587+ open-access research outputs.
The transition from gaseous precursors to nascent solid particles and their subsequent structural maturation were investigated in single-pulse shock-tube experiments using ex situ Fourier-transform inโฆ
A time-domain analytic expression for chromatographic peak shapes is derived within a stochastic-diffusive framework that incorporates axial diffusion (molecular and multipath/Eddy), finite initial spโฆ
Among the possible types of magnetic dipole interactions in molecular systems, couplings between nuclear motion and the nuclear spin have probably received the least attention in molecular spectroscopโฆ
Electric field-assisted chemistry has attracted much attention in recent years, particularly in the context of oriented external electric fields for controlling molecular structure and reactivity. Sucโฆ
Although continuous symmetry theory has attracted increasing attention in modern chemistry, local symmetry remains under-investigated. As a consequence, the relationship between symmetry and chemical โฆ
Pulsed Dynamic Nuclear Polarization (DNP) is currently receiving substantial interest as a means to enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) by โฆ
Methane (CH4) pyrolysis is a promising route to co-produce hydrogen (H2) and carbon black (CB) while avoiding emissions associated with steam-methane reforming and furnace black processes. Model develโฆ
Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interacโฆ
This Review is devoted to the presentation of the exact factorization as a framework employed to study a variety of quantum-mechanical many-body problems. Since its original formulation in the 70s, thโฆ
Density functional theory is the workhorse of modern electronic structure calculations, with wide-ranging applications in chemistry, physics, materials science, and machine learning. At its heart liesโฆ
Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most existing approaches learโฆ
This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural language based interaโฆ
Density-functional theory (DFT) has become the workhorse of modern computational chemistry, with dispersion corrections such as the exchange-hole dipole moment (XDM) model playing a key role in high-aโฆ
The discovery of high-performance organic photocatalysts for hydrogen evolution remains limited by the vastness of chemical space and the reliance on human intuition for molecular design. Here we presโฆ
We present OmniMol, a state-of-the-art transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundation model for particle jets fouโฆ
Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliaโฆ
Silicate bioceramics have attracted significant attention in medical applications, particularly in hard tissue regeneration, because of their controllable chemical, physical, and biological functionalโฆ
Panchromatic absorbing materials are widely regarded as a key strategy for enhancing solar energy utilization and photocurrent generation. However, in artificial molecular systems, broadening the absoโฆ
Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a visโฆ
Stratospheric aerosol injection (SAI) has been proposed as a geoengineering strategy to mitigate global warming by increasing Earth's albedo. Silica-based materials, such as diamond-doped silica aerogโฆ
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