Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning

Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

HYPOTHESIS

The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases.

EXPERIMENTS AND SIMULATIONS

This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system.

FINDINGS

The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. This research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

Amplifying Nanoparticle Reinforcement through Low Volume Topologically Controlled Chemical Coupling

We present a streamlined method to covalently bond hydroxylated carbon nanotubes (CNOH) within a polyphenol matrix, all achieved through a direct, solvent-free process. Employing an extremely small concentration of CNOH (0.01% w/w) along with topologically contrasting linkers led to a maximum of 5-fold increase in modulus and a 25% enhancement in tensile strength compared to the unaltered matrix, an order of magnitude greater reinforcement (w/w) compared to state-of-the-art melt-processed nanocomposites. Through dynamic mechanical analysis, low field solid-state nuclear magnetic resonance spectroscopy, and molecular dynamics simulations, we uncovered the profound influence of linker's conformational degrees of freedom on the segmental dynamics and therefore the material's properties.

Viscoelastic relaxation and topological fluctuations in glass-forming liquids

A method for characterizing the topological fluctuations in liquids is proposed. This approach exploits the concept of the weighted gyration tensor of a collection of particles and permits the definition of a local configurational unit (LCU). The first principal axis of the gyration tensor serves as the director of the LCU, which can be tracked and analyzed by molecular dynamics simulations. Analysis of moderately supercooled Kob-Andersen mixtures suggests that orientational relaxation of the LCU closely follows viscoelastic relaxation and exhibits a two-stage behavior. The slow relaxing component of the LCU corresponds to the structural, Maxwellian mechanical relaxation. Additionally, it is found that the mean curvature of the LCUs is approximately zero at the Maxwell relaxation time with the Gaussian curvature being negative. This observation implies that structural relaxation occurs when the configurationally stable and destabilized regions interpenetrate each other in a bicontinuous manner. Finally, the mean and Gaussian curvatures of the LCUs can serve as reduced variables for the shear stress correlation, providing a compelling proof of the close connection between viscoelastic relaxation and topological fluctuations in glass-forming liquids.

Enriching 2D transition metal borides via MB XMenes (M = Fe, Co, Ir): Strong correlation and magnetism

Recently, two-dimensional (2D) FeSe-like anti-MXenes (or XMenes), composed of late d-block transition metal M and p-block nonmetal X elements, have been both experimentally and theoretically investigated. Here, we select three 2D borides FeB, CoB and IrB for a deeper investigation by including strong correlation effects, as a fertile ground for understanding and applications. Using a combination of Hubbard corrected first-principles calculations and Monte Carlo simulations, FeB and CoB are found to be ferro- and anti-ferro magnetic, contrasting with the non-magnetic nature of IrB. The metallic FeB XMene monolayer, superior to most of the MXenes or MBenes, exhibits robust ferromagnetism, driven by intertwined direct-exchange and super-exchange interactions between adjacent Fe atoms. The predicted Curie temperature (TC) of the FeB monolayer via the Heisenberg model reaches an impressive 425 K, with the easy-axis oriented out-of-plane and high magnetic anisotropic energy (MAE). The asymmetry in the spin-resolved transmission spectrum induces a thermal spin current, providing an opportunity for spin filtration. This novel 2D FeB material is expected to hold great promise as an information storage medium and find applications in emerging spintronic devices.

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

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 [Formula: see text], where [Formula: see text] is the polyelectrolyte peak in the structure factor S(q) and [Formula: see text] is the correlation length, the relaxation time obtained from fits to stretched exponential was [Formula: see text], which describes unscreened Zimm-like dynamics. This is in contrast to implicit-solvent simulations using a Langevin thermostat where [Formula: see text]. At [Formula: see text], a crossover region was observed that eventually transitions to another inflection point [Formula: see text] at length scales larger than [Formula: see text] for both implicit- and explicit-solvent simulations. The simulation results were also compared to scaling predictions for correlation length, [Formula: see text], specific viscosity, [Formula: see text], and diffusion coefficient, [Formula: see text], where [Formula: see text] is the polyelectrolyte concentration. The scaling prediction for [Formula: see text] holds; however, deviations from the predictions for [Formula: see text] and D were observed for systems at higher [Formula: see text], 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.

Mesoscopic two-point collective dynamics of glass-forming liquids

The collective density-density and hydrostatic pressure-pressure correlations of glass-forming liquids are spatiotemporally mapped out using molecular dynamics simulations. It is shown that the sharp rise of structural relaxation time below the Arrhenius temperature coincides with the emergence of slow, nonhydrodynamic collective dynamics on mesoscopic scales. The observed long-range, nonhydrodynamic mode is independent of wave numbers and closely coupled to the local structural dynamics. Below the Arrhenius temperature, it dominates the slow collective dynamics on length scales immediately beyond the first structural peak in contrast to the well-known behavior at high temperatures. These results highlight a key connection between the qualitative change in mesoscopic two-point collective dynamics and the dynamic crossover phenomenon.

