Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we benchmark a quantum-classical hybrid solver tailored for particle-hole symmetric AIMs, using the variational quantum eigensolver to prepare the ground state of the model with shallow quantum circuits. The solver uses shallow quantum ansätze and one set of variational parameters to prepare the ground state and its particle and hole excitations, enabling the construction of the impurity Green’s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment correction to the variational energies, and benchmark the approach by comparing the density of states computed from the impurity Green’s function against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green’s function construction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

Quantitative simulation of trivalent f-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac3+–DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CNAc,Ow≈1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CNAc,Ow≈1 and CNAc,Ow≈2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac3+–DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

Metastable‐state photoacid (mPAH) has become a common tool for controlling and driving chemical processes with light. mPAHs with fast reverse reactions are desirable for precise temporal control or generating quick pulses of proton concentration. In this work, different approaches towards fast reversing mPAHs are studied. Experimental and computational results showed that stabilizing the charge–transfer intermediate is an effective way to increase the rate. A novel mPAH with a reverse reaction ≈500 times faster than the most used mPAH in methanol is developed. Another water‐soluble mPAH showed a reverse reaction with a rate constant of 7.8 s −1 , which is the fastest ever reported. The half‐life of the acidic state is calculated to be 89 ms, which allowed to demonstrate sub‐second switching using this mPAH.

In this study, we investigate the reliability of cluster perturbation (CP) theory applied to the calculation of electronically excited states through a comprehensive benchmark. In CP theory, perturbative corrections are added to the properties of a parent excitation space, which converge toward the properties of a target excitation space. For the CPS(D-n) model, perturbative corrections through order n are added to the coupled cluster singles (CCS) excitation energies to target the coupled cluster singles and doubles (CCSD) excitation energies. Through a comparative analysis of excitation energy calculations across a diverse set of molecules and wavefunction methods, we present a comprehensive evaluation of the accuracy of the third-order CPS(D) model, CPS(D-3), in calculating excitation energies. Our findings demonstrate that CPS(D-3) is a reliable alternative to established methods, particularly CCSD, while systematically overestimating the excitation energies compared to high-level coupled cluster methods such as CC3. These results highlight the strengths and limitations of CPS(D-3), as well as the promising directions for its future development.

The linear scaling divide-expand-consolidate (DEC) framework is expanded to include unrestricted Hartree-Fock references. By partitioning the orbital space and employing local molecular orbitals, the full molecular calculation can be performed as independent calculations on individual fragments, making the method well-suited for massively parallel implementations. This approach also incorporates error control through the fragment optimization threshold (FOT), which maintains precision and consistency throughout the calculations. A benchmark was conducted for correlation energies of open-shell systems and the relative energies of both open- and closed-shell molecules at the MP2 level of theory. The full calculation result is achieved as the FOT approaches zero. For correlation energies, an FOT of 10(-3) is sufficient to recover over 98% of the full result in all cases. However, for relative energies and the electronic energy component of oxidation potentials, a tighter FOT of 10(-4) is required to keep the DEC error within 10% for both open- and closed-shell molecules. This is likely due to a lack of systematic error cancellation for the molecules with vastly different chemical natures. Therefore, for accurate relative energies, the FOT should be an order of magnitude lower, and additional caution is needed, particularly for large systems. The DEC method extension to unrestricted references maintains favorable features of linear scaling and can be implemented in a massively parallel algorithm to calculate correlation energies for large open-shell systems.

Lanthanide rare-earth metals are ubiquitous in modern technologies 1-5 , but we know little about chemistry of the 61st element, promethium (Pm) 6 , a lanthanide that is highly radioactive and inaccessible. Despite its importance 7,8 , Pm has been conspicuously absent from the experimental studies of lanthanides, impeding our full comprehension of the so-called lanthanide contraction phenomenon: a fundamental aspect of the periodic table that is quoted in general chemistry textbooks. Here we demonstrate a stable chelation of the 147Pm radionuclide (half-life of 2.62 years) in aqueous solution by the newly synthesized organic diglycolamide ligand. The resulting homoleptic PmIII complex is studied using synchrotron X-ray absorption spectroscopy and quantum chemical calculations to establish the coordination structure and a bond distance of promethium. These fundamental insights allow a complete structural investigation of a full set of isostructural lanthanide complexes, ultimately capturing the lanthanide contraction in solution solely on the basis of experimental observations. Our results show accelerated shortening of bonds at the beginning of the lanthanide series, which can be correlated to the separation trends shown by diglycolamides 9-11 . The characterization of the radioactive PmIII complex in an aqueous environment deepens our understanding of intra-lanthanide behaviour 12-15 and the chemistry and separation of the f-block elements 16 . Stable chelation of the 147Pm radionuclide in aqueous solution by the newly synthesized organic diglycolamide ligand is demonstrated and the resulting complex studied, showing accelerated shortening of bonds at the beginning of the lanthanide series.

