CyberSec Research

The disconnect between AI-generated molecules with desirable properties and their synthetic feasibility remains a critical bottleneck in computational drug and material discovery. While generative AI has accelerated the proposal of candidate molecules, many of these structures prove challenging or impossible to synthesize using established chemical reactions. Here, we introduce SynTwins, a novel retrosynthesis-guided molecular analog design framework that designs synthetically accessible molecular analogs by emulating expert chemist strategies through a three-step process: retrosynthesis, similar building block searching, and virtual synthesis. In comparative evaluations, SynTwins demonstrates superior performance in generating synthetically accessible analogs compared to state-of-the-art machine learning models while maintaining high structural similarity to original target molecules. Furthermore, when integrated with existing molecule optimization frameworks, our hybrid approach produces synthetically feasible molecules with property profiles comparable to unconstrained molecule generators, yet its synthesizability ensured. Our comprehensive benchmarking across diverse molecular datasets demonstrates that SynTwins effectively bridges the gap between computational design and experimental synthesis, providing a practical solution for accelerating the discovery of synthesizable molecules with desired properties for a wide range of applications.

As silicon is approaching its theoretical limit for the anode materials in lithium battery, searching for a higher limit is indispensable. Herein, we demonstrate the possible of achieving ultrahigh capacity over 6500 mAh g-1 in silicon-carbon composites. Considering the numerous defects inside the silicon nanostructures, it is deduced the formation of quasi-Bose Einstein condensation should be possible, which can lead to the low viscosity flow of lithium-ions through the anode. At a charge-discharge rate of 0.1C (0.42 A g-1), the initial discharge specific capacity reaches 6694.21 mAh g-1, with a Coulomb efficiency (CE) of 74.71%, significantly exceeding the theoretical capacity limit of silicon. Further optimization of the anode material ratio results in improved cycling stability, with a discharge specific capacity of 5542.98 mAh g-1 and a CE of 85.25% at 0.1C. When the initial discharge capacity is 4043.01 mAh g-1, the CE rises to 86.13%. By training a multilayer perceptron with material parameters as inputs and subsequently optimizing it using a constrained genetic algorithm, an initial discharge specific capacity of up to 7789.55 mAh g-1 can be achieved theoretically. This study demonstrates that silicon-carbon composites have great potential to significantly enhance the energy density of lithium-ion batteries.

We present a 3D-printing-based design to produce wire-guided liquid microfilms that can be used for versatile spectroscopic applications. We demonstrate the ability of our instrument to provide optically useful liquid microfilms with highly tunable thicknesses over the range 25 - 180 $\mu$m, with standard temporal thickness deviation less than 1.0% on the low end of the range of flow rates, and spatially homogeneous microfilms that remain stable over the course of ten hours. We then show the device's versatility through its use in Raman, fluorescence, and nonlinear spectroscopy. Our approach is highly reproducible as a unique advantage of a 3D-printed enclosure and limited other components. The 3D-printable file for the enclosure is included in the supplementary materials. This innovation in design shows the feasibility of applying 3D-printing to physical and chemical instrumentation for faster adoption of experimental techniques.

Chemical space which encompasses all stable compounds is unfathomably large and its dimension scales linearly with the number of atoms considered. The success of machine learning methods suggests that many physical quantities exhibit substantial redundancy in that space, lowering their effective dimensionality. A low dimensionality is favorable for machine learning applications, as it reduces the required number of data points. It is unknown however, how far the dimensionality of physical properties can be reduced, how this depends on the exact physical property considered, and how accepting a model error can help further reducing the dimensionality. We show that accepting a modest, nearly negligible error leads to a drastic reduction in independent degrees of freedom. This applies to several properties such as the total energy and frontier orbital energies for a wide range of neutral molecules with up to 20 atoms. We provide a method to quantify an upper bound for the intrinsic dimensionality given a desired accuracy threshold by inclusion of all continuous variables in the molecular Hamiltonian including the nuclear charges. We find the intrinsic dimensionality to be remarkably stable across molecules, i.e. it is a property of the underlying physical quantity and the number of atoms rather than a property of an individual molecular configuration and therefore highly transferable between molecules. The results suggest that the feature space of state-of-the-art molecular representations can be compressed further, leaving room for more data efficient and transferable models.

