this challenge is to use fractional occupation density to identify potentially problematic components in a system and then examine this moiety with higher-level computations on appropriately constructed smaller models.

OpenQP: A Quantum Chemical Platform Featuring MRSF-TDDFT with an Emphasis on Open-Source Ecosystem.

The OpenQP (Open Quantum Platform) is a new open-source quantum chemistry library developed to tackle sustainability and interoperability challenges in the field of computational chemistry. OpenQP provides various popular quantum chemical theories as autonomous modules such as energy and gradient calculations of HF, DFT, TDDFT, SF-TDDFT, and MRSF-TDDFT, thereby allowing easy interconnection with third-party software. A scientifically notable feature is the innovative mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) and its customized exchange-correlation functionals such as the DTCAM series of VAEE, XI, XIV, AEE, and VEE, which significantly expand the applicability scope of DFT and TDDFT. OpenQP also supports parallel execution and is optimized with BLAS and LAPACK for high performance. Future enhancements such as extended Koopman's theorem (EKT)-MRSF-TDDFT and spin-orbit coupling (SOC)-MRSF-TDDFT will further expand OpenQP's capabilities. Additionally, a Python wrapper PyOQP is provided that performs tasks such as geometry optimization, conical intersection searches, and nonadiabatic coupling calculations, among others, by prototyping the modules of the OpenQP library in combination with third-party libraries. Overall, OpenQP aligns with modern trends in high-performance scientific software development by offering flexible prototyping and operation while retaining the performance benefits of compiled languages like Fortran and C. They enhance the sustainability and interoperability of quantum chemical software, making OpenQP a crucial platform for accelerating the development of advanced quantum theories like MRSF-TDDFT.

Small tensor product distributed active space (STP-DAS) framework for relativistic and non-relativistic multiconfiguration calculations: Scaling from 109 on a laptop to 1012 determinants on a supercomputer

Despite the power and flexibility of configuration interaction (CI) based methods in computational chemistry, their broader application is limited by an exponential increase in both computational and storage requirements, particularly due to the substantial memory needed for excitation lists that are crucial for scalable parallel computing. The objective of this work is to develop a new CI framework, namely, the small tensor product distributed active space (STP-DAS) framework, aimed at drastically reducing memory demands for extensive CI calculations on individual workstations or laptops, while simultaneously enhancing scalability for extensive parallel computing. Moreover, the STP-DAS framework can support various CI-based techniques, such as complete active space (CAS), restricted active space, generalized active space, multireference CI, and multireference perturbation theory, applicable to both relativistic (two- and four-component) and non-relativistic theories, thus extending the utility of CI methods in computational research. We conducted benchmark studies on a supercomputer to evaluate the storage needs, parallel scalability, and communication downtime using a realistic exact-two-component CASCI (X2C-CASCI) approach, covering a range of determinants from 109 to 1012. Additionally, we performed large X2C-CASCI calculations on a single laptop and examined how the STP-DAS partitioning affects performance.

Computational chemistry facilitates the development of second near-infrared xanthene-based dyes.

The dyes in the second near-infrared (NIR-II) region play a crucial role in advancing imaging technology. However, developing small-molecule dyes in NIR-II poses a significant bottleneck to meet the substantial demands in biological fields, which may be attributed to the lack of a rational design strategy. Herein, we designed a series of rhodamine analogs with more red-shifted emission by replacing the oxygen-bridge atom in xanthene-based dyes with -C(CH3)2, -Si(CH3)2, -SO2, and -P(O)Ph. We investigated the frontier molecular orbital, electrostatic potential surfaces, the interaction region indicator, electron-hole distribution, and absorption and emission spectrum of xanthene-based dyes using (time-dependent) density functional theory. Our results demonstrated that these designed small molecular dyes exhibit long emission wavelengths covering 1377-1809nm. We expected these findings to enable the targeted design of long-wavelength rhodamines. Geometry optimization of dyes in the ground and excited states was carried out at ω-B97XD/Def2SVP level using Gaussian 16 A03. The absorption and emission wavelengths were evaluated using 13 functional, including TPSSH, O3LYP, B3LYP*, B3LYP, PBE0, MPW1B95, PBE-1/3, PBE38, MPWB1K, MN15, BHandHLYP, ω-B97XD, and CAM-B3LYP.

The adsorption of drugs on nanoplastics has severe biological impact

Micro- and nanoplastics can interact with various biologically active compounds forming aggregates of which the effects have yet to be understood. To this end, it is vital to characterize these aggregates of key compounds and micro- and nanoplastics. In this study, we examined the adsorption of the antibiotic tetracycline on four different nanoplastics, made of polyethylene (PE), polypropylene (PP), polystyrene (PS), and nylon 6,6 (N66) through chemical computation. Two separate approaches were employed to generate relevant conformations of the tetracycline-plastic complexes. In the first approach, we folded the plastic particle from individual polymer chains in the presence of the drug through multiple separate simulated annealing setups. In the second, more biased, approach, the neat plastic was pre-folded through simulated annealing, and the drug was placed at its surface in multiple orientations. The former approach was clearly superior to the other, obtaining lower energy conformations even with the antibiotic buried inside the plastic particle. Quantum chemical calculations on the structures revealed that the adsorption energies show a trend of decreasing affinity to the drug in the order of N66> PS> PP> PE. In vitro experiments on tetracycline-sensitive cell lines demonstrated that, in qualitative agreement with the calculations, the biological activity of tetracycline drops significantly in the presence of PS particles. Preliminary molecular dynamics simulations on two selected aggregates with each plastic served as first stability test of the aggregates under influence of temperature and in water. We found that all the selected cases persisted in water indicating that the aggregates may be stable also in more realistic environments. In summary, our data show that the interaction of micro- and nanoplastics with drugs can alter drug absorption, facilitate drug transport to new locations, and increase local antibiotic concentrations, potentially attenuating antibiotic effect and at the same time promoting antibiotic resistance.

Synthesis, Electrochemistry and Density Functional Theory of Osmium(II) Containing Different 2,2′:6′,2″-Terpyridines

In coordination chemistry, 2,2′:6′,2″-terpyridine is a versatile and extensively studied tridentate ligand. Terpyridine forms stable complexes with a variety of metal ions through coordination sites provided by the three nitrogen atoms in its pyridine rings. This paper presents an electrochemical study on various bis(terpyridine)osmium(II) complexes, addressing the absence of a systematic investigation into their redox behavior. Additionally, a computational chemistry analysis was conducted on these complexes, as well as on eight previously studied osmium(II)-bipyridine and -phenanthroline complexes, to expand both the experimental and theoretical understanding. The experimental redox potentials, Hammett constants, and DFT-calculated energies show linear correlations due to the electron-donating or electron-withdrawing nature of the substituents, as described by the Hammett constants. These substituent effects cause shifts to lower or higher redox potentials, respectively.

Molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.

The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

AI-Driven Electrolyte Additive Selection to Boost Aqueous Zn-Ion Batteries Stability.

In tackling the stability challenge of aqueous Zn-ion batteries (AZIBs) for large-scale energy storage, the adoption of electrolyte additive emerges as a practical solution. Unlike current trial-and-error methods for selecting electrolyte additives, a data-driven strategy is proposed using theoretically computed surface free energy as a stability descriptor, benchmarked against experimental results. Numerous additives are calculated from existing literature, forming a database for machine learning (ML) training. Importantly, this ML model relies solely on experimental values, effectively addressing the challenge of large solvent molecule models that are difficult to handle with quantum chemistry computation. The interpretable linear regression algorithm identifies the number of heavy atoms in the additive molecule and the liquid surface tension as key factors. Artificial intelligence (AI) clustering categorizes additive molecules, identifying regions with the most significant impact on enhancing battery stability. Experimental verification successfully confirms the exceptional performance of 1,2,3-butanetriol and acetone in the optimal region. This integrated methodology, combining theoretical models, data-driven ML, and experimental validation, provides insights into the rational design of battery electrolyte additives.

