PAL 2.0: a physics-driven bayesian optimization framework for material discovery.

The lack of efficient discovery tools for advanced functional materials remains a major bottleneck to enabling advances in the next-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials (with respect to material compositions and processing conditions) that is typically redolent of such materials-centric applications. Searches of this large combinatorial space are often influenced by expert knowledge and clustered close to material configurations that are known to perform well, thus ignoring potentially high-performing candidates in unanticipated regions of the composition-space or processing protocol. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible material candidates can be prohibitively expensive, making exhaustive approaches to determine the best candidates infeasible. As a result, there remains a need for the development of computational algorithms that can efficiently search a large parameter space for a given material application. Here, we introduce PAL 2.0, a method that combines a physics-based surrogate model with Bayesian optimization. The key contributing factor of our proposed framework is the ability to create a physics-based hypothesis using XGBoost and Neural Networks. This hypothesis provides a physics-based "prior" (or initial beliefs) to a Gaussian process model, which is then used to perform a search of the material design space. In this paper, we demonstrate the usefulness of our approach on three material test cases: (1) discovery of metal halide perovskites with desired photovoltaic properties, (2) design of metal halide perovskite-solvent pairs that produce the best solution-processed films and (3) design of organic thermoelectric semiconductors. Our results indicate that the novel PAL 2.0 approach outperforms other state-of-the-art methods in its efficiency to search the material design space for the optimal candidate. We also demonstrate the physics-based surrogate models constructed in PAL 2.0 have lower prediction errors for material compositions not seen by the model. To the best of our knowledge, there is no competing algorithm capable of this useful combination for materials discovery, especially those for which data are scarce.

A multiscale approach to uncover the self-assembly of ligand-covered palladium nanocubes.

Ligand-mediated superlattice assemblies of metallic nanocrystals represent a new type of mesoscