N-[2-(Trimethylammonium)ethyl]-1,8-naphthalimide (TENI) is a promising, versatile anion sensor that can detect the electronegativity of a target anion depending on the fluorescence intensity. In TENI, an emission from a lower-energy state than the first excited state of a single TENI is observed experimentally, and its character has remained unknown. Here, we have investigated the origin of the lower-energy peaks in the fluorescence spectrum of TENI using molecular dynamics (MD) simulations and time-dependent density functional theory (TD-DFT) calculations with three functionals. The computation reveals that TENIs form a structural ensemble in solution, and that stacked TENI dimers exhibit red-shifted fluorescence compared to a single TENI. The formation of excimer results in the longer-wavelength fluorescence that can be corroborated by changes in frontier orbital energies due to the intermolecular interaction. Our work will provide valuable knowledge for the development of new anion sensors.

A series of FLAP molecules are one of the functional fluorescent probes that can sense subtle changes in local environments such as viscosity, polymer free volume, and external mechanical force. In this work, potential energy surfaces (PESs) of a representative Flexible Aromatic Photofunctional (FLAP) molecule, FLAP0, which bears anthraceneimide wings fused with a cyclooctatetraene (COT) core, have been investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT). The calculation results indicate that, upon excitation with 350-400 nm light, the accessible states are the second (S2) and third (S3) singlet excited states. FLAP0 in these states undergoes nonadiabatic transitions to the first excited (S1) state via S3/S2 and S2/S1 conical intersections (CIX). Notably, a transition state (TS) on the S1 PES near the S2/S1 CIX exhibits a symmetry-breaking motion rather than flapping of the anthraceneimide wings, depriving the driving force for planarization. After reaching the S1 state with a shallow V-shaped geometry, therefore, FLAP0 planarization is thermally induced by COT hydrogen wagging. Due to the low barrier for this motion, the V-shaped and planar forms exist in thermal equilibrium on the S1 surface rather than undergoing unidirectional transformation. This equilibrium facilitates access to diverse energy minima around the planar S1 geometry, leading to the structured green emission bands. Consequently, the putative "vibronic structure" in solution is revealed to be the sum of emissions from these distinct planar forms.

π-Conjugated hydrocarbons are foundational molecular scaffolds in materials science. However, their design and discovery are limited by the lack of systematic frameworks for exploring their chemical space while preserving the structural validity. We present a unit-based molecule generation framework tailored to π-conjugated hydrocarbons, named CARBOT. This framework comprises a minimal set of chemically rational unit transformations, including vinyl- and ethynyl-group addition and cyclization via C-C bond formation. CARBOT is provably sound and complete within the scope of neutral Kekulé hydrocarbons, with carbons restricted to sp and sp2 hybridization, excluding allenes and carbenes. To accelerate access to aromatic frameworks, additional composite transformations are introduced. A depth-first search combined with a substructure-matching score enables rediscovery of a wide range of π-conjugated hydrocarbons within the defined space, including nonplanar nanographenes, highly strained carbon nanobelts, and Möbius hydrocarbons, and it can be scaled up to molecules as large as C200H100. The molecule generator is expected to provide a systematic foundation for the design and discovery of functional π-conjugated molecules.

The rational design of coordination complexes with precisely controlled excited-state dynamics remains a major challenge in the discovery of functional materials, particularly in multidimensional chemical spaces. In this context, aluminum complexes are particularly attractive because of their earth abundance, low toxicity, and unique ability to form structurally robust, luminescent helicates with versatile photophysical properties. Here, we report an AI-assisted computational–experimental strategy for metal complexes to design aluminum(III) dinuclear helicates exhibiting efficient long-wavelength emission. By integrating density functional theory calculations with asynchronous Bayesian optimization, we systematically modulated ligand electron density, conjugation length, and symmetry to control inter- 2 and intraligand charge–transfer processes. On the basis of the optimized methods, we synthesized five helicates, which displayed deep-red emission with quantum yields of up to 0.96 (λmax = 603 nm) and broadband emission extending into the near-infrared region (λmax = 671 nm), with considerable spectral weight extending to approximately 800 nm, remarkably properties in aluminum complexes. Transient absorption spectroscopy revealed that suppression of interligand charge transfer minimizes nonradiative decay and enables high quantum yields. The large apparent Stokes shifts arise from the combined effects of intraligand charge transfer and pronounced excited-state structural relaxation, which work together to stabilize the emissive state. In addition, the helicates further exhibit circularly polarized luminescence after optical resolution. These findings establish a scalable and generalizable AI-assisted strategy for designing sustainable coordination luminophores beyond purely organic systems.

Recent advances in generative artificial intelligence have made in silico molecular design a powerful approach for exploring chemical space toward specific goals. However, despite the need for trial-and-error adjustment of generative strategies and reward formulations, most methods implicitly fix the searchable chemical space, significantly limiting flexibility in practical design. This paper introduces ChemTSv3, an exploration framework with a flexible architecture that accommodates diverse design scenarios for adaptive molecular design. Specifically, molecular representations are unified as nodes, including string-based encodings, molecular graphs, and protein sequences. Molecular generations and editing operations are abstracted as transitions between nodes, allowing graph-based modifications, sequential mutations, and large-language-model-driven transformations to be handled within the same formulation. Representations and transition types can be dynamically switched to adapt the search space to the stage and nature of the design task. Here we show that this flexibility enables efficient exploration across diverse design spaces, from drug-like small molecules to proteins. Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains, however, most existing generation methods implicitly fix the searchable chemical space, limiting their flexibility. Here, the authors introduce ChemTSv3, a generalized framework for reward-directed molecular design that modularizes node states and transitions along with rewards and filters, allowing these components to be defined and interchanged as needed, and demonstrate its applicability to small molecules and protein design.

Recent advances in generative AI have sparked anticipation of a future where functional molecules that potentially outperform existing ones can be freely designed. However, as a comprehensive understanding of the property space defined by known functional molecules is lacking, assessing whether new AI/human-designed molecules truly surpass existing ones is challenging. To address this, we computed ~50 experimentally observable properties for >5 million molecules curated from three datasets covering commercially available, reported, and artificially constructed compounds using density functional theory. Based on them, we developed MolAtlas, a visualization system that reveals statistical boundaries and property relationships within an observable property space, offering a reference framework for evaluating molecular novelty. As an example, by analyzing frontier orbital energies, we proposed an empirical requirement for molecular air-stability. We further demonstrated the utility of the system by identifying a small fluorescent compound and characterizing charge-retaining molecules for liquid electrets. Thus, MolAtlas provides a data-driven foundation for navigating the molecular property space and accelerating functional molecule design.

Progress in chemistry has been driven by the streamlining of inverse problem-solving methods. In the history of chemistry, several revolutionary technologies have led to leaps forward: the establishment of atomistic theory in the 19th century, structural analysis by spectroscopy in the 20th century, and the development of simulation by theoretical chemistry. Currently, chemistry is about to make a significant leap forward by integrating generative artificial intelligence (AI). In 2016, deep learning techniques were introduced in this domain, leading to explosive development. This paper reviews the development path, including traditional models such as variational autoencoders and more up-to-date models such as large language models and diffusion models. We also discuss how AI can have a real impact on chemistry, including the possibilities and problems associated with synthesizing AI-generated molecules.

QCforever is a wrapper designed to automatically and simultaneously calculate various physical quantities using quantum chemical (QC) calculation software for blackbox optimization in chemical space. We have updated it to QCforever2 to search the conformation and optimize density functional parameters for a more accurate and reliable evaluation of an input molecule. In blackbox optimization, QCforever2 can work as compactly arranged surrogate models for costly chemical experiments. QCforever2 is the future of QC calculations and would be a good companion for chemical laboratories, providing more reliable search and exploitation in the chemical space.

Stable proton configurations in solid-state materials are a prerequisite for the theoretical microscopic investigation of solid-state proton-conductive materials. However, a large number of initial atomistic configurations should be considered to find stable proton configurations, and relaxation calculations using the density functional theory approach are required for each initial configuration. Consequently, the determination of stable configurations is a difficult and time-consuming task. Furthermore, when the size of the simulation cells or the number of doped atoms increases, the number of initial configurations leads to a combinatorial explosion, rendering the computation infeasible. In this study, black-box optimization was combined with an Ising machine and density functional calculations to perform an efficient search for stable proton configurations. Scandium-doped barium zirconate, a typical high-proton conductive oxide, was selected as the model system. The Ising machine was able to rapidly select the initial atomistic configuration, ultimately leading to stable proton configurations after subsequent relaxation calculations. This optimization strategy should be able to solve various issues related to configuration optimization in solid-state materials, thereby promoting novel scientific discoveries.

π-Conjugated and aromatic hydrocarbons are foundational molecular scaffolds in materials science. Their design and discovery, however, remain limited by the lack of systematic frameworks for exploring the vast chemical space while ensuring structural validity. To address this, we present a rule-based molecular generation framework for π-conjugated and aromatic hydrocarbons. This framework defines a minimal set of chemically inspired transformation rules, each corresponding to fundamental structural modifications while preserving π-conjugation. We provide rigorous proof that the method is sound and complete, as it generates only chemically valid π-conjugated hydrocarbons and can construct all such molecules within the defined space using a finite sequence of rules. The generated structures are further analyzed in terms of size, topology, and conjugation patterns, revealing an expansive and chemically meaningful design space. This framework enables the rediscovery of any π-conjugated hydrocarbon within the defined space and offers a systematic basis for data-driven design and discovery of functional π-conjugated materials.

Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop Equivariant N-body Interaction Networks (ENINet) that explicitly integrates l = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and generalized the formulation to N-body interactions. Experiments indicate that integrating many-body equivariant representations enhances prediction accuracy across diverse scalar and tensorial quantum chemical properties.

FeCo alloy-metal fluoride nanogranular films are known for their significant magnetoelectric responses, which are useful as several electromagnetic devices. In this study, we analyzed the interfaces between (110) FeCo and various clusters of metal fluorides (MgF2, AlF3, and YF3) to understand the origin of the large dielectric and magnetoelectric response caused by metal fluoride insulators through density functional molecular dynamics (DF-MD). Our DF-MD computations revealed that the metal fluoride clusters adhered to the (110) FeCo surface via Co/Fe-F bonds. The electronic structures of the (110) FeCo surfaces with the adsorbed metal fluoride clusters showed that FeCo was oxidized by the metal fluorides, resulting in polarization at the interface. Additionally because metal fluorides contain extra electrons, they can be magnetic, and the performance of electromagnetic devices depends on the electron affinity of fluoride insulators and the orbital magnetic moment of their acceptor levels.

Designing functional molecules is the prerogative of experts who have advanced knowledge and experience in their fields. To democratize automatic molecular design for both experts and non-experts, we introduce a generic open-sourced framework, ChemTSv2, to design molecules based on a de novo molecule generator equipped with an easy-to-use interface. Besides, ChemTSv2 can easily be integrated with various simulation packages, such as Gaussian 16 package, and supports a massively parallel exploration that accelerates molecular designs. We exhibit the potential of molecular design with ChemTSv2, including previous work, such as chromophores, fluorophores, drugs, and so forth. ChemTSv2 contributes to democratizing inverse molecule design in various disciplines relevant to chemistry.

Biological systems precisely and selectively control ion binding through various chemical reactions, molecular recognition, and transport by virtue of effective molecular interactions with biological membranes and proteins. Because ion binding is inhibited in highly polar media, recognition systems for anions in aqueous media, which are relevant to biological and environmental systems, are still limited. In this study, we explored the anion binding of Langmuir monolayers formed by amphiphilic naphthalenediimide (NDI) derivatives with a series of substituents at air/water interfaces via anion-π interactions. Density functional theory (DFT) simulations revealed that the binding of anions originating from anion-π interactions is related to the electron density of the anions. At the air/water interfaces, amphiphilic NDI derivatives formed Langmuir monolayers, and the addition of anions caused expansion of the Langmuir monolayers. The anions with larger hydration energies related to electron density showed larger binding constants (Ka) for 1:1 stoichiometry with the NDI derivatives. The loosely packed monolayer formed by the amphiphilic NDI derivatives with bromine groups showed a better anion response. In contrast, the binding of NO3- was significantly enhanced in the highly packed monolayer. These results indicate that the packing of NDI derivatives with rigid aromatic rings influenced the binding of the anions. These results provide insight into ion binding using the air/water interface as a promising recognition site for mimicking biological membranes. In future, sensing devices can be developed using Langmuir-Blodgett films on electrodes. Furthermore, the capture of anions on electron-deficient aromatic compounds can lead to doping or composition technologies for n-type semiconductors.

Self‐learning entropic population annealing (SLEPA) is a recently developed method used for achieving interpretable black‐box optimization via density‐of‐states estimation. Applying SLEPA to a chemical space is not straightforward, however, because of its dependence on Markov chain Monte Carlo sampling in the space of generated entities. Herein, SLEPA is applied to optimal molecule generation by combining an irreducible Markov chain in the space of fragment multisets and a probabilistic fragment assembler such as MoLeR. The weighted samples from SLEPA are used to identify salient fragments for the highest occupied molecular orbitals‐lowest unoccupied molecular orbitals (HOMO‐LUMO) gap maximization and minimization, and the relationship between the identified fragments and the electronic structures is elucidated. This approach offers a viable platform to reconcile the incompatible goals of optimization and interpretation during molecular design.

Based on available datasets prepared by numerical simulations and machine learning, maps of properties for materials that have not yet been synthesized can be developed. These maps can be used to select promising materials for synthetic experiments. With a single objective function, the ranking of the optimal solutions can be simply obtained based on the values of the target property. However, applications with multiple target properties require the calculation of Pareto optimal solutions to visualize trade-offs. These solutions are generally ranked manually, selecting the weight of the multiple objectives based on prior knowledge. In this study, to provide an automated ranking of Pareto solutions, we introduced the most-isolated Pareto solution (MIPS) score, which is defined by a projection free energy. Using the MIPS ranking, it is possible to appropriately select the most isolated materials predicted in the property space. To verify the effectiveness of the proposed method, we used a database of semiconductors created by density-functional theory. Our method was able to correctly select and rank the most isolated solutions in both convex and concave two-dimensional Pareto frontiers, outperforming the most relevant outlier detection methods. We also demonstrated that our approach can be easily extended to three-dimensional property spaces.

Formaldehyde (FA) is a deleterious C1 pollutant commonly found in the interiors of modern buildings. C1 chemicals are generally more toxic than the corresponding C2 chemicals, but the selective discrimination of C1 and C2 chemicals using simple sensory systems is usually challenging. Here, we report the selective detection of FA vapor using a chemiresistive sensor array composed of modified hydroxylamine salts (MHAs, ArCH2ONH2·HCl) and single-walled carbon nanotubes (SWCNT). By screening 32 types of MHAs, we have identified an ideal sensor array that exhibits a characteristic response pattern for FA. Thus, trace FA (0.02-0.05 ppm in air) can be clearly discriminated from the corresponding C2 chemical, acetaldehyde (AA). This system has been extended to discriminate methanol (C1) from ethanol (C2) in combination with the catalytic conversion of these alcohols to their corresponding aldehydes. Our system offers portable and reliable chemical sensors that discriminate the subtle differences between C1 and C2 chemicals, enabling advanced environmental monitoring and healthcare applications.