The Bethe-Salpeter equation (BSE) formalism, combined with the $GW$ approximation for ionization energies and electron affinities, is emerging as an efficient and accurate method for predicting optical excitations in molecules. In this letter, we present the first derivation and implementation of fully analytic nuclear gradients for the BSE@$G_0W_0$ method. Building on recent developments for $G_0W_0$ nuclear gradients, we derive analytic nuclear gradients for several BSE@$G_0W_0$ variants. We validate our implementation against numerical gradients and compare excited-state geometries and adiabatic excitation energies obtained from different BSE@$G_0W_0$ variants with those from state-of-the-art wavefunction methods.

Many chirality-sensitive light-matter interactions are governed by chiral electron dynamics. Therefore, the development of advanced technologies harnessing chiral phenomena would critically benefit from measuring and controlling chiral electron dynamics on their natural attosecond time scales. Such endeavors have so far been hampered by the lack of characterized circularly polarized attosecond pulses, an obstacle that has recently been overcome (Han et al. Optica 10 (2023) 1044-1052, Han et al. Nature Physics 19 (2023) 230-236). In this article, we introduce chiroptical spectroscopy with attosecond pulses and demonstrate attosecond coherent control over photoelectron circular dichroism (PECD) (Goetz et al. Physical Review Letters 122 (2019) 013204, Goetz et al. arXiv:2104.07522), as well as the measurement of chiral asymmetries in the forward-backward and angle-resolved photoionisation delays of chiral molecules. We show that co-rotating attosecond and near-infrared pulses can nearly double the PECD and even change its sign compared to single-photon ionisation. We demonstrate that chiral photoionisation delays depend on both polar and azimuthal angles of photoemission in the light-propagation frame, requiring three-dimensional momentum resolution. We measure forward-backward chiral-sensitive delays of up to 120 as and polar-angle-resolved photoionisation delays up to 240 as, which include an asymmmetry of $\sim$60 as originating from chirality in the continuum-continuum transitions. Attosecond chiroptical spectroscopy opens the door to quantitatively understanding and controlling the dynamics of chiral molecules on the electronic time scale.

To move towards the utility era of quantum computing, many corporations have posed distributed quantum computing (DQC) as a framework for scaling the current generation of devices for practical applications. One of these applications is quantum chemistry, also known as electronic structure theory, which has been poised as a "killer application" of quantum computing, To this end, we analyze five electronic structure methods, found in common packages such as Tequila and ffsim, which can be easily interfaced with the Qiskit Circuit Cutting addon. Herein, we provide insights into cutting these algorithms using local operations (LO) to determine their aptitude for distribution. The key findings of our work are that many of these algorithms cannot be efficiently parallelized using LO, and new methods must be developed to apply electronic structure theory within a DQC framework.

Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs and Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$ over multiple orders of magnitude of energy, yielding overall results with a relative error $\lesssim 10\%$. Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics.

Hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) show promise for quantum simulations on near-term quantum devices, but are often limited by complex objective functions and expensive optimization procedures. Here, we propose Flow-VQE, a generative framework leveraging conditional normalizing flows with parameterized quantum circuits to efficiently generate high-quality variational parameters. By embedding a generative model into the VQE optimization loop through preference-based training, Flow-VQE enables quantum gradient-free optimization and offers a systematic approach for parameter transfer, accelerating convergence across related problems through warm-started optimization. We compare Flow-VQE to a number of standard benchmarks through numerical simulations on molecular systems, including hydrogen chains, water, ammonia, and benzene. We find that Flow-VQE outperforms baseline optimization algorithms, achieving computational accuracy with fewer circuit evaluations (improvements range from modest to more than two orders of magnitude) and, when used to warm-start the optimization of new systems, accelerates subsequent fine-tuning by up to 50-fold compared with Hartree--Fock initialization. Therefore, we believe Flow-VQE can become a pragmatic and versatile paradigm for leveraging generative modeling to reduce the costs of variational quantum algorithms.

Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce domain-specific datasets and the demand for precise symbolic and structural reasoning. Here we introduce ECNU-ChemGPT(name after East China Normal University), a chemistry-specialized LLM engineered for deep chemical knowledge understanding and accurate retrosynthetic route planning. Our approach is distinguished by four key strategies: structured prompt-based knowledge distillation from authoritative chemistry textbooks to construct a high-quality question-answering dataset; domain-specific prompt engineering using curated chemical keywords, combined with LLMs APIs for data derivation and knowledge distillation; large-scale fine-tuning on a meticulously cleaned and enriched Pistachio reaction dataset to enhance retrosynthesis prediction accuracy; and integration of BrainGPT, a dynamic multi-model scheduling framework that enables task-specific invocation of multiple specialized models trained for diverse chemistry-related tasks. ECNU-ChemGPT exhibits superior performance on chemistry question-answering and retrosynthetic planning benchmarks, outperforming leading general-purpose models-including Deepseek-R1, Qwen-2.5, and GPT-4o. In retrosynthesis, it achieves a Top-1 accuracy of 68.3% on the USPTO_50K dataset and successfully reconstructed 13 complete experimental pathways for real-world drug molecules from medicinal chemistry journals. These results underscore the effectiveness of domain-adapted fine-tuning combined with dynamic multi-model task scheduling, providing a scalable and robust solution for chemical knowledge question answering and retrosynthetic planning.

