Wiley Interdisciplinary Reviews: Computational Molecular Science

Zhang, Ting; Yao, Yi‐Fan; Ai, Wenna; Su, Neil Qiang

doi: 10.1002/wcms.70070pmid: N/A

Functional theories reformulate the many‐electron problem by expressing electronic properties as functionals of reduced quantities, providing efficient alternatives to wave function‐based correlation methods. Kohn‐Sham density functional theory (KS‐DFT) and reduced density matrix functional theory (RDMFT) exemplify this philosophy but remain limited by their single‐determinant nature and numerical complexity, respectively. This review presents hierarchically correlated orbital functional theory (HCOFT), a unified framework developed to overcome these limitations. By extending orbitals into tunable hypercomplex spaces and deriving hierarchically correlated orbitals (HCOs) with fractional occupations through Clifford algebra, HCOFT establishes the corresponding variational foundation and a continuous dimensional hierarchy that spans KS‐DFT, RDMFT, and the intermediate 1‐HCOFT—a third formal functional theory featuring paired HCOs that naturally capture strong correlation while maintaining computational stability. Further advances, including the explicit‐by‐implicit scheme for stable occupation optimization, the coupled optimization strategy for accelerated convergence through simultaneous orbital and occupation updates, and the development of short‐range screened, occupation‐dependent orbital functionals for balanced treatment of dynamical and strong correlation, further strengthen the practical applicability of HCOFT. By integrating mathematical rigor, algorithmic efficiency, and a flexible platform for functional construction, HCOFT provides a systematically improvable foundation for electronic‐structure modeling and offers a promising pathway toward a versatile and unifying paradigm for accurate first‐principles calculations.

Gayvert, James R.; Kranc, Alyssa J.; Tazhigulov, Ruslan N.; Bravaya, Ksenia B.

doi: 10.1002/wcms.70071pmid: 42058869

In this review we present eMap 2.0, a web‐based application for predicting electron/hole transfer pathways in proteins and protein families based on their structures. The underlying model can be viewed as a coarse‐grained version of the Pathways approach by Beratan and Onuchic [Beratan et al. J. Chem. Phys. 1987, 86, 4488]. Similar to the original framework, eMap employs graph‐theory algorithms to search for the most efficient electron transfer pathways as shortest paths on a graph representation of the protein. In eMap, the nodes represent electron transfer active sites and only through‐space tunneling is considered for each individual electron/hole hop. eMap 2.0 takes this model one step further by aiming at identifying shared electron transfer pathways in protein sets. From a graph theory standpoint, this is achieved using frequent subgraph mining (FSM) algorithms. Lastly, eMap 2.0 utilizes sequence and structural similarity measures to analyze and cluster the results. Here, we show how this robust method can be utilized to rapidly provide insights regarding conserved electron transfer pathways within protein families and to identify outliers, in which the conserved electron transfer pathway is blocked either by a mutation or conformational changes.

doi: 10.1002/wcms.70068pmid: N/A

Sun, Xiang

doi: 10.1002/wcms.70069pmid: N/A

Simulating photoinduced charge transfer (CT) in the condensed phase is essential for understanding solar energy conversion. Traditional Marcus theory is limited by its assumption of a thermally equilibrated initial state, which is often invalid for photoinduced processes, where vertical excitation creates a nonequilibrium nuclear state. The subsequent structural relaxation requires a time‐dependent rate coefficient. This review focuses on Instantaneous Marcus Theory (IMT), an approach recently developed to capture these nonequilibrium effects. Derived as the classical limit of the nonequilibrium Fermi's golden rule (NE‐FGR), IMT provides a practical, Marcus‐like expression for the time‐dependent rate based on the dynamical average and variance of the donor‐acceptor energy gap. While the direct evaluation of IMT requires computationally expensive nonequilibrium molecular dynamics, the nonlinear‐response (NLR) formulation reformulates the theory in terms of efficient equilibrium molecular dynamics simulations. This framework has been extended to multistate systems, allowing the simulation of complex reaction networks through a set of coupled Pauli's master equations. We highlight the application of these methods to the carotenoid‐porphyrin‐fullerene molecular triad, a prototypical organic photovoltaic system, dissolved in organic solvent. For this system, IMT correctly predicts a transient enhancement of the CT rate by over an order of magnitude, a nonequilibrium effect missed by Marcus theory. The population dynamics from multistate IMT are in excellent agreement with results from all‐atom nonadiabatic semiclassical mapping dynamics and quantum NE‐FGR calculations. This work establishes the multistate NLR‐IMT method as a reliable and cost‐effective tool for simulating photoinduced CT dynamics in realistic condensed‐phase systems.