Scientific productivity can be enhanced through workflow management tools, relieving large High Performance Computing (HPC) system users from the tedious tasks of scheduling and designing the complex computational execution of scientific applications. This paper presents a study on the usage of ensemble workflow tools to accelerate science using the Summit and Frontier supercomputing systems. The research aims to connect science domain simulations using Oak Ridge Leadership Computing Facility (OLCF) supercomputing platforms with ensemble workflow methods in order to accelerate HPC-enabled discovery and boost scientific impact. We present the coupling, porting and optimization of Radical-Cybertools on three applications: Chroma, NAMD and LAMMPS. The tools augment traditional HPC monolithic runs with a pilot scheduler. Lessons-learned are discussed for physics, biology and materials science applications. We discuss intrinsic limitations of coupling and porting ensemble workflow tools to applications that run on large HPC systems. The origins of technical challenges and their solutions developed during the implementation process are discussed. Data management strategies, OLCF’s policies for ensembles, and natively supported workflow tools are also summarized. 1

The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF) theory, deliver the desired accuracy, but have impractical scaling for modeling biosystems with thousands of atoms. Combining molecular fragmentation with MP2 perturbation theory, this study presents an innovative approach that enables biomolecular-scale ab initio molecular dynamics (AIMD) simulations at WF theory level. Leveraging the resolution-of-the-identity approximation for Hartree-Fock and MP2 gradients, our approach eliminates computationally intensive four-center integrals and their gradients, while achieving near-peak performance on modern GPU architectures. The introduction of asynchronous time steps minimizes time step latency, overlapping computational phases and effectively mitigating load imbalances. Utilizing up to 9,400 nodes of Frontier and achieving 59% (1006.7 PFLOP/s) of its double-precision floating-point peak, our method enables us to break the million-electron and 1 EFLOP/s barriers for AIMD simulations with quantum accuracy.

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory-a linear-scaling, massively parallel framework-as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

The advent of Exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems.

We present here a massively parallel implementation of the recently developed CPS(D-3) excitation energy model that is based on cluster perturbation theory. The new algorithm extends the one developed in Baudin et al. [J. Chem. Phys., 150, 134110 (2019)] to leverage multiple nodes and utilize graphical processing units for the acceleration of heavy tensor contractions. Furthermore, we show that the extended algorithm scales efficiently with increasing amounts of computational resources and that the developed code enables CPS(D-3) excitation energy calculations on large molecular systems with a low time-to-solution. More specifically, calculations on systems with over 100 atoms and 1000 basis functions are possible in a few hours of wall clock time. This establishes CPS(D-3) excitation energies as a computationally efficient alternative to those obtained from the coupled-cluster singles and doubles model.

Inhibition of the SARS-CoV-2 main protease (MPpro) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of MPpro. Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 mu M. Among these, four compounds with IC50 values around 1 mu M were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Roomtemperature X-ray structures of co-crystallized protein-inhibitor complexes were determined up to 1.9 angstrom resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for MPpro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

The dissimilatory sulfite reductase enzyme has very characteristic active site where the substrate binds to an iron site, ligated by a siroheme macrocycle and a thiol directly connected to a [Fe4S4] cluster. This arrangement gives the enzyme remarkable efficiency in reducing sulfite and nitrite all the way to hydrogen sulfide and ammonia. For the first time we present a theoretical study where substrate binding modalities and activation are elucidated using active site models containing proton supply side chains and the [Fe4S4] cluster. Density functional theory (DFT) was deployed in conjunction with the energy decomposition scheme (as implemented in AMS), the quantum theory of atoms in molecules (QTAIM), and conceptual DFT (cDFT) descriptors. We quantified the role of the electrostatic interactions inside the active site created by the side chains as well as the influence of the [Fe4S4] cluster on the substrate binding. Furthermore, using conceptual DFT results we shed light of the activation process, thus, laying foundation for further mechanistic studies. We found that the bonding of the ligands to the iron complex is dominated by electrostatic interactions, but the presence of the [Fe4S4] cubane leads to substantial changes in electronic interaction. The spin state of the cubane, however, affects the binding energy only marginally. The conceptual DFT results show that the presence of the [Fe4S4] cubane affects the reactivity of the active site as it is involved in electron transfer. This is corroborated by an increase in the electrophilicity index, thus making the active site more prone to react with the ligands. The interaction energies between the ligand and the siroheme group are also increased upon the presence of the cubane group, thus, suggesting that the siroheme group is not an innocent spectator but plays an active role in the reactivity of the dSIR active site.

