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At least 829 records · Page 46

2023 Roadmap on molecular modelling of electrochemical energy materials

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO 2 reduction reaction, and fuel cell applications.

25 ENERGY STORAGE↗

Geometry-complete perceptron networks for 3D molecular graphs

Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.

59 BASIC BIOLOGICAL SCIENCES↗

Molecular basis of crosstalk in nuclear receptors: heterodimerization between PXR and CAR and the implication in gene regulation

The 48 human nuclear receptors (NRs) form a superfamily of transcription factors that regulate major physiological and pathological processes. Emerging evidence suggests that NR crosstalk can fundamentally change our understanding of NR biology, but detailed molecular mechanisms of crosstalk are lacking. Here, we report the molecular basis of crosstalk between the pregnane X receptor (PXR) and constitutive androstane receptor (CAR), where they form a novel heterodimer, resulting in their mutual inhibition. PXR and CAR regulate drug metabolism and energy metabolism. Although they have been broadly perceived as functionally redundant, a growing number of reports suggests a mutual inhibitory relation, but their precise mode of coordinated action remains unknown. Using methods including RNA sequencing, small-angle X-ray scattering and crosslinking mass spectrometry we demonstrate that the mutual inhibition altered gene expression globally and is attributed to the novel PXR–CAR heterodimerization via the same interface used by each receptor to heterodimerize with its functional partner, retinoid X receptor (RXR). These findings establish an unexpected functional relation between PXR, CAR and RXR, change the perceived functional relation between PXR and CAR, open new perspectives on elucidating their role and designing approaches to regulate them, and highlight the importance to comprehensively investigate nuclear receptor crosstalk.

59 BASIC BIOLOGICAL SCIENCES↗

Plants use molecular mechanisms mediated by biomolecular condensates to integrate environmental cues with development

This review highlights recent literature on biomolecular condensates in plant development and discusses challenges for fully dissecting their functional roles. Plant developmental biology has been inundated with descriptive examples of biomolecular condensate formation, but it is only recently that mechanistic understanding has been forthcoming. Here, we discuss recent examples of potential roles biomolecular condensates play at different stages of the plant life cycle. We group these examples based on putative molecular functions, including sequestering interacting components, enhancing dwell time, and interacting with cytoplasmic biophysical properties in response to environmental change. We explore how these mechanisms could modulate plant development in response to environmental inputs and discuss challenges and opportunities for further research into deciphering molecular mechanisms to better understand the diverse roles that biomolecular condensates exert on life.

59 BASIC BIOLOGICAL SCIENCES↗

Molecular principles of Piwi-mediated cotranscriptional silencing through the dimeric SFiNX complex

Nuclear Argonaute proteins, guided by their bound small RNAs to nascent target transcripts, mediate cotranscriptional silencing of transposons and repetitive genomic loci through heterochromatin formation. The molecular mechanisms involved in this process are incompletely understood. Here, we show that the SFiNX complex, a silencing mediator downstream from nuclear Piwi-piRNA complexes in Drosophila, facilitates cotranscriptional silencing as a homodimer. The dynein light chain protein Cut up/LC8 mediates SFiNX dimerization, and its function can be bypassed by a heterologous dimerization domain, arguing for a constitutive SFiNX dimer. Dimeric, but not monomeric SFiNX, is capable of forming molecular condensates in a nucleic acid-stimulated manner. Mutations that prevent SFiNX dimerization result in loss of condensate formation in vitro and the inability of Piwi to initiate heterochromatin formation and silence transposons in vivo. We propose that multivalent SFiNX-nucleic acid interactions are critical for heterochromatin establishment at piRNA target loci in a cotranscriptional manner.

59 BASIC BIOLOGICAL SCIENCES↗

Molecular bond stabilization in the strong-field dissociation of O 2 +

We theoretically examine the rotational and vibrational dynamics of O 2 + molecular ions exposed to intense, short laser pulses for conditions realized in contemporary pump-probe experiments. We solve the time-dependent Schrödinger equation within the Born-Oppenheimer approximation for an initial distribution of randomly aligned molecular ions. For fixed peak intensities, our numerical results show that total, angle-integrated O 2 + → O( 3 P) + O + ( 4 S 0 ) dissociation yields do not monotonically increase with increasing infrared-probe pulse duration. We find this pulse-duration-dependent stabilization to be consistent with the transient trapping of nuclear probability density in a light-induced (bond-hardening) potential-energy surface and robust against rotational excitation. We analyze this stabilization effect and its underlying bond-hardening mechanism (i) in the time domain, by following the evolution of partial nuclear probability densities associated with the dipole-coupled O 2 + (a 4 Π u ) and O 2 + (f 4 Π g ) cationic states, and (ii) in the frequency domain, by examining rovibrational quantum-beat spectra for the evolution of the partial nuclear probability densities associated with these states. In this work, our analysis reveals the characteristic timescale for the bond-hardening mechanism in O 2 + and explains the onset of bond stabilization for sufficiently long pulse durations.