Selective deconstruction of mixed plastics by a tailored organocatalyst

Plastic represents an essential material in our society; however, a major imbalance between their high production and end-of-life management is leading to unrecovered energy, economic hardship, and a high carbon footprint. The adoption of plastic recycling has been limited, mainly due to the difficulty of recycling mixed plastics. Here, we report a versatile organocatalyst for selective glycolysis of diverse consumer plastics and their mixed waste streams into valuable chemicals. The developed organocatalyst selectively deconstructs condensation polymers at a specific temperature, and additives or other polymers such as polyolefin or cellulose can be readily separated from the mixed plastics, providing a chemical recycling path for many existing mixed plastics today. The Life Cycle Assessment indicates that the production of various condensation polymers from the deconstructed monomers will result in a significant reduction in greenhouse gas emissions and energy input, opening a new paradigm of plastic circularity toward a net-zero carbon society.

Chemical upcycling of polyethylene, polypropylene, and mixtures to high-value surfactants

Conversion of plastic wastes to fatty acids is an attractive means to supplement the sourcing of these high-value, high-volume chemicals. We report a method for transforming polyethylene (PE) and polypropylene (PP) at ~80% conversion to fatty acids with number-average molar masses of up to ~700 and 670 daltons, respectively. The process is applicable to municipal PE and PP wastes and their mixtures. Temperature-gradient thermolysis is the key to controllably degrading PE and PP into waxes and inhibiting the production of small molecules. The waxes are upcycled to fatty acids by oxidation over manganese stearate and subsequent processing. PP ꞵ-scission produces more olefin wax and yields higher acid-number fatty acids than does PE ꞵ-scission. We further convert the fatty acids to high-value, large-market-volume surfactants. Industrial-scale technoeconomic analysis suggests economic viability without the need for subsidies.

The Role of SnO2 Processing on Ionic Distribution in Double-Cation-Double Halide Perovskites

Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion efficiency. Serving as a gateway to optimize device performance, consideration must be given to chemical synthesis processing techniques. Therefore, how does common substrate processing techniques influence the behavior of MHP phenomena such as ion migration and strain? Here, we demonstrate how a hybrid approach of chemical bath deposition (CBD) and nanoparticle SnO₂ substrate processing significantly improves the performance of (FAPbI₃)_0.97(MAPbBr₃)_0.03 by reducing micro-strain in the SnO₂ lattice, allowing distribution of K⁺ from K-Cl treatment of substrates to passivate defects formed at the interface and produce higher current in light and dark environments. X-ray diffraction reveals differences in lattice strain behavior with respect to SnO₂ substrate processing methods. Through use of conductive atomic force microscopy (c-AFM), conductivity is measured spatially with MHP morphology, showing higher generation of current in both light and dark conditions for films with hybrid processing. Additionally, time-of-flight secondary ionization mass spectrometry (ToF-SIMS) observed the distribution of K⁺ at the perovskite/SnO₂ interface, indicating K⁺ passivation of defects to improve the power conversion efficiency (PCE) and device stability. We show how understanding the role of ion distribution at the SnO₂ and perovskite interface can help reduce the creating of defects and promote a more efficient MHP device.

Anti-polyelectrolyte and polyelectrolyte effects on conformations of polyzwitterionic chains in dilute aqueous solutions

Polyzwitterions (PZs) are considered as model synthetic analogs of intrinsically disordered proteins. Based on this analogy, PZs in dilute aqueous solutions are expected to attain either globular (i.e. molten, compact) or random coil conformations. Addition of salt is expected to open these conformations. To the best of our knowledge, these hypotheses about conformations of PZs have never been verified. In this study, we test these hypotheses by studying effects of added salt [potassium bromide (KBr)] on gyration and hydrodynamic radii of poly(sulfobetaine methacrylate) in dilute aqueous solutions using dynamic light scattering and small-angle X-ray scattering, respectively. Effects of zwitteration are revealed by direct comparisons of the PZs with the polymers of the same backbone but containing (1) no explicit charges on side groups such as poly(2-dimethylaminoethyl methacrylate)s and (2) explicit cationic side groups with tertiary amino bromide pendants. Zeta-potential measurements, transmission electron microscopy, and ab initio molecular dynamics simulations reveal that the PZs acquire net positive charge in near salt-free conditions due to protonation but retain coiled conformations. Added KBr leads to nonmonotonic changes exhibiting an increase followed by a decrease in radius of gyration (and hydrodynamic radius), which are called antipolyelectrolyte and polyelectrolyte effects, respectively. Charge regulation and screening of charge-charge interactions are discussed in relation to the antipolyelectrolyte and polyelectrolyte effects, respectively, which highlight the importance of salt in affecting net charge and conformations of PZs.

