Stimulated X-ray Raman and Absorption Spectroscopy of Iron-Sulfur Dimers

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems

Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)-porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies.

Chebyshev Matrix Product States with Canonical Orthogonalization for Spectral Functions of Many-Body Systems

We propose a method to calculate the spectral functions of many-body systems by Chebyshev expansion in the framework of matrix product states coupled with canonical orthogonalization (coCheMPS). The canonical orthogonalization can improve the accuracy and efficiency significantly because the orthogonalized Chebyshev vectors can provide an ideal basis for constructing the effective Hamiltonian in which the exact recurrence relation can be retained. In addition, not only the spectral function but also the excited states and eigenenergies can be directly calculated, which is usually impossible for other MPS-based methods such as time-dependent formalism or correction vector. The remarkable accuracy and efficiency of coCheMPS over other methods are demonstrated by calculating the spectral functions of spin chain and ab initio hydrogen chain. For the first time we demonstrate that Chebyshev MPS can be used to deal with ab initio electronic Hamiltonian effectively. We emphasize the strength of coCheMPS to calculate the low excited states of systems with sparse discrete spectrum. We also caution the application for electron-phonon systems with dense density of states.

Spin-Pure Stochastic-CASSCF via GUGA-FCIQMC Applied to Iron-Sulfur Clusters

In this work, we demonstrate how to efficiently compute the one- and two-body reduced density matrices within the spin-adapted full configuration interaction quantum Monte Carlo (FCIQMC) method, which is based on the graphical unitary group approach (GUGA). This allows us to use GUGA-FCIQMC as a spin-pure configuration interaction (CI) eigensolver within the complete active space self-consistent field (CASSCF) procedure and hence to stochastically treat active spaces far larger than conventional CI solvers while variationally relaxing orbitals for specific spin-pure states. We apply the method to investigate the spin ladder in iron-sulfur dimer and tetramer model systems. We demonstrate the importance of the orbital relaxation by comparing the Heisenberg model magnetic coupling parameters from the CASSCF procedure to those from a CI-only (CASCI) procedure based on restricted open-shell Hartree-Fock orbitals. We show that the orbital relaxation differentially stabilizes the lower-spin states, thus enlarging the coupling parameters with respect to the values predicted by ignoring orbital relaxation effects. Moreover, we find that, while CASCI results are well fit by a simple bilinear Heisenberg Hamiltonian, the CASSCF eigenvalues exhibit deviations that necessitate the inclusion of biquadratic terms in the model Hamiltonian.

Electronic and Structural Properties of the Double Cubane Iron-Sulfur Cluster

The double-cubane cluster (DCC) refers to an [Fe8S9] iron-sulfur complex that is otherwise only known to exist in nitrogenases. Containing a bridging µ2-S ligand, the DCC in the DCC-containing protein (DCCP) is covalently linked to the protein scaffold via six coordinating cysteine residues. In this study, the nature of spin coupling and the effect of spin states on the cluster’s geometry are investigated computationally. Using density functional theory (DFT) and a broken symmetry (BS) approach to study the electronic ground state of the system, we computed the exchange interaction between the spin-coupled spins of the four FeFe dimers contained in the DCC. This treatment yields results that are in excellent agreement with both computed and experimentally determined exchange parameters for analogously coupled di-iron complexes. Hybrid quantum mechanical (QM)/molecular mechanical (MM) geometry optimizations show that cubane cluster A closest to charged amino acid side chains (Arg312, Glu140, Lys146) is less compact than cluster B, indicating that electrons of the same spin in a charged environment seek maximum separation. Overall, this study provides the community with a fundamental reference for subsequent studies of DCCP, as well as for investigations of other [Fe8S9]-containing enzymes.

Characterization of charge transfer excited states in [2Fe–2S] iron–sulfur clusters using conventional configuration interaction techniques

The experimental UV–Vis spectra of the biologically relevant [2Fe–2S] iron–sulfur clusters feature typically three bands in the 300–800 nm range. Based on ground-state orbitals and using the one electron transition picture, these bands are said to be of charge transfer character. The key complication in the electronic structure calculations of these compounds are the antiferromagnetic coupling of the iron centers and high covalency of Fe–S bonds. Thus, the examples of the direct computations of electronically excited states of these systems are rare. Whereas low lying electronic excited states were subject of recent studies, higher energy states computed with many-body theories were never reported. In this work we present, for the first time, calculations of the electronic spectra of [Fe2S2](SMe)42− biomimetic compound. We demonstrate that spin-averaged restricted open-shell Hartree–Fock orbitals are superior to high-spin orbitals and are convenient reference for subsequent configuration interaction calculations. Moreover, the use of conventional configuration interaction methods enabled us to study the nature of the excited states in details with the difference density maps. By systematic extension of the donor orbital space we show that key excitations in the 300–800 nm range are of Fe 3d ← (μ-S) character.