Cascade CO2 Insertion in Carbanion Ionic Liquids Driven by Structure Rearrangement.

The CO2 chemisorption in state-of-the-art sorbents based on oxide/hydroxide/amine moieties is driven by strong chemical bonding formation in the carbonate/bicarbonate/carbamate products, which in turn leads to high energy input in sorbent regeneration. In addition, the CO2 uptake capacity was limited by the active sites' utilization efficiency, with each active site incorporating one CO2 molecule or less. In this work, a new concept and generation of sorbent was developed to achieve cascade insertion of multiple CO2 molecules by leveraging structure rearrangement as the driving force, leading to in situ generation of extra CO2-binding sites and significantly reduced energy input for CO2 release. The designed ionic liquids (ILs) containing carbanions with conjugated and asymmetric structure, deprotonated (methylsulfonyl)acetonitrile ([MSA]) anion, allowed the cascade insertion of two CO2 molecules via consecutive C-C and O-C bond formations. The proton transfer and structure rearrangement of the carboxylic acid intermediates played critical roles in stabilizing the first integrated CO2 and generating extra electron-rich oxygen sites for the insertion of the second CO2. The structure variation and reaction pathway were confirmed by operando spectroscopy, magnetic resonance spectroscopy (NMR), mass spectroscopy, and computational chemistry. The energy input in sorbent regeneration could be further reduced by harnessing the phase-changing behavior of the carbanion salts in ether solutions upon reacting with CO2, avoiding the energy consumption in heating the solvent. The fundamental insights obtained herein provide a promising approach to greatly improve the CO2 sorption performance via sophisticated molecular-scale structural engineering of the sorbents.

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

Fuel-drivenπ-conjugated Superstructures to Form Transient Conductive Hydrogels.

Despite advances in creating dissipative materials with transient properties, such as hydrogels and active droplets, their application remains confined to temporal changes in structural properties. Developing out-of-equilibrium materials whose electronic functions are parameterized by a chemical reaction cycle is challenging. Yet, this class of materials is required to construct biomimetic materials. In contrast to traditional chemical reaction cycles that exploit molecularly dissolved building blocks at thermodynamic equilibrium, we show that fiber structures derived from reactive naphthalene diimide (NDI) building blocks can be used as resting states to form far-from-equilibrium conductive hydrogels after the addition of chemical fuels. Upon fueling the NDI-derived fibers, a dual-component activation and deactivation pathway is deduced by kinetic analysis and is absent when using a molecularly dissolved resting state. Investigating the solid-state morphologies of the structures formed throughout the fuel-driven reaction cycle using cryo-EM reveals that the resting thermodynamic fibers evolve to transient thicker fibrils and layered superstructures. We show that the transient redox-active hydrogels exhibit a nearly threefold increase in electrical conductivity upon fuel consumption before reverting to their original value over hours. These far-from-equilibrium materials are potential candidates in applications such as programmable biorobotics and chemical computing.

Fuel‐driven π‐conjugated Superstructures to Form Transient Conductive Hydrogels

Despite advances in creating dissipative materials with transient properties, such as hydrogels and active droplets, their application remains confined to temporal changes in structural properties. Developing out‐of‐equilibrium materials whose electronic functions are parameterized by a chemical reaction cycle is challenging. Yet, this class of materials is required to construct biomimetic materials. In contrast to traditional chemical reaction cycles that exploit molecularly dissolved building blocks at thermodynamic equilibrium, we show that fiber structures derived from reactive naphthalene diimide (NDI) building blocks can be used as resting states to form far‐from‐equilibrium conductive hydrogels after the addition of chemical fuels. Upon fueling the NDI‐derived fibers, a dual‐component activation and deactivation pathway is deduced by kinetic analysis and is absent when using a molecularly dissolved resting state. Investigating the solid‐state morphologies of the structures formed throughout the fuel‐driven reaction cycle using cryo‐EM reveals that the resting thermodynamic fibers evolve to transient thicker fibrils and layered superstructures. We show that the transient redox‐active hydrogels exhibit a nearly threefold increase in electrical conductivity upon fuel consumption before reverting to their original value over hours. These far‐from‐equilibrium materials are potential candidates in applications such as programmable biorobotics and chemical computing.

