Wiley Interdisciplinary Reviews: Computational Molecular Science

DiRisio, Ryan J.; Finney, Jacob M.; McCoy, Anne B.

doi: 10.1002/wcms.1615pmid: N/A

Diffusion quantum Monte Carlo (DMC) provides a powerful approach for obtaining the ground state energy and wave function of molecules, ions, and molecular clusters. The approach is uniquely well suited for studies of fluxional molecules, which undergo large amplitude vibrational motions even in their ground state. In contrast to the electronic structure problem, where the wave function must be antisymmetric with respect to exchange of any pair of electrons, the wave function for the ground vibrational state is nodeless. This greatly simplifies the application of DMC for vibrational problems. Because there is not a single potential function that can be used to describe the intramolecular and intermolecular interactions in all molecular systems, most methods that are used to describe nuclear quantum effects rely on a carefully chosen zero‐order description of the molecular vibrations. In contrast, DMC calculations can be performed in Cartesian coordinates, making the DMC algorithm easily transferable between different chemical systems. In this contribution, the theory that underlies DMC will be discussed along with important considerations for performing DMC calculations. Extensions for evaluating vibrationally excited states and molecular properties are also discussed. Insights that can be obtained from DMC calculations are illustrated in the context of the protonated water clusters.

Iribarren, Iñigo; Garcia, Marianne Rica; Trujillo, Cristina

doi: 10.1002/wcms.1616pmid: N/A

The field of organocatalysis, more specifically asymmetric organocatalysis, is continuously expanding having grown significantly over the recent years. However, despite this exponential expansion, the ability to determine with any degree of certainty the reaction mechanisms of these types of reactions fails to keep within pace. Due to increasing calculation capacity and methods accuracy, computational methodologies have been established as an essential approach in both a predictive and supportive role to aid the synthetic design of novel catalysts by enabling the prediction of catalytic behaviour. This review is focused on the computationally‐led catalyst design within asymmetric organocatalysis, discussing the different theoretical approaches most commonly utilised.

He, Xin; Wu, Baihua; Shang, Youhao; Li, Bingqi; Cheng, Xiangsong; Liu, Jian

doi: 10.1002/wcms.1619pmid: N/A

We report recent progress on the phase space formulation of quantum mechanics with coordinate‐momentum variables, focusing more on new theory of (weighted) constraint coordinate‐momentum phase space for discrete‐variable quantum systems. This leads to a general coordinate‐momentum phase space formulation of composite quantum systems, where conventional representations on infinite phase space are employed for continuous variables. It is convenient to utilize (weighted) constraint coordinate‐momentum phase space for representing the quantum state and describing nonclassical features. Various numerical tests demonstrate that new trajectory‐based quantum dynamics approaches derived from the (weighted) constraint phase space representation are useful and practical for describing dynamical processes of composite quantum systems in the gas phase as well as in the condensed phase.

Casadio, Rita; Martelli, Pier Luigi; Savojardo, Castrense

doi: 10.1002/wcms.1618pmid: N/A

Proteins are “social molecules.” Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein‐aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein‐binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never‐seen‐before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML‐computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind data sets. Results indicate that the state‐of‐the‐art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI‐functional interactions.

Teimouri, Hamid; Kolomeisky, Anatoly B.

doi: 10.1002/wcms.1612pmid: N/A

All chemical processes exhibit two main universal features. They are stochastic because chemical reactions might happen only after random successful collisions of reacting species, and they are dynamic because the amount of reactants and products changes with time. Since biological processes rely heavily on specific chemical reactions, the stochasticity and dynamics are also crucial features for all living systems. To understand the molecular mechanisms of chemical and biological processes, it is important to develop and apply theoretical methods that fully incorporate the randomness and dynamic nature of these systems. In recent years, there have been significant advances in formulating and exploring such theoretical methods. As an illustration of such developments, in this review the recent applications of stochastic kinetic models for various biological processes are discussed. Specifically, we focus on applying these theoretical approaches to investigate the biological signaling, clearance of bacteria under antibiotics, T cells activation in the immune system, and cancer initiation dynamics. The main advantage of the presented stochastic kinetic models is that they generally can be solved analytically, allowing to clarify the underlying microscopic picture, as well as to explain the existing experimental observations and to make new testable predictions. This theoretical approach becomes a powerful tool in uncovering the molecular mechanisms of complex natural phenomena.

