Quantum Landscape of Precision Chemistry

Quantum mechanics provides the foundational framework for understanding the properties and behavior of matter at the microscopic scale. It governs phenomena ranging from the electronic structure of atoms and molecules to the dynamics of chemical reactions, determining the energy landscapes, reaction pathways, and observable characteristics that define chemical phenomena. Achieving precision chemistrythe ability to predict, control, and exploit chemical processes with quantitative accuracytherefore requires a thorough and systematic grasp of the quantum nature of chemical systems.

In recent years, remarkable advances have been made across multiple fronts. High-level quantum chemistry methods, including those based on quantum computing, continue to push the boundaries of accuracy in modeling molecular electronic structure. Quantum dynamics simulations now enable detailed modeling of complex nonadiabatic behaviorsuch as nuclear motion, nonadiabatic transitions, and tunneling on coupled potential energy surfacesrevealing previously inaccessible mechanistic insights. Concurrently, advanced synthesis techniques allow for the preparation of novel quantum materials with precisely controlled structures, and state-of-the-art characterization toolsincluding low-temperature matrix isolation spectroscopy, magnetotransport measurements, and electrochemical analysisprovide direct observations of quantum phenomena in real systems.

To highlight the advances in this rapidly evolving area, we have organized this special issue, ″Quantum Landscape of Precision Chemistry″ It brings together ten contributions that collectively address the themes outlined above, spanning quantum algorithm development and computational methodology to materials synthesis, spectroscopic characterization, and device-level demonstrations.

Quantum computing serves as a powerful tool for studying quantum systems. Sheng et al. propose a quantum computing algorithm for simulating molecules coupled to optical cavities. By combining variational quantum algorithms with variational boson encoders, their approach efficiently handles the complex electron–phonon–photon couplings in both the Holstein–Tavis–Cummings and Pauli–Fierz models. Chang et al. introduce the quantum-assisted variational Monte Carlo (QA-VMC) algorithm for solving the ground state of quantum many-body systems. Inspired by quantum-enhanced Markov chain Monte Carlo, their method leverages quantum computers to generate proposal distributions that exhibit larger absolute spectral gaps and reduced autocorrelation times compared to classical proposals.

Electronic structure and the potential models derived from it form the foundation for understanding chemical systems. Liu and Dou address the optimization bottleneck in neural quantum states (NQS) by introducing the implicitly restarted Lanczos (IRL) method. By reformulating the parameter update as a Hermitian eigenvalue problem, IRL automatically determines the optimal descent direction and step size without hyperparameter tuning. Chen et al. benchmark machine learning foundation potentials (MACE-OMol-0 and UMA) against a hierarchy of DFT functionals for predicting molecular redox potentials. While these potentials achieve exceptional accuracy for proton-coupled electron transfer reactions, their performance degrades for multielectron transfer processes involving reactive ions underrepresented in training data.

Dynamics and kinetics lie at the core of chemistry. Dong et al. discover a paradoxical suppression of exciton diffusion by long-range interactions through large-scale nonadiabatic dynamics simulations. While long-range couplings enhance exciton mobility when nearest-neighbor interactions are weakas conventional wisdom predictsthey can paradoxically suppress diffusion when nearest-neighbor coupling is strong. This counterintuitive behavior arises from the competition between energy redistribution and exciton delocalization. Chu et al. revisit the question of whether iodine interstitial defects serve as recombination centers in FAPbI₃ perovskites. Through ab initio and nonadiabatic molecular dynamics simulations, they demonstrate that at room temperature the iodine interstitial defect level falls below the valence band maximum due to temperature-dependent I-dimer dissociation, effectively eliminating it as a trap state.

Experimentally, Upadhyay et al. investigate the reaction dynamics of H + HeH⁺ → He + H₂ ⁺ using two independently constructed potential energy surfaces. Both surfaces yield rate coefficients consistent with the Langevin rate at 10 K but approximately five times larger than the only available experimental value, suggesting that the previously reported ion cyclotron resonance measurement may underestimate the true rate. Rostkowska et al. report an experimental characterization of conformational interconversions in isolated isoguanine monomers trapped in low-temperature Ar and Ne matrices. Selective narrowband near-infrared excitation drives bidirectional OH rotamerization, while spontaneous hydrogen-atom tunneling from the higher-energy conformer to the global minimum occurs with time constants of several hours.

Quantum materials and devices also occupy an important position in the quantum landscape of precision chemistry. Ma et al. report the unconventional magnetic and magneto-transport properties of Fe₃SnSb polycrystals synthesized via a two-step solid-state reaction. The compound exhibits a Néel temperature of 156 K with coexisting ferromagnetism and antiferromagnetism below 50 K, alongside the relatively rare phenomenon of canted antiferromagnetism. Mishra et al. present a comprehensive review of atomically precise platinum nanoclusters, highlighting their unique position at the transition between coordination molecules and colloidal nanoparticles. The authors reviewed hierarchical design principles that enable precise control over catalytic properties for both electrochemical and thermal transformations.

From the contributions gathered in this special issue, it is evident that the integration of experiment and theory is essential for advancing precision chemistry. Whether reconciling computed and measured reaction rate coefficients for astrophysical modeling, benchmarking machine learning potentials against experimental redox potentials, or validating quantum dynamics simulations through spectroscopic observations, the close interplay between computation and experiment drives the field toward quantitative predictability. Looking ahead, the synergy among these advances will be key to realizing the full promise of precision chemistry, bringing chemistry closer to a truly predictive and controllable science. The quantum landscape remains vast and only partially explored, and the contributions in this special issue represent important strides toward mapping this landscape with increasing accuracy, detail and chemical insight.

Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.