Mn cage complexes are rare, and the ones successfully isolated in the solid state are not stable in water and organic solvents. Herein, we present the first report of mononuclear Mn clathrochelates, in which the encapsulated metal exists in the oxidation state +4. The complexes are extremely stable in the crystalline state and in solutions and show rich redox chemistry.

High-performance computing (HPC) increasingly relies on heterogeneous architectures to achieve higher performance. In the Oak Ridge Leadership Facility (OLCF), Oak Ridge, TN, USA, this trend continues as its latest supercomputer, Summit, entered production in early 2019. The combination of IBM POWERS CPU and NVIDIA V100 GPU, along with a fast NVLink2 interconnect and other latest technologies, pushes system performance to a new height and breaks the exascale barrier by certain measures. Due to Summit powerful GPUs and much higher GPU-CPU ratio, offloading to accelerators becomes a requirement for any application, which intends to effectively use the system. To facilitate navigating a complex landscape of competing heterogeneous architectures, a collection of applications from a wide spectrum of scientic domains is selected for early adoption on Summit. In this article, the experience and lessons learned are summarized, in the hope of providing useful guidance to address new programming challenges, such as scalability, performance portability, and software maintainability, for future application development efforts on heterogeneous HPC systems.

The cluster perturbation series, CPS(D), for coupled cluster singles and doubles excitation energies is considered. It is demonstrated that the second-order model CPS(D-2) is identical to the configuration interaction singles with perturbative doubles, CIS(D) model. The third-order model, CPS(D-3), provides excitation energies of coupled cluster singles and doubles (CCSD) quality in the sense that the difference between CPS(D-3) and CCSD excitation energies is of the same size or smaller than the effect of adding triples corrections to CCSD excitation energies. We further show that the third-order corrections can be efficiently implemented, in particular, when the resolution of the identity approximation is used for integrals. We also show that the CPS(D-3) excitation energies can be determined for system sizes that are far beyond what can be considered in conventional CCSD excitation energy calculations.

The implementation and evaluation of a multilayer extension of the divide-expand-consolidate (DEC) scheme within the LSDalton program is presented. The DEC scheme is a linear-scaling, fragmentation-based local coupled-cluster (CC) method that provides a means of overcoming the scaling wall associated with canonical CC electronic structure calculations on large molecular systems. Taking advantage of the local nature of correlation effects, the correlation energy for the full molecule is calculated from a set of independent fragments using localized molecular orbitals. However, when only a small subsystem of a larger system is of interest, for example, adsorption sites or catalytically active sites, the majority of the computational time may be spent evaluating the correlation energy of fragments which have little effect on the properties in the area of interest (AOI). The multilayer DEC (ML-DEC) scheme addresses this by taking advantage of the independent nature of the fragments in order to evaluate the correlation energy of various regions of the system at different levels of theory. Regions far from the AOI are evaluated at lower (cheaper) levels of theory such as Hartree-Fock (HF) or Møller-Plesset second-order perturbation theory (MP2), while the area immediately surrounding the AOI is treated with a higher level CC model. Through the ML-DEC scheme, the computational cost of CC calculations on these types of systems can be significantly reduced while maintaining the accuracy of higher-level calculations. Results from HF/RI-MP2 and RI-MP2/CCSD ML-DEC calculations of the binding energy of a fatty acid dimer are presented. We find that the ML-DEC scheme is capable of reproducing DEC energy differences at a target level of theory, provided that the region treated at the target level of theory is chosen to be sufficiently large. Time-to-solution is found to be significantly reduced, particularly in the RI-MP2/CCSD calculations. Finally, the ML-DEC scheme is applied to the calculation of CO2 adsorption in a Mg-MOF-74 channel.