Quantum theory of electronic excitation and sputtering by transmission electron microscopy

Many computational models have been developed to predict the rates of atomic displacements in two-dimensional (2D) materials under electron beam irradiation. However, these models often drastically underestimate the displacement rates in 2D insulators, in which beam-induced electronic excitations can reduce the binding energies of the irradiated atoms. This bond softening leads to a qualitative disagreement between theory and experiment, in that substantial sputtering is experimentally observed at beam energies deemed far too small to drive atomic dislocation by many current models. To address these theoretical shortcomings, this paper develops a first-principles method to calculate the probability of beam-induced electronic excitations by coupling quantum electrodynamics (QED) scattering amplitudes to density functional theory (DFT) single-particle orbitals. The presented theory then explicitly considers the effect of these electronic excitations on the sputtering cross section. Applying this method to 2D hexagonal BN and MoS₂ significantly increases their calculated sputtering cross sections and correctly yields appreciable sputtering rates at beam energies previously predicted to leave the crystals intact. The proposed QED-DFT approach can be easily extended to describe a rich variety of beam-driven phenomena in any crystalline material.

Assembly of polyelectrolyte star block copolymers at the oil-water interface

To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at the oil-water interface. This is important to guide development of novel surfactants or amphiphiles for chemical transformations and separations. The star block copolymer consisted of arms that are comprised of hydrophilic-hydrophobic block copolymers that are covalently tethered via the hydrophobic blocks to one point. The hydrophobic core represents polystyrene (PS) chains, while the hydrophilic corona represents quaternized poly(2-vinylpyridine) (P2VP) chains. The P2VP is modeled to become protonated when in contact with an acidic aqueous phase, thereby massively increasing the hydrophilicity of this block, and changing the nature of the star at the oil-water interface. This results in a configurational change whereby the chains comprising the hydrophilic corona are significantly stretched into the aqueous phase, while the hydrophobic core remains solubilized in the oil phase. In the simulations, we followed the kinetics of the anchoring and assembly of the star block copolymer at the interface, monitoring the lateral assembly, and the subsequent reconfiguration of the star via changes in the interfacial tension that varies as the degree-of-protonation increases. At low fractions of protonation, the arm cannot fully partition into the aqueous side of the interface and instead interacts with other arms in the oil phase forming a network near the interface. These insights were used to interpret the non-monotonic dependence of pH with the asymptotic interfacial tension from pendant drop tensiometry experiments and spectral signatures of aromatic stretches seen in vibrational sum frequency generation (SFG) spectroscopy. We describe the relationship of interfacial tension to the star assembly via the Frumkin isotherm, which phenomenologically describes anti-cooperativity in adsorbing stars to the interface due to crowding. Although our model explicitly considers long-range electrostatics, the contribution of electrostatics to interfacial tension is small and brought about by strong counterion condensation at the interface. These results provide key insights into resolving the adsorption, reconfiguration, and equilibrium conformations of charged star block copolymers as surfactants.

Structure-Based Design of Dual Bactericidal and Bacteria-Releasing Nanosurfaces

Here, we report synergistic nanostructured surfaces combining bactericidal and bacteria-releasing properties. A polystyrene-block-poly(methyl methacrylate) (PS-block-PMMA) diblock copolymer is used to fabricate vertically oriented cylindrical PS structures ("PS nanopillars") on silicon substrates. The results demonstrate that the PS nanopillars (with a height of about 10 nm, size of about 50 nm, and spacing of about 70 nm) exhibit highly effective bactericidal and bacteria-releasing properties ("dual properties") against Escherichia coli for at least 36 h of immersion in an E. coli solution. Interestingly, the PS nanopillars coated with a thin layer (≈3 nm thick) of titanium oxide (TiO₂) ("TiO₂ nanopillars") show much improved dual properties against E. coli (a Gram-negative bacterium) compared to the PS nanopillars. Moreover, the dual properties emerge against Listeria monocytogenes (a Gram-positive bacterium). To understand the mechanisms underlying the multifaceted property of the nanopillars, coarse-grained molecular dynamics (MD) simulations of a lipid bilayer (as a simplified model for E. coli) in contact with a substrate containing hexagonally packed hydrophilic nanopillars were performed. The MD results demonstrate that when the bacterium-substrate interaction is strong, the lipid heads adsorb onto the nanopillar surfaces, conforming the shape of a lipid bilayer to the structure/curvature of nanopillars and generating high stress concentrations within the membrane (i.e., the driving force for rupture) at the edge of the nanopillars. Membrane rupture begins with the formation of pores between nanopillars (i.e., bactericidal activity) and ultimately leads to the membrane withdrawal from the nanopillar surface (i.e., bacteria-releasing activity). In the case of Gram-positive bacteria, the adhesion area to the pillar surface is limited due to the inherent stiffness of the bacteria, creating higher stress concentrations within a bacterial cell wall. The present study provides insight into the mechanism underlying the "adhesion-mediated" multifaceted property of nanosurfaces, which is crucial for the development of next-generation antibacterial surface coatings for relevant medical applications.

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

Extracting Inelastic Scattering Cross Sections for Finite and Aperiodic Materials from Electronic Dynamics Simulations

Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. In this paper, it is demonstrated that electronic dynamics simulations can similarly be employed to study cross sections for the scattering-induced electronic excitations probed in nonresonant inelastic X-ray scattering and momentum-resolved electron energy loss spectroscopies. A method is put forth for evaluating the electronic dynamic structure factor, which involves the application of a momentum boost-type perturbation and transformation of the resulting reciprocal space density fluctuations into the frequency domain. Good agreement is first demonstrated between the dynamic structure factor extracted from these electronic dynamics simulations and the corresponding transition matrix elements from linear response theory. The method is then applied to some extended (quasi)one-dimensional systems, for which the wave vector becomes a good quantum number in the thermodynamic limit. Finally, the dispersion of many-body excitations in a series of hydrogen-terminated graphene flakes (and twisted bilayers thereof) is investigated to highlight the utility of the presented approach for capturing morphology-dependent effects in the inelastic scattering cross sections of nanostructured and/or noncrystalline materials.

Fingerprinting Brownian Motions of Polymers under Flow

We present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universal features. For both unentangled and entangled melts, the center-of-mass motions in the flow frame are described by superdiffusive, anisotropic Gaussian distributions, whereas the isotropic component of monomer self-dynamics in the center-of-mass frame is strongly suppressed. Spatial correlation analysis shows that the heterogeneity of monomer self-dynamics increases significantly under flow.

Decoding polymer self-dynamics using a two-step approach

The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. We show that the interpretation of non-Gaussian behavior of polymers is generally complicated by intrachain averaging of distinct self-dynamics of different segments. By the very nature of the mathematics involved, the averaging process not only conceals critical dynamical information, but also contributes to the observed non-Gaussian dynamics. To fully expose this issue and provide a thorough benchmark of polymer self-dynamics, we perform analyses of coarse-grained molecular dynamics simulations of linear and ring polymer melts as well as several theoretical models using a "two-step" approach, where interchain and intrachain averagings of segmental self-dynamics are separated. While past investigations primarily focused on the average behavior, our results indicate that a more nuanced approach to polymer self-dynamics is clearly required.

Understanding the Impacts of Support-Polymer Interactions on the Dynamics of Poly(ethyleneimine) Confined in Mesoporous SBA-15

Supported amines are a promising class of CO₂ sorbents offering large uptake capacities and fast uptake rates. Among supported amines, poly(ethyleneimine) (PEI) physically impregnated in the mesopores of SBA-15 silica is widely used. Within these composite materials, the chain dynamics and morphologies of PEI strongly influence the CO₂ capture performance, yet little is known about chain and macromolecule mobility in confined pores. Here, we probe the impact of the support-PEI interactions on the dynamics and structures of PEI at the support interface and the corresponding impact on CO₂ uptake performance, which yields critical structure-property relationships. The pore walls of the support are grafted with organosilanes with different chemical end groups to differentiate interaction modes (spanning from strong attraction to repulsion) between the pore surface and PEI. Combinations of techniques, such as quasi-elastic neutron scattering (QENS), ¹H T₁-T₂ relaxation correlation solid-state NMR, and molecular dynamics (MD) simulations, are used to comprehensively assess the physical properties of confined PEI. We hypothesized that PEI would have faster dynamics when subjected to less attractive or repulsive interactions. However, we discover that complex interfacial interactions resulted in complex structure-property relationships. Indeed, both the chain conformation of the surface-grafted chains and of the PEI around the surface influenced the chain mobility and CO₂ uptake performance. By coupling knowledge of the dynamics and distributions of PEI with CO₂ sorption performance and other characteristics, we determine that the macroscopic structures of the hybrid materials dictate the first rapid CO₂ uptake, and the rate of CO₂ sorption during the subsequent gradual uptake stage is determined by PEI chain motions that promote diffusive jumps of CO₂ through PEI-packed domains.

Small angle scattering of diblock copolymers profiled by machine learning

We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

Highly fluorescent purine-containing conjugated copolymers with tailored optoelectronic properties

Conjugated copolymers containing electron donor and acceptor units in their main chain have emerged as promising materials for organic electronic devices due to their tunable optoelectronic properties.

On-surface cyclodehydrogenation reaction pathway determined by selective molecular deuterations

Understanding the reaction mechanisms of dehydrogenative C_aryl–C_aryl coupling is the key to directed formation of π-extended polycyclic aromatic hydrocarbons. Here we utilize isotopic labeling to identify the exact pathway of cyclodehydrogenation reaction in the on-surface synthesis of model atomically precise graphene nanoribbons (GNRs). Using selectively deuterated molecular precursors, we grow seven-atom-wide armchair GNRs on a Au(111) surface that display a specific hydrogen/deuterium (H/D) pattern with characteristic Raman modes. A distinct hydrogen shift across the fjord of C_aryl–C_aryl coupling is revealed by monitoring the ratios of gas-phase by-products of H₂, HD, and D₂ with in situ mass spectrometry. The identified reaction pathway consists of a conrotatory electrocyclization and a distinct [1,9]-sigmatropic D shift followed by H/D eliminations, which is further substantiated by nudged elastic band simulations. Our results not only clarify the cyclodehydrogenation process in GNR synthesis but also present a rational strategy for designing on-surface reactions towards nanographene structures with precise hydrogen/deuterium isotope labeling patterns.

Selective deuterations were exploited to synthesize graphene nanoribbons on Au(111) surface with a specific H/D pattern on edges, allowing the determination of cyclodehydrogenation reaction pathway within the framework of pericyclic reactions.

Spatial correlations of entangled polymer dynamics

The spatial correlations of entangled polymer dynamics are examined by molecular dynamics simulations and neutron spin-echo spectroscopy. Due to the soft nature of topological constraints, the initial spatial decays of intermediate scattering functions of entangled chains are, to the first approximation, surprisingly similar to those of an unentangled system in the functional forms. However, entanglements reveal themselves as a long tail in the reciprocal-space correlations, implying a weak but persistent dynamic localization in real space. Comparison with a number of existing theoretical models of entangled polymers suggests that they cannot fully describe the spatial correlations revealed by simulations and experiments. In particular, the strict one-dimensional diffusion idea of the original tube model is shown to be flawed. The dynamic spatial correlation analysis demonstrated in this work provides a useful tool for interrogating the dynamics of entangled polymers. Lastly, the failure of the investigated models to even qualitatively predict the spatial correlations of collective single-chain density fluctuations points to a possible critical role of incompressibility in polymer melt dynamics.

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Revealing the Chemical Bonding in Adatom Arrays via Machine Learning of Hyperspectral Scanning Tunneling Spectroscopy Data

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co₃Sn₂S₂ cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co₃Sn₂S₂ surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

MX Anti-MXenes from Non-van der Waals Bulks for Electrochemical Applications: The Merit of Metallicity and Active Basal Plane

Two-dimensional transition-metal compounds (2DTMCs) are promising materials for electrochemical applications, but 2DTMCs with metallicity and active basal planes are rare. In this work, we proposed a simple and effective strategy to extract 2DTMCs from non-van der Waals bulk materials and established a material library of 79 2DTMCs, which we named as anti-MXenes since they are composed of one M atomic layer sandwiched by two X atomic layers. By means of density functional theory computations, 24 anti-MXenes were confirmed to be thermodynamically, dynamically, mechanically, and thermally stable. The metallicity and active basal plane endow these anti-MXenes with potential as excellent electrode materials, for example, as electrocatalysts for hydrogen evolution reactions (HER). Among the noble-metal free anti-MXenes with favorable H-binding, CuS can boost HER at the whole range of H coverages, while CoSi, FeB, CoB, and CoP show promise for HER at some specific H coverages. The active sites are the tetra-coordinating nonmetal atoms at the basal planes, thus rendering a very high density of active sites for these materials. CoB is also a promising anode material for lithium-ion batteries, showing low Li diffusion energy barriers, a very high capacity, and a suitable open circuit voltage. This work promotes the "computational exfoliation" of 2D materials from non-van der Waals bulks and exemplifies the applications of anti-MXenes in various electrochemical processes.

Interactions of an Imine Polymer with Nanoporous Silica and Carbon in Hybrid Adsorbents for Carbon Capture

Efficient carbon capture from stationary point sources can be achieved using hybrid adsorbents comprising nanoporous substrates coated with imine polymers. The physical properties of the CO₂-adsorbing, nanodispersed polymers are altered by their interactions with the substrate, which in turn may impact their capture capacity. We study silica and carbon nanoporous substrates with different pore morphologies that were impregnated with polymer imine with the goal of characterizing the polymer dispersions in the pores. For silica and carbon samples, the mean densities of confined poly(ethylene imine) (PEI) were measured as functions of polymer loading and temperature using small-angle neutron scattering. Strong densification is found for imine polymers imbibed in mesoporous carbon. PEI in nanoporous silica does not experience this strong densification. At high loadings, plugs form, preferably at the pore throats, and can reduce accessible porosity. CO₂ capture measurements show that PEI interactions with the substrate play an important role. PEI in carbon shows the highest capture capacity at low temperatures and the lowest CO₂ adsorption at high temperatures, making it well-suited for temperature swing adsorption applications.

Spatiotemporal mapping of mesoscopic liquid dynamics

The study of liquid dynamics at mesoscopic scales is still strewn with difficulty due to limitations in theory and experiment. Historically, significant attention has been given to the analysis of space-time correlation functions and their frequency-Fourier transforms at a few discrete wave numbers. The massive computing power afforded by modern high performance computing clusters and the advent of a wide-angle neutron spin-echo spectrometer, however, have unlocked a more intuitive and fruitful approach to this problem. Using molecular dynamics simulations, here we demonstrate the benefits of spatiotemporally mapping intermediate scattering functions on a dense grid of correlation times and wave numbers. Four model systems are investigated: a Lennard-Jones liquid, a coarse-grained bead-spring polymer, a molten sodium chloride, and a poly(ethylene oxide) melt. We show that the spatiotemporal mapping approach is particularly useful for elucidating the mesoscopic dynamics in these liquids, where several underlying mechanisms, such as molecular relaxations, hydrodynamic modes, and nonhydrodynamic excitations, are potentially at play. Compared to the traditional method, direct visualization of density space-time correlation functions on two-dimensional color maps permits appraisals of complicated dynamical behavior at mesoscales in a global manner. For example, the scaling relations between space and time for different types of molecular motions can be straightforwardly identified on these plots, without any model-dependent analysis. Additionally, we show how theoretical ideas regarding collective mesoscopic dynamics, such as the classical hydrodynamic theory, the convolution approximation, and a recently proposed phenomenological model, can be discussed in terms of the global features of spatiotemporal maps of intermediate scattering functions. The new perspective offered by the spatiotemporal mapping method should prove useful for the study of liquid dynamics in general.

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

Electron-Beam-Induced Molecular Plasmon Excitation and Energy Transfer in Silver Molecular Nanowires

We investigate (1) electron-beam-induced plasmon absorption spectra of Ag molecular nanowire dimers and (2) electron-beam-induced energy transfer between two nanowires. We employ linear-response time-dependent density functional theory (TDDFT) and real-time TDDFT methods to simulate the electron-beam-induced plasmonic excitations, dynamics, and corresponding electron energy loss spectrum for small models of a single molecular nanowire with four Ag atoms and for two Ag nanowires. An array of different relative orientations of nanowires and of different initial excitation conditions resulting from applying an electron beam at different positions with respect to the Ag nanowires is investigated. The results demonstrate (1) an electron beam can induce plasmonic excitations from the molecular Ag nanowire ground state to the excited states that are both optically allowed and forbidden, (2) a tunability for selective excitations that can be controlled by the position of a focused electron beam, and (3) kinetic and dynamic behaviors of time-dependent electron-beam-induced energy transfer between two Ag molecular nanowires depend on the position of the beam source and nanowire separation distance, providing insights into the spatial dependences of plasmonic couplings in nanowire arrays.

Nonadiabatic Effects on Defect Diffusion in Silicon-Doped Nanographenes

Single atom impurities in graphene, substitutional silicon defects in particular, have been observed to diffuse under electron beam irradiation. However, the relative importance of elastic and inelastic scattering in facilitating their mobility remains unclear. Here, we employ excited-state electronic structure calculations to explore potential inelastic effects, and find an electronically nonadiabatic excited-state silicon diffusion pathway involving "softened" Si-C bonding that presents an ∼2 eV lower diffusion barrier than the ground-state pathway. Beam-induced transition rates to this state indicate that the excited-state pathway is accessible through irradiation of the defect site. However, even in the limit of fully elastic scattering, upward nonadiabatic transitions are also possible along the diffusion coordinate, increasing the diffusion barrier and further demonstrating the potential for electronic nonadiabaticity to influence beam-induced atomic transformations in materials. We also propose some experimentally testable signatures of such excited-state pathways.

Mapping the Interfacial Chemistry and Structure of Partially Fluorinated Bottlebrush Polymers and Their Linear Analogues

Polymer interfaces are key to a range of applications including membranes for chemical separations, hydrophobic coatings, and passivating layers for antifouling. While important, challenges remain in probing the interfacial monolayer where the molecular ordering and orientation can change depending on the chemical makeup or processing conditions. In this work, we leverage surface specific vibrational sum frequency generation (SFG) and the associated dependence on molecular symmetry to elucidate the ordering and orientations of key functional groups for poly(2,2,2-trifluoroethyl methacrylate) bottlebrush polymers and their linear polymer analogues. These measurements were framed by atomistic molecular dynamic simulations to provide a complementary physical picture of the gas-polymer interface. Simulations and SFG measurements show that methacrylate backbones are buried beneath a layer of trifluoroethyl containing side groups that result in structurally similar interfaces regardless of the polymer molecular weight or architecture. The average orientational angles of the trifluoroethyl containing side groups differ depending on polymer linear and bottlebrush architectures, suggesting that the surface groups can reorient via available rotational degrees of freedom. Results show that the surfaces of the bottlebrush and linear polymer samples do not strongly depend on molecular weight or architecture. As such, one cannot rely on increasing the molecular weight or altering the architecture to tune surface properties. This insight into the polymer interfacial structure is expected to advance the design of new material interfaces with tailored chemical/functional properties.

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.

Modulating Microphase Separation of Lamellae-Forming Diblock Copolymers via Ionic Junctions

We present a molecular dynamics simulation study investigating the phase behavior of lamellae-forming diblock copolymers with a single ionic junction on the backbone. Our results show qualitative agreement with experimental findings regarding enhanced microphase separation with the introduction of an ionic junction at the conjunction point, while further revealing nonmonotonic changes in domain spacing and order-disorder transition as a function of the electrostatic interaction strength. This highlights the dominant roles of entropic and binding effects of counterions under weak and strong ionic correlations, respectively. The location of the ionic junction is found to effectively modulate the charge distribution and chain conformation in the ordered domains; its presence in the middle of a block promotes folding of the block, leading to a smaller domain size. These findings demonstrate the interplay of ionic coupling with steric hindrance and chain end effects, which enhances our understanding of the delicate control over the microphase domain features.

Density-functional tight-binding for phosphine-stabilized nanoscale gold clusters

We report a parameterization of the second-order density-functional tight-binding (DFTB2) method for the quantum chemical simulation of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces. Our parameterization extends the previously released DFTB2 "auorg" parameter set by connecting it to the electronic parameter of phosphorus in the "mio" parameter set. Although this connection could technically simply be accomplished by creating only the required additional Au-P repulsive potential, we found that the Au 6p and P 3d virtual atomic orbital energy levels exert a strong influence on the overall performance of the combined parameter set. Our optimized parameters are validated against density functional theory (DFT) geometries, ligand binding and cluster isomerization energies, ligand dissociation potential energy curves, and molecular orbital energies for relevant phosphine-ligated Au n clusters (n = 2-70), as well as selected experimental X-ray structures from the Cambridge Structural Database. In addition, we validate DFTB simulated far-IR spectra for several phosphine- and thiolate-ligated gold clusters against experimental and DFT spectra. The transferability of the parameter set is evaluated using DFT and DFTB potential energy surfaces resulting from the chemisorption of a PH3 molecule on the gold (111) surface. To demonstrate the potential of the DFTB method for quantum chemical simulations of metalloid gold clusters that are challenging for traditional DFT calculations, we report the predicted molecular geometry, electronic structure, ligand binding energy, and IR spectrum of Au108S24(PPh3)16.

Double membrane formation in heterogeneous vesicles

Lipids are capable of forming a variety of structures, including multi-lamellar vesicles. Layered lipid membranes are found in cell organelles, such as autophagosomes and mitochondria. Here, we present a mechanism for the formation of a double-walled vesicle (i.e., two lipid bilayers) from a unilamellar vesicle through the partitioning and phase separation of a small molecule. Using molecular dynamics simulations, we show that double membrane formation proceeds via a nucleation and growth process - i.e., after a critical concentration of the small molecules, a patch of double membrane nucleates and grows to cover the entire vesicle. We discuss the implications of this mechanism and theoretical approaches for understanding the evolution and formation of double membranes.

Capacitance of thin films containing polymerized ionic liquids

Electrode-polymer interfaces dictate many of the properties of thin films such as capacitance, the electric field experienced by polymers, and charge transport. However, structure and dynamics of charged polymers near electrodes remain poorly understood, especially in the high concentration limit representative of the melts. To develop an understanding of electric field-induced transformations of electrode-polymer interfaces, we have studied electrified interfaces of an imidazolium-based polymerized ionic liquid (PolyIL) using combinations of broadband dielectric spectroscopy, specular neutron reflectivity, and simulations based on the Rayleigh's dissipation function formalism. Overall, we obtained the camel-shaped dependence of the capacitance on applied voltage, which originated from the responses of an adsorbed polymer layer to applied voltages. This work provides additional insights related to the effects of molecular weight in affecting structure and properties of electrode-polymer interfaces, which are essential for designing next-generation energy storage and harvesting devices.