Synthesis, Characterization, Biological Potency, and Molecular Docking of Co2+, Ni2+, and Pd2+ Complexes Derived From Carbohydrazone Ligand and Its Application in Dye Removal

ABSTRACTAs a result of the Schiff base condensation reaction between carbohydrazide and 4‐aminoacetophenone, novel HL ligand [(Z)‐N′‐((Z)‐1‐(4‐aminophenyl)ethylidene)‐2‐(1‐(4‐aminophenyl)ethylidene)hydrazine‐1‐carbohydrazide] and three coordination compounds were successfully obtained with the formulas of [Co2(L)(Cl)3(H2O)].Cl, [Ni2(L)(Cl)2(H2O)2].Cl.H2O, and [Pd (HL)(Cl)(H2O)].Cl, the obtained structures were analyzed using analytical and spectroscopic techniques such as Fourier‐transform infrared (FT‐IR), NMR, UV–Vis, molar conductivity, elemental analysis, and magnetic susceptibility measurements. Additionally, thermal stabilities, kinetic, and thermodynamic parameters were estimated utilizing thermogravimetric analysis. The structures were confirmed through quantum chemical computations. The antioxidant, anticancer, and antimicrobial biological efficacies of the ligand and its metal chelates were assessed. The ligand shows optimistic results as an antioxidant, while Co2+ and Pd2+ complexes showed the highest antimicrobial activities. The DNA binding affinity and cleavage of the isolated compounds were evaluated. Furthermore, the fluorescence spectrum of ligand in the absence and presence of Co2+ was recorded in order to investigate the interaction affinity along with the limit of detection. Another application of this work is the removal of methylene blue and crystal violet dyes from wastewater and reusability, through an inventive synthesis of a cellulose‐based material “LDC” Schiff base.

AI-Guided Design of MALDI Matrices: Exploring the Electron Transfer Chemical Space for Mass Spectrometric Analysis of Low-Molecular-Weight Compounds.

The development of matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) has traditionally relied on experimental efforts. Here, we propose a Goal-Directed artificial intelligence generative model, fueled by computational chemistry calculated data, to construct a chemical space optimized for Electron Transfer (ET) processes in MALDI analysis. We utilized a group of 30 reported ET matrices, subjected to structural enumeration and molecular properties prediction using semiempirical and ab initio calculations, to establish a comprehensive database comprising diverse structural and property data. Subsequently, employing a protocol of structural enumeration with 68 canonical SMILES of Bemis-Murcko (BM) fragments, we expanded the structural complexity of the initial library. This process generated 82753 compounds organized into 10 scaffold levels, with a p50 index from the Cyclic System Retrieval (CSR) curve of scaffolds of 50%. From the resulting enumerated library, a diverse subset of structures was selected by using the Jarvis-Patrick clustering method. These structures, along with their associated properties measured from quantum mechanics and experimental data, were used to train a Machine Learning (ML) model to predict ionization energy (Ei) values. Subsequently, a Scoring Neural Network (SNN), coupled with our Goal-Directed generative model using a Recurrent Neural Network (RNN) with Deep Learning (DL) architectures, was trained. The generative model was guided using a prior network within a Reinforcement/Transfer Learning environment. The final AI-generative model learned that structures with high unsaturation, H/C ratios under 1, and molecular weights between 100 and 300 u are favorable for ET MALDI matrices, as well as those with few aromatic rings and zero aliphatic rings. Other molecular features were also favored. The resulting AI-generated library exhibits Ei values over 8.0 eV, akin to those of reported "good" ET MALDI matrices, indicating successful design with high synthesis accessibility scores. In conclusion, our generative model provided valuable insights into the molecular features ideal for ET MALDI compounds while generating a wide range of structurally diverse molecules within a similar molecular property space. The next critical step in this process is to synthesize a selection of these generated compounds for the experimental validation and further characterization.