Kirk, Steven R.; Jenkins, Samantha

doi: 10.1002/wcms.1611pmid: N/A

Next generation quantum theory of atoms in molecules (NG‐QTAIM) is currently the only vector‐based quantum chemical theory as all other quantum chemical theories are scalar‐based. NG‐QTAIM can, for instance, be used to distinguish enantiomers, isotopomers undergoing normal modes of vibration, predict ring‐opening reaction products, ground and excited states at a conical intersection and predict reaction pathways of permutation‐inversion isomers. As a consequence, NG‐QTAIM can uniquely be used to investigate iso‐energetic phenomena where the reliance on differences in geometric measures is removed. NG‐QTAIM has already been used to determine molecular chirality consistent with the Cahn–Ingold–Prelog (CIP) classifications for chiral molecules in addition to the reactant and product of a chiral SN2 reaction. The CIP classifications however, cannot quantify the chirality of intermediate reaction structures due to their formally achiral nature. We identify the presence of chirality by quantifying a helical response of the bond electrons consistent with photoexcitation circular dichroism experiments that utilize the helical motion of the bound electrons for chiral discrimination. NG‐QTAIM can also be used to provide nongeometric based reasoning as to why there are stronger hydrogen‐bonds in cyclic water clusters compared with compact water clusters. Additionally, the effects of electric‐fields and laser irradiation in conditions not amenable to explanation using scalar measures are quantified. The QuantVec software is introduced, which coordinates external programs and efficiently computes the NG‐QTAIM analyses using the wavefunctions from a wide range of ground and excited states computed with DFT, coupled‐cluster, complete active space SCF, configuration interaction, or quantum dynamics methods.

Shi, Yuming; Chávez, Victor H.; Wasserman, Adam

doi: 10.1002/wcms.1617pmid: N/A

From the most fundamental to the most practical side of density functional theory (DFT), Kohn–Sham inversions (iKS) can contribute to the development of functional approximations and shed light on their performance and limitations. On the one hand, iKS allows for the direct exploration of the Hohenberg–Kohn and Runge–Gross density‐to‐potential mappings that provide the foundations for DFT and time‐dependent DFT. On the other hand, iKS can guide the analysis and development of approximate exchange–correlation and noninteracting kinetic energy functionals, and diagnose their errors. iKS can also play a similar role in the development of nonadditive functionals for modern density‐based embedding methods. Various strategies to perform iKS calculations have been explored since the inception of DFT. We introduce n2v, a density‐to‐potential inversion Python module that is capable of performing the most useful and state‐of‐the‐art inversion calculations. Currently based on NumPy, n2v was developed to be easy to learn by newcomers to the field. Its structure allows for other inversion methods to be easily added. The code offers a general interface that gives the freedom to use different software packages in the computational molecular sciences (CMS) community, and the current release supports the Psi4 and PySCF packages. Six inversion methods have been implemented into n2v and are reviewed here along with detailed numerical illustrations on molecules with numbers of electrons ranging from ~10 to ~100.

Ren, Jiajun; Li, Weitang; Jiang, Tong; Wang, Yuanheng; Shuai, Zhigang

doi: 10.1002/wcms.1614pmid: N/A

The simulations of spectroscopy and quantum dynamics are of vital importance to the understanding of the electronic processes in complex systems, including the radiative/radiationless electronic relaxation relevant for optical emission, charge/energy transfer in molecular aggregates related to carrier mobility in organic materials, as well as photovoltaic and thermoelectric conversion, light‐harvesting and spin transport, and so forth. In recent years, time‐dependent density matrix renormalization group (TD‐DMRG) has emerged as a general, numerically accurate and efficient method for high‐dimensional full‐quantum dynamics. This review will cover the fundamental algorithms of TD‐DMRG in the modern framework of matrix product states (MPS) and matrix product operators (MPO), including the basic algebra with respect to MPS and MPO, the novel time evolution schemes to propagate MPS, and the automated MPO construction algorithm to encode generic Hamiltonian. Most importantly, the proposed method can handle the mixed state density matrix at finite temperature, enabling quantum statistical description for molecular aggregates. We demonstrate the performance of TD‐DMRG by benchmarking with the current state‐of‐the‐art methods for simulating quantum dynamics of the spin‐boson model and the Frenkel–Holstein(–Peierls) model. As applications of TD‐DMRG to real‐world problems, we present theoretical investigations of carrier mobility and spectral function of rubrene crystal, and the radiationless decay rate of azulene with an anharmonic potential energy surface.

doi: 10.1002/wcms.1553pmid: N/A