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Microplastics (MPs) and nanoplastics (NPs) have been discovered in diverse environmental milieux, and they have attracted much attention due to their possible toxicological impacts on living organisms. Likewise, salts, organic chemicals, heavy metals, natural organic matter, and other biomolecules are ubiquitously in the ecosystem. In a natural environment, MPs and NPs can interact with natural organic matter and biomolecules because of their exceptional characteristics. They can develop corona-based complexes, including eco-corona (EC) or bio-corona (BC) on the surface of MPs and NPs that not only change physiochemical characteristics but also its fate, distribution, uptake, transportation, biotransformation, and toxicological performance. Unlike orthodox toxins, coronas-based complexes on MPs and NPs rather than pristine MPs/NPs may be more hazardous. Therefore, the current critical review aims to discuss the up-to-date status of the modulations in the toxicological behavior of coronas-based MPs and NPs complexes on different environmental species. Significantly, it also focuses on the factors affecting the modulations of toxicological behavior of eco- and bio-corona-based MPs and NPs complexes for better understanding the role of environmental variables on the toxicity of plastics particles. Furthermore, this review systematically highlighted that the effect of a coronas-based complexes on the toxicological behavior of MPs and NPs is multifarious and variable in terms of plastic particle sizes, biomolecule’s types, polymer types, environmental species, and designed experimental conditions. To sum up, the current review addresses existing knowledge gaps and suggests recommendations.

Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.

Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields.

Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (Te,Tl)-AIMDEF [Alducin, M.;Phys. Rev. Lett.2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y.;J. Phys. Chem. Lett.2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (Te,Tl)-AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90–1000 K); a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms; and the varying CO coverage caused by the abundant desorption events.

Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas–surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H2+ Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H2dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases.

Nonadiabatic effects that arise from the concerted motion of electrons and atoms at comparable energy and time scales are omnipresent in thermal and light-driven chemistry at metal surfaces. Excited (hot) electrons can measurably affect molecule–metal reactions by contributing to state-dependent reaction probabilities. Vibrational state-to-state scattering of NO on Au(111) has been one of the most studied examples in this regard, providing a testing ground for developing various nonadiabatic theories. This system is often cited as the prime example for the failure of electronic friction theory, a very efficient model accounting for dissipative forces on metal-adsorbed molecules due to the creation of hot electrons in the metal. However, the exact failings compared to experiment and their origin from theory are not established for any system because dynamic properties are affected by many compounding simulation errors of which the quality of nonadiabatic treatment is just one. We use a high-dimensional machine learning representation of electronic structure theory to minimize errors that arise from quantum chemistry. This allows us to perform a comprehensive quantitative analysis of the performance of nonadiabatic molecular dynamics in describing vibrational state-to-state scattering of NO on Au(111) and compare directly to adiabatic results. We find that electronic friction theory accurately predicts elastic and single-quantum energy loss but underestimates multiquantum energy loss and overestimates molecular trapping at high vibrational excitation. Our analysis reveals that multiquantum energy loss can potentially be remedied within friction theory whereas the overestimation of trapping constitutes a genuine breakdown of electronic friction theory. Addressing this overestimation for dynamic processes in catalysis and surface chemistry will likely require more sophisticated theories

Water adsorption on metal surfaces is of both fundamental and practical relevance to heterogeneous and electrocatalysis. Here, we report a comprehensive theoretical study on the dynamics of H2O adsorption on Pt(110)-(1 × 2) based on a neural network potential energy surface (PES) fit to thousands of density functional theory data points. This PES allows us to investigate the dynamics of molecular and dissociative adsorption of water on Pt(110)-(1 × 2) from first principles with no adjustable parameters. Specifically, we find a molecular adsorption well depth that is close to the experimental estimate with no entry barrier, leading to a monotonic decrease in initial sticking probability (S0) with increasing translational energy. While this translational energy dependence of S0is consistent with the conventional mechanism for nonactivated adsorption and with earlier theoretical results, it is in qualitative disagreement with experimental data. Using an electronic friction model implemented with the local density friction approximation, we find that the low-energy electron–hole pair excitation mildly increases the trapping probability, whereas it has little influence on the translational energy dependence. Other possible factors, such as the rotational alignment and dynamical steering, which may be relevant to the discrepancy between theory and experiment, have also been discussed. On the other hand, the dissociative sticking probability is predicted as complementary information awaiting experimental confirmation. We hope that these results could motivate future experimental and theoretical studies to explore this puzzling gas–surface system.

Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.

Recently, we have developed a multilayer energy-based fragment (MLEBF) method to describe excited states of large systems in which photochemically active and inert regions are separately treated with multiconfigurational and single-reference electronic structure method and their mutual polarization effects are naturally described within the many-body expansion framework. This MLEBF method has been demonstrated to provide highly accurate energies and gradients. In this work, we have further derived the MLEBF method with which highly accurate excited-state Hessian matrices of large systems are efficiently constructed. Moreover, in combination with recently proposed embedded atom neural network (EANN) model we have developed a machine learning (ML) accelerated MLEBF method (i.e., ML-MLEBF) in which photochemically inert region is entirely replaced with trained ML models. ML-MLEBF is found to improve computational efficiency of Hessian matrices in particular for large systems. Furthermore, both MLEBF and ML-MLEBF methods are highly parallel and exhibit low-scaling computational cost with multiple CPUs. The present developments could motivate combining various ML techniques with fragment-based electronic structure methods to explore Hessian-matrix-based excited-state properties of large systems.