Electrocatalysis provides an avenue for transitioning the global energy dependence from fossil fuels to renewable energy sources. While electrocatalytic reactions are being used for several decades, recently, there is a growing interest for electrocatalytic reactions that are useful from sustainability perspective. The wide industrial applications of these sustainable electrocatalytic processes is largely limited by the degradation of the electrocatalysts. This review begins with an introduction to such reactions, followed by a detailed discussion of the electrocatalysts. Finally we describe the processes that are responsible for the degradation of electrocatalytic activity.

The present study investigates the linear and non-linear optical and magneto-optical properties of TeO$_2$-BaO-Bi$_2$O$_3$ (TeBaBi) glasses prepared by the conventional melt-quenching technique at 900 {\deg}C. Prepared glass composition ranges across the whole glass-forming-ability (GFA) region focusing on mutual substitution trends of constituent oxides, where TeO$_2$: 55-85 mol.%, BaO: 10-35 mol.%, Bi$_2$O$_3$: 5-15 mol.%. Studied glasses exhibit high values of linear ($n_{632} \approx$ 1.922-2.084) and non-linear refractive index ($n_2\approx$1.63-3.45$\times10^{-11}$ esu), Verdet constant ($V_{632} \approx$ 26.7-45.3 radT$^{-1}$m$^{-1}$) and optical band gap energy ($E_g \approx$ 3.1-3.6 eV). The introduction of TeO$_2$ and Bi$_2$O$_3$ results in increase of both linear/non-linear refractive index and Verdet constant, with a more pronounced influence of Bi$_2$O$_3$. Measured spectral dispersion of refractive index and Verdet constant were used for estimation of magneto-optic anomaly parameter ($\gamma \approx$ 0.71-0.92), which may be used for theoretical modelling of magneto-optic response in diamagnetic TeBaBi glasses. Additionally, the properties of the prepared TeBaBi glasses were directly compared to those of the TeO$_2$-ZnO-BaO glass system, which was prepared and characterized under similar experimental conditions. The compositional dependence of the refractive index in both glass systems was described using multilinear regression analysis, demonstrating high correlation and uniformity of estimation across the entire GFA region. This makes them highly promising for precise dispersion engineering and construction of optical devices operating from visible to mid-infrared spectral region.

Understanding the physical and chemical properties of aqueous interfaces is important in diverse fields of science, ranging from biology and chemistry to materials science. In spite of crucial progress in surface sensitive spectroscopic techniques over the past decades, the microscopic properties of aqueous interfaces remain difficult to measure. Here we explore the use of noise spectroscopy to characterize interfacial properties, specifically of quantum sensors hosted in two-dimensional materials in contact with water. We combine molecular dynamics simulations of water/graphene interfaces and the calculations of the spin dynamics of an NV-like color center, and we investigate the impact of interfacial water and simple ions on the decoherence time of the defect. We show that the Hahn echo coherence time of the NV center is sensitive to motional narrowing and to the hydrogen bonding arrangement and the dynamical properties of water and ions at the interface. We present results as a function of the liquid temperature, strength of the water-surface interaction, and for varied mono-valent and di-valent ions, highlighting the broad applicability of near-surface qubits to gain insight into the properties of aqueous interfaces.

Many reactions in chemistry and biology involve multiple electronic states, rendering them nonadiabatic in nature. These reactions can be formally described using Fermi's golden rule (FGR) in the weak-coupling limit. Nonadiabatic instanton theory presents a semiclassical approximation to FGR, which is directly applicable to molecular systems. However, there are cases where the theory has not yet been formulated. For instance, in many real-world reactions including spin-crossover or proton-coupled electron transfer, the crossing occurs near a barrier on a diabatic state. This scenario gives rise to competing nonadiabatic reaction pathways, some of which involve tunneling through a diabatic barrier while simultaneously switching electronic states. To date, no rate theory is available for describing tunneling via these unconventional pathways. Here we extend instanton theory to model this class of processes, which we term the ``non-convex'' regime. Benchmark tests on model systems show that the rates predicted by instanton theory are in excellent agreement with quantum-mechanical FGR calculations. Furthermore, the method offers new insights into multi-step tunneling reactions and the competition between sequential and concerted nonadiabatic tunneling pathways.