Abraham, B. Moses; Gogotsi, Yury

doi: 10.1002/wcms.70067pmid: N/A

The rapid evolution of machine learning (ML) has advanced materials discovery, providing tools to explore, predict, and design materials with tailored properties. Here we present an overview of emerging ML tools for data‐driven materials innovation, including data curation, feature engineering, model development, interpretability, and inverse design. We highlight high‐throughput material databases in providing large‐scale, DFT‐computed datasets, and discuss the importance of descriptor libraries that encode compositional and structural information into machine‐readable inputs for model development. Advances in ML architectures, ranging from classical algorithms to graph neural networks, are discussed for their ability to capture complex structure–property relationships. Particular emphasis is given to inverse design frameworks using generative models and optimization strategies to enable property‐targeted materials generation. We further explore interpretability and uncertainty quantification techniques that are important for bridging ML predictions with experimental validation. Automation platforms are described as tools for closed‐loop, high‐throughput discovery pipelines. We outline grand challenges, including data sparsity, model generalizability, and experimental integration. Finally, we summarize future directions that include foundation models pre‐trained on broad, multimodal materials data; self‐supervised learning strategies to reduce dependence on labeled datasets; ML workflows that embed thermodynamic and symmetry constraints to enhance interpretability; and fully autonomous laboratories that couple ML guidance with robotic synthesis and real‐time feedback.

Gangwal, Amit; Lavecchia, Antonio

doi: 10.1002/wcms.70072pmid: N/A

Artificial intelligence (AI) is reshaping drug discovery by accelerating timelines and reducing costs, yet its impact remains constrained by a persistent gap between computational promise and translational delivery. This gap stems from upstream preclinical failures, including weak target validation, biologically irrelevant models, and insufficient accountability for overstated methodological claims that contribute to late‐stage attrition. The Implementation, Methodology, Productivity, Assessment, Collaboration, Translation (IMPACT) framework addresses these root causes by establishing global standards that reinforce biological grounding, methodological credibility, and equitable collaboration. Implementation emphasizes Findable, Accessible, Interoperable, and Reusable (FAIR)‐compliant datasets, standardized vocabularies, and clear gradients of AI involvement from assisted to fully AI‐driven workflows. Methodology prioritizes reproducibility through model cards, containerized environments, and transparent reporting to support robust models. Productivity aligns AI efforts with urgent therapeutic priorities, including rare diseases, antimicrobial resistance, drug repurposing, and natural‐product discovery. Assessment promotes rigorous benchmarking, blind validation, and uncertainty quantification, drawing on the long‐established CASP model as a historical gold standard while critically examining emerging initiatives such as CACHE and Polaris Hub, which remain comparatively recent and evolving. Collaboration leverages federated learning, pre‐competitive consortia, and interdisciplinary teams integrating AI specialists with domain experts. Translation ensures outputs are explainable, clinically relevant, ethically aligned, and regulatory‐ready, consistent with emerging frameworks such as the FDA Draft Guidance on AI in Drug Development and the EU AI Act. By integrating technical standards with operational governance mechanisms, IMPACT provides a structured pathway toward transparent and translationally reliable AI‐driven drug discovery.