74 ATOMIC AND MOLECULAR PHYSICS↗

Attosecond time delays in the body frame for photodetachment from molecular anions

Attosecond time delays in molecular photoionization at low energies are dominated by the contribution of the Coulomb phase, which diverges as the energy of the photoelectron decreases towards zero. With calculations on core-electron ejection from CN – and C 2 – we demonstrate that in photodetachment of molecular anions the absence of the long-range Coulomb interaction in the final state can reveal the delays of narrow low-energy shape resonances that do not generally occur in photoionization. Delays of several femtoseconds can be associated with such resonances. Finally, the angular dependence of photodetachment time delays in the body frame is highly sensitive to short-range anisotropies in the electron-molecule interaction as well as to the initial state.

74 ATOMIC AND MOLECULAR PHYSICS↗

Investigating the time dependence of neutron-proton equilibration using molecular dynamics simulations

Neutron-proton equilibration has previously been studied experimentally in dynamically deformed nuclei in heavy ion collisions [Phys. Rev. Lett. 118, 062501 (2017); Phys. Rev. C 95, 044604 (2017)] using the NIMROD detector array at the Cyclotron Institute at Texas A&M University. Results indicated the composition of the two heaviest fragments originating from the excited projectile-like fragment evolved exponential with respect to their orientation angle to be more similar. Constrained Molecular Dynamics and Antisymmetrized Molecular Dynamics simulations were performed for different formulations of the density dependence of the asymmetry energy term of the nuclear equation of state. The simulations are compared to experimental results, which indicate a better agreement with a softer interaction in the nuclear equation of state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Observation of a π-Type Dipole-Bound State in Molecular Anions

We report the observation of a π-type dipole-bound state (π-DBS) in cryogenically cooled deprotonated 9-anthrol molecular anions (9AT – ) by resonant two-photon photoelectron imaging. A DBS is observed 191 cm –1 (0.0237 eV) below the detachment threshold, and the existence of the π-DBS is revealed by a distinct (s + d)-wave photoelectron angular distribution. The π-DBS is stabilized by the large anisotropic in-plane polarizability of 9AT. Here, the population of the dipole-forbidden π-DBS is proposed to be via a nonadiabatic coupling with the dipole-allowed σ-type DBS mediated by molecular rotations.

74 ATOMIC AND MOLECULAR PHYSICS↗

Phonon-Induced Localization of Excitons in Molecular Crystals from First Principles

The spatial extent of excitons in molecular systems underpins their photophysics and utility for optoelectronic applications. Phonons are reported to lead to both exciton localization and delocalization. However, a microscopic understanding of phonon-induced (de)localization is lacking, in particular, how localized states form, the role of specific vibrations, and the relative importance of quantum and thermal nuclear fluctuations. Here, we present a first-principles study of these phenomena in solid pentacene, a prototypical molecular crystal, capturing the formation of bound excitons, exciton-phonon coupling to all orders, and phonon anharmonicity, using density functional theory, the ab initio GW-Bethe-Salpeter equation approach, finite-difference, and path integral techniques. In this study, we find that for pentacene zero-point nuclear motion causes uniformly strong localization, with thermal motion providing additional localization only for Wannier-Mott-like excitons. Anharmonic effects drive temperature-dependent localization, and, while such effects prevent the emergence of highly delocalized excitons, we explore the conditions under which these might be realized

74 ATOMIC AND MOLECULAR PHYSICS↗

Electrical Manipulation of Quantum Coherence in a Two-Level Molecular System

We report the manipulation of ultrafast quantum coherence of a two-level single hydrogen molecular system by employing static electric field from the sample bias in a femtosecond terahertz scanning tunneling microscope. A H 2 molecule adsorbed on the polar Cu 2⁢ N surface develops an electric dipole and exhibits a giant Stark effect. An avoided crossing of the quantum state energy levels is derived from the resonant frequency of the single H 2 two levels in a double-well potential. The dephasing time of the initial wave packet can also be changed by applying the electric field. The electrical manipulation for different tunneling gaps in three dimensions allows quantification of the surface electrostatic fields at the atomic scale. Further, our work demonstrated the potential application of molecules as controllable two-level molecular systems.