Spherical density functional theory (DFT) [Theophilou, J. Chem. Phys. 149, 074104 (2018)] is a reformulation of the classic theorems of DFT, in which the role of the total density of a many-electron system is replaced by a set of sphericalized densities, constructed by spherically-averaging the total electron density about each atomic nucleus. In Hohenberg-Kohn DFT and its constrained-search generalization, the electron density suffices to reconstruct the spatial locations and atomic numbers of the constituent atoms, and thus the external potential. However, the original proofs of spherical DFT require knowledge of the atomic locations at which each sphericalized density originates, in addition to the set of sphericalized densities themselves. In the present work, we utilize formal results from geometric algebra -- in particular, the subfield of distance geometry -- to show that this spatial information is encoded within the ensemble of sphericalized densities themselves, and does not require independent specification. Consequently, the set of sphericalized densities uniquely determines the total external potential of the system, exactly as in Hohenberg-Kohn DFT. This theoretical result is illustrated through numerical examples for LiF and for glycine, the simplest amino acid. In addition to establishing a sound practical foundation for spherical DFT, the extended theorem provides a rationale for the use of sphericalized atomic basis densities -- rather than orientation-dependent basis functions -- when designing classical or machine-learned potentials for atomistic simulation.

Molecular exciton-polaritons exhibit long-range, ultrafast propagation, yet recent experiments have reported far slower propagation than expected. In this work, we implement a nonperturbative approach to quantify how static energetic disorder renormalizes polariton group velocity in strongly coupled microcavities. The method requires no exact diagonalization or master equation propagation, and depends only on measurable parameters: the mean exciton energy and its variance, the microcavity dispersion, and the Rabi splitting. Using parameters corresponding to recently probed organic microcavities, we show that exciton inhomogeneous broadening slows both lower and upper polaritons, particularly when the mean exciton energy fluctuation approaches the collective light-matter coupling strength. A detailed discussion and interpretation of these results is provided using perturbation theory in the limit of weak resonance scattering. Overall, our results support the view that exciton-phonon interactions likely dominate the recent experimental observations of polariton slowdown in disordered media.

Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but lack accuracy for transition states and fine conformational details critical in many chemical and biological processes. Quantum Mechanics (QM) methods are highly accurate but computationally infeasible for large-scale or long-time simulations. AI-based force fields (AIFFs) aim to achieve QM-level accuracy with efficiency but struggle to balance many-body modeling complexity, accuracy, and speed, often constrained by limited training data and insufficient validation for generalizability. To overcome these challenges, we introduce LiTEN, a novel equivariant neural network with Tensorized Quadrangle Attention (TQA). TQA efficiently models three- and four-body interactions with linear complexity by reparameterizing high-order tensor features via vector operations, avoiding costly spherical harmonics. Building on LiTEN, LiTEN-FF is a robust AIFF foundation model, pre-trained on the extensive nablaDFT dataset for broad chemical generalization and fine-tuned on SPICE for accurate solvated system simulations. LiTEN achieves state-of-the-art (SOTA) performance across most evaluation subsets of rMD17, MD22, and Chignolin, outperforming leading models such as MACE, NequIP, and EquiFormer. LiTEN-FF enables the most comprehensive suite of downstream biomolecular modeling tasks to date, including QM-level conformer searches, geometry optimization, and free energy surface construction, while offering 10x faster inference than MACE-OFF for large biomolecules (~1000 atoms). In summary, we present a physically grounded, highly efficient framework that advances complex biomolecular modeling, providing a versatile foundation for drug discovery and related applications.

Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion (ICF) applications. The work horse of warm dense matter theory is given by thermal density functional theory (DFT), which, however, suffers from two limitations: (i) its accuracy can depend on the utilized exchange--correlation (XC) functional, which has to be approximated and (ii) it is generally limited to single-electron properties such as the density distribution. Here, we present a new ansatz combining time-dependent DFT results for the dynamic structure factor $S_{ee}(\mathbf{q},\omega)$ with static DFT results for the density response. This allows us to estimate the electron--electron static structure factor $S_{ee}(\mathbf{q})$ of warm dense hydrogen with high accuracy over a broad range of densities and temperatures. In addition to its value for the study of warm dense matter, our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.