FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations.

Implementation of real-time TDDFT for periodic systems in the open-source PySCF software package.

We present a new implementation of real-time time-dependent density functional theory (RT-TDDFT) for calculating excited-state dynamics of periodic systems in the open-source Python-based PySCF software package. Our implementation uses Gaussian basis functions in a velocity gauge formalism and can be applied to periodic surfaces, condensed-phase, and molecular systems. As representative benchmark applications, we present optical absorption calculations of various molecular and bulk systems and a real-time simulation of field-induced dynamics of a (ZnO) molecular cluster on a periodic graphene sheet.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs).

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of and biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines.

Photo-induced degradation of PFASs: Excited-state mechanisms from real-time time-dependent density functional theory.

Per- and polyfluoroalkyl substances (PFASs) are hazardous, carcinogenic, and bioaccumulative contaminants found in drinking water sources. To mitigate and remove these persistent pollutants, recent experimental efforts have focused on photo-induced processes to accelerate their degradation; however, the mechanistic details of these promising degradation processes remain unclear. To shed crucial insight on these electronic-excited state processes, we present the first study of photo-induced degradation of explicitly-solvated PFASs using excited-state, real-time time-dependent density functional theory (RT-TDDFT) calculations.

Harnessing Plasma Environments for Ammonia Catalysis: Mechanistic Insights from Experiments and Large-Scale Molecular Dynamics.

By combining experimental measurements with molecular dynamics simulations, we provide the first microscopic description of the interaction between metal surfaces and a low-temperature nitrogen-hydrogen plasma. Our study focuses on the dissociation of hydrogen and nitrogen as the main activation route. We find that ammonia forms via an Eley-Rideal mechanism where atomic nitrogen abstracts hydrogen from the catalyst surface to form ammonia on an extremely short time scale (a few picoseconds).

Indirect but Efficient: Laser-Excited Electrons Can Drive Ultrafast Polarization Switching in Ferroelectric Materials.

To enhance the efficiency of next-generation ferroelectric (FE) electronic devices, new techniques for controlling ferroelectric polarization switching are required. While most prior studies have attempted to induce polarization switching via the excitation of phonons, these experimental techniques required intricate and expensive terahertz sources and have not been completely successful. Here, we propose a new mechanism for rapidly and efficiently switching the FE polarization via laser-tuning of the underlying dynamical potential energy surface.

An MM and QM Study of Biomimetic Catalysis of Diels-Alder Reactions Using Cyclodextrins.

We performed a computational investigation of the mechanism by which cyclodextrins (CDs) catalyze Diels-Alder reactions between 9-anthracenemethanol and -cyclohexylmaleimide. Hydrogen bonds (Hbonds) between -cyclohexylmaleimide and the hydroxyl groups of cyclodextrins were suggested to play an important role in this catalytic process. However, our free energy calculations and molecular dynamics simulations showed that these Hbonds are not stable, and quantum mechanical calculations suggested that the reaction is not promoted by these Hbonds.