Synthesis of new pyridine based ONNO type schiff bases and Cd(II) complexes, investigation of structural, electronic and anti-cancer properties as computationally and experimentally

Pyridine based Schiff bases and their Cd(II) complexes were synthesized and characterized by spectral techniques which are ATR-IR, 1H NMR, 13C NMR, LC/QTOF-MS, and Single Crystal XRD. Structural properties and geometric parameters of these compounds were investigated both experimental and computational analyses. Electronic properties of Schiff bases were investigated by Hirshfeld surface analyses and molecular electrostatic potential (MEP) map calculations. All experimentally obtained data were supported by computational chemistry methods. B3LYP/6–31G(d) level was used for synthesized Schiff bases while B3LYP/6–31G(d)(LANL2DZ) level was used for Cd(II) complexes. Cell viability assays were performed to determine anticancer properties of related compounds against breast adenocarcinoma (MCF-7), colorectal adenocarcinoma (HT-29), and gastric carcinoma (SNU-16) cell lines. It was found that anticancer activity increased with complexation. The synthesized molecules were found to be more effective against HT-29. Furthermore, molecular docking calculations for each compounds were done against histone deacetylase 1 (HDAC1) due to the fact that expression level of HDAC1 is acceptable range.

Tutorial on Umbrella Sampling Simulation with a Combined QM/MM Potential: The Potential of Mean Force for an SN2 Reaction in Water.

We present a tutorial to carry out umbrella-sampling free-energy simulations with a combined quantum mechanical and molecular mechanical (QM/MM) potential, which may also be used in a computational or biophysical chemistry curriculum for first-year graduate and undergraduate students. In this article, we choose the Type II SN2 Menshutkin reaction between ammonia and chloromethane to construct the potential of mean force (PMF) for the reaction in aqueous solution. In this exercise, we wish to accomplish three tasks: (1) an understanding of the concept of PMF and the umbrella-sampling free-energy simulation method, (2) the use of a combined QM/MM potential in molecular dynamics simulation of chemical reactions, and (3) an understanding of solvent effects and intermolecular interactions on chemical reactions through comparison with gas-phase results. Analysis of the simulation results allows students to appreciate the difference between the transition state along a PMF from statistical simulations versus the optimized saddle point in the gas phase, the shift of transition state with respect to solvation, and the significance of electronic polarization. Through this tutorial, students will gain the necessary skills to carry out free energy simulations of chemical reactions in solution and in enzymes both in a classroom and in the laboratory.

DMRG-Tailored Coupled Cluster Method in the 4c-Relativistic Domain: General Implementation and Application to the NUHFI and NUF3 Molecules.

Heavy atom compounds represent a challenge for computational chemistry due to the need for simultaneous treatment of relativistic and correlation effects. Often such systems also exhibit strong correlation, which hampers the application of perturbation theory or single-reference coupled cluster (CC) methods. As a viable alternative, we have proposed externally correcting the CC method using the density matrix renormalization group (DMRG) wave functions, yielding the DMRG-tailored CC method. In a previous paper [J. Chem. Phys. 2020, 152, 174107], we reported a first implementation of this method in the relativistic context, which was restricted to molecules with real double group symmetry. In this work, we present a fully general implementation of the method, covering complex and quaternion double groups as well. The 4c-TCC method thus becomes applicable to polyatomic molecules, including heavy atoms. For the assessment of the method, we performed calculations of the chiral uranium compound NUHFI, which was previously studied in the context of the enhancement of parity violation effects. In particular, we performed calculations of a cut of the potential energy surface of this molecule along the stretching of the N-U bond, where the system exhibits strong multireference character. Since there are no experimental data for NUHFI, we have performed also an analogous study of the (more symmetric) NUF3 molecule, where the vibrational frequency of the N-U bond can be compared with spectroscopic data.