36 MATERIALS SCIENCE↗

Electroweak Nuclear Properties from Single Molecular Ions in a Penning Trap

Here, we present a novel technique to probe electroweak nuclear properties by measuring parity violation (PV) in single molecular ions in a Penning trap. The trap’s strong magnetic field Zeeman shifts opposite-parity rotational and hyperfine molecular states into near degeneracy. The weak interaction-induced mixing between these degenerate states can be larger than in atoms by more than 12 orders of magnitude, thereby vastly amplifying PV effects. The single molecule sensitivity would be suitable for applications to nuclei across the nuclear chart, including rare and unstable nuclei.

electronic structure of atoms & molecules↗

Neural Network Molecular Dynamics at Scale

Neural network molecular dynamics (NNMD) simulations could revolutionize atomistic modeling of materials with quantum-mechanical accuracy at a fraction of computational cost. However, popular NNMD frameworks are generally implemented for a single computing node, and conventional energy-based NN models still suffer from large time-to-solution (T2S), prohibiting the application of NNMD to challenging materials simulations encompassing large spatiotemporal scales. Consequently, no leadership-scale NNMD simulation has thus far been reported. Here, we present a scalable parallel NNMD software (RXMD-NN) based on our scalable reactive molecular dynamics simulation engine named RXMD. RXMD-NN has achieved high scalability up to 786,432 IBM BlueGene/Q cores involving 1.7 billion atoms. Furthermore, we have achieved 4.6-fold reduction of T2S by using a novel network that directly predicts atomic forces from feature vectors. Reduced T2S has for the first time allowed the study of large-scale off-stoichiometry effect in a widely used phase change material, Ge 2 Se 2 Te 5 , thereby resolving its "firstsharp diffraction peak mystery".

fist sharp diffraction peak↗

Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions

Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar + ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl 2 and Ar + impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl 2 -Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. Further, the demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

Artificial neural networks↗

Coarse-grained explicit-solvent molecular dynamics simulations of semidilute unentangled polyelectrolyte solutions

In this study, we present results from explicit-solvent coarse-grained molecular dynamics (MD) simulations of fully charged, salt-free, and unentangled polyelectrolytes in semidilute solutions. The inclusion of a polar solvent in the model allows for a more physical representation of these solutions at concentrations, where the assumptions of a continuum dielectric medium and screened hydrodynamics break down. The collective dynamic structure factor of polyelectrolytes, S(q, t), showed that at q > q*, where q* = 2π/ξ is the polyelectrolyte peak in the structure factor S(q) and ξ is the correlation length, the relaxation time obtained from fits to stretched exponential was $\tau$ KWW ~ q -3 , which describes unscreened Zimm-like dynamics. This is in contrast to implicit-solvent simulations using a Langevin thermostat where $\tau$ KWW ~ q -2 . At q < q*, a crossover region was observed that eventually transitions to another inflection point $\tau$ KWW ~ q -2 at length scales larger than ξ for both implicit- and explicit-solvent simulations. The simulation results were also compared to scaling predictions for correlation length, ξ ~ c$-½\atop{p}$, specific viscosity, η sp ~ c$½\atop{p}$, and diffusion coefficient, D ~ c$0\atop{p}$, where c p is the polyelectrolyte concentration. The scaling prediction for ξ holds; however, deviations from the predictions for η sp and D were observed for systems at higher c p , which are in qualitative agreements with recent experimental results. This study highlights the importance of explicit-solvent effects in molecular dynamics simulations, particularly in semidilute solutions, for a better understanding of polyelectrolyte solution behavior.

36 MATERIALS SCIENCE↗

CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

Background: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results: The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion: With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

59 BASIC BIOLOGICAL SCIENCES↗

Role of pore dilation in molecular transport through the nuclear pore complex: Insights from polymer scaling theory

The nuclear pore complex (NPC), a channel within the nuclear envelope filled with intrinsically disordered proteins, regulates the transport of macromolecules between the nucleus and the cytoplasm. Recent studies have highlighted the NPC’s ability to adjust its diameter in response to the membrane tension, underscoring the importance of exploring how variations in pore size influence molecular transport through the NPC. In this study, we investigated the relationship between pore size and transport rate and proposed a mathematical model describing this connection. We began by theoretically analyzing how the pore size scales with the characteristic dimensions of the mesh-like structure within the pore. By introducing key assumptions about how the meshwork structure influences molecular diffusion, we derived a mathematical expression for the transport rate based on the size of the pore and the transported molecules. To validate our model, we conducted Brownian dynamics simulations using a coarse-grained representation of the NPC. These simulations, performed across a range of pore sizes, demonstrated strong agreement with our model’s predictions, confirming its accuracy and applicability. Our model is specifically tailored for small-to-medium-sized molecules, approximately 5 nanometers in size, making it relevant to a wide range of transcription factors and signaling molecules. It also extends to molecules with weak and transient interactions with FG-Nups, such as importin-β. By presenting this model formula, our study offers a quantitative framework for analyzing the effects of pore dilation on nucleocytoplasmic transport.

Biochemistry & Molecular Biology↗