CO oxidation on transition metal surfaces is not only a prototype for studying surface chemistry but also of critical importance in applications such as pollution control and fuel cells. The reverse process, the dissociation of CO2, is also key in the sequestration of this greenhouse gas. However, our understanding of the dynamics involved in these processes is still incomplete. Theoretical studies of surface dynamics have so far been largely hindered by the high computational costs of on-the-fly calculations. To overcome this bottleneck, we report here a high-dimensional potential energy surface (PES) for both the dissociative chemisorption and recombinative desorption of CO2on Pt(111), by using a machine learning method. Trained with a large number of density functional points in a large configuration space, the multipurpose neural network PES accurately reproduces the geometry and energy of the stationary points along the CO2reaction path on Pt(111), as well as the dynamical results obtained using the ab initiomolecular dynamics method. In addition, we propose a new perspective on the chemical shape of the surface, which reveals the site specificity of the chemical barrier. This approach opens the door to accurate studies of these relevant reactions on surfaces, with a low computational cost, granting access to a more in-depth description of the chemical processes taking place in these systems.

Nonadiabatic effects in chemical reaction at metal surfaces, due to excitation of electron–hole pairs, stand at the frontier of the studies of gas-surface reaction dynamics. However, the first-principles description of electronic excitation remains challenging. In an efficient molecular dynamics with electronic friction (MDEF) method, the nonadiabatic couplings are effectively included in a so-called electronic friction tensor (EFT), which can be computed from first-order time-dependent perturbation theory (TDPT) in terms of density functional theory (DFT) orbitals. This second-rank tensor depends on adsorbate position and features a complicated transformation with regard to the intrinsic symmetry operations of the system. In this work, we develop a new symmetry-adapted neural network representation of EFT, based on our recently proposed embedded atom neural network (EANN) framework. Inspired by the derivation of the nonadiabatic coupling matrix, we represent the tensorial friction by the first and second derivatives of multiple outputs of NNs with respect to atomic Cartesian coordinates. This rigorously preserves the positive semidefiniteness, directional property, and correct symmetry-equivariance of EFT. Unlike previous methods, our new approach can readily include both molecular and surface degrees of freedom, regardless of the type of surface. Tests on the H2+ Ag(111) system show that this approach yields an accurate, efficient, and continuous representation of EFT, making it possible to perform large scale TDPT-based MDEF simulations to study both adiabatic and nonadiabatic energy dissipation in a unified framework.

Machine learning potential has become increasingly successful in atomistic simulations. Many of these potentials are based on an atomistic representation in a local environment, but an efficient description of nonlocal interactions that exceed a common local environment remains a challenge. Herein, we propose a simple and efficient equivariant model, EquiREANN, to effectively represent a nonlocal potential energy surface. It relies on a physically inspired message-passing framework, where the fundamental descriptors are linear combinations of atomic orbitals, while both invariant orbital coefficients and the equivariant orbital functions are iteratively updated. We demonstrate that this EquiREANN model is able to describe the subtle potential energy variation due to the nonlocal structural change with high accuracy and little extra computational cost than an invariant message passing model. Our work offers a generalized approach to create equivariant message-passing adaptations of other advanced local many-body descriptors.

The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons.

While the ab initiomolecular dynamics (AIMD) approach to gas–surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is orders-of-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/reaction of the impinging molecule on the solid surface on an equal footing.

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3–4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

The paper proposed a dielectrophoresis microfluidic chip for particle separation, which uses dielectric properties to perform size-based fractionation of blood cells. Using the principle of control variables, the value range of each parameter satisfying the separation condition was calculated by MATLAB and COMSOL software simulation and the specific parameters were selected to simulate the motion trajectory of cells in the microfluidic channel. At this time, all three kinds of particles were negative dielectrophoretic responses. The spheroid white blood cells were subjected to a stronger dielectrophoretic force than red blood cells and platelets, biased toward the right exit. Platelets and red blood cells flowed out from the left and middle outlets, respectively, under the combined action of fluid force and dielectrophoretic force to achieve the purpose of separation.

The paper proposes a dielectrophoresis microfluidic chip for particle separation, which uses dielectric properties to perform size-based fractionation of red blood cells and platelets. Based on the control variables, the distribution of the electric field in the chip and the trajectory of the particles in the microfluidic channel are calculated using COMSOL Multiphysics under different electrode shapes, voltages and chip exit structures. Both red blood cells and platelets respond to negative dielectrophoresis at an alternating current signal with a frequency of 100 kHz. The larger red blood cells are subjected to a stronger dielectrophoretic force than the platelets and are biased toward the right outlet, and the platelets flow out from the left outlet under the combined action of fluid force and dielectrophoretic force to achieve the purpose of separation. On this basis, through quantitative comparison and analysis, a more optimized microfluidic chip capable of effectively separating particles is finally selected.

With the development of surface acoustic wave devices, the separation of micron-sized particles based on standing surface acoustic waves (SSAW) has been studied, and the method has the advantages of high efficiency and ease of operation. In this paper, we design a two steps SSAW-based model for separating three different sizes of particles. The particle with the largest diameter is separated in the first sorting step region, and the remaining two particles are separated in the second sorting step region. During the study, we investigate the SSAW device firstly. We chose three different materials for the comparative analysis of the piezoelectric substrates. Next, we investigate the effects of fork-finger pairs and input voltage of the interdigital transducer (IDT) on the stability of the output waveform and sound pressure intensity. Finally, we select the appropriate number of fork-finger pairs and determine the acoustic pressure intensity in the low and high voltage regions respectively, so that three different particles could be successfully sorted in two steps. This research result can provide a certain theoretical basis for practical research fields such as cell sorting and drug detection. [ABSTRACT FROM AUTHOR]