The role of mid-gap phonon modes in thermal transport of transition metal dichalcogenides

We present a comprehensive theoretical study on thermal transport in monolayer transition metal dichalcogenides MX₂ (M: Mo, W; X: S, Se) with various sample sizes. An unusually high anharmonic scattering strength is found in MoSe₂ compared to the other three family members, which arises from its unique phonon band dispersion, specifically the mid-frequency phonon branches associated with the vibrations of Se atoms of MoSe₂. The mid-frequency modes almost completely span the gap that exists between the high-frequency phonon branches and the acoustic ones, allowing the former to readily decay into the latter. The resultant high anharmonic scattering gives rise to a short mean free path which makes the room temperature in-plane thermal conductivity in MoSe₂ even lower than WSe₂ when the sample length is larger than 51.5 nm. With varying sample sizes, the ordering of thermal conductivity among the four materials changes as phonon transport transits from the ballistic to diffusive regime, driven by the competition between the phonon frequency spectrum range and the scattering strength. Our work provides a microscopic picture of phonon transport in TMDs and guidance to tailor their thermal conductivities for electronic and thermoelectric applications.

Hierarchical TiO2:Cu2O Nanostructures for Gas/Vapor Sensing and CO2 Sequestration

We investigate the effect of high-surface-area self-assembled TiO₂:Cu₂O nanostructures for CO₂ and relative humidity gravimetric detection using polyethylenimine (PEI), 1-ethyl-3-methylimidazolium (EMIM), and polyacrylamide (PAAm). Introduction of hierarchical TiO₂:Cu₂O nanostructures on the surface of quartz crystal microbalance sensors is found to significantly improve sensitivity to CO₂ and to H₂O vapor. The response of EMIM to CO₂ increases fivefold for 100 nm-thick TiO₂:Cu₂O as compared to gold. At ambient CO₂ concentrations, the hierarchical assembly operates as a sensor with excellent reversibility, while at higher pressures, the CO₂ desorption rate decreases, suggesting possible application for CO₂ sequestration under these conditions. The gravimetric response of PEI to CO₂ increases by a factor of 3 upon introduction of a 50 nm TiO₂:Cu₂O layer. The PAAm gravimetric response to water vapor also increases by a factor of 3 and displays improved reversibility with the addition of 50 nm TiO₂:Cu₂O structures. We found that TiO₂:Cu₂O can be used to lower the detection limits for CO₂ sensing with EMIM and PEI and lower the detection limits for H₂O sensing with PAAm by over a factor of 2. Coarse-grained and all-atom molecular dynamics simulations indicate the dissociative character of ionic liquid assembly on TiO₂:Cu₂O interfaces and different distributions of CO₂ and H₂O molecules on bare and ionic liquid-coated surfaces, confirming experimental observations. Overall, our results show high potential of hierarchical assemblies of TiO₂:Cu₂O/room temperature ionic liquid and polymer films for sensors and CO₂ sequestration.

Orally Bioavailable Androgen Receptor Degrader, Potential Next-Generation Therapeutic for Enzalutamide-Resistant Prostate Cancer

PURPOSE

Androgen receptor (AR)-targeting prostate cancer drugs, which are predominantly competitive ligand-binding domain (LBD)-binding antagonists, are inactivated by common resistance mechanisms. It is important to develop next-generation mechanistically distinct drugs to treat castration- and drug-resistant prostate cancers.

EXPERIMENTAL DESIGN

Second-generation AR pan antagonist UT-34 was selected from a library of compounds and tested in competitive AR binding and transactivation assays. UT-34 was tested using biophysical methods for binding to the AR activation function-1 (AF-1) domain. Western blot, gene expression, and proliferation assays were performed in various AR-positive enzalutamide-sensitive and -resistant prostate cancer cell lines. Pharmacokinetic and xenograft studies were performed in immunocompromised rats and mice.

RESULTS

UT-34 inhibits the wild-type and LBD-mutant ARs comparably and inhibits the in vitro proliferation and in vivo growth of enzalutamide-sensitive and -resistant prostate cancer xenografts. In preclinical models, UT-34 induced the regression of enzalutamide-resistant tumors at doses when the AR is degraded; but, at lower doses, when the AR is just antagonized, it inhibits, without shrinking, the tumors. This indicates that degradation might be a prerequisite for tumor regression. Mechanistically, UT-34 promotes a conformation that is distinct from the LBD-binding competitive antagonist enzalutamide and degrades the AR through the ubiquitin proteasome mechanism. UT-34 has a broad safety margin and exhibits no cross-reactivity with G-protein-coupled receptor kinase and nuclear receptor family members.

CONCLUSIONS

Collectively, UT-34 exhibits the properties necessary for a next-generation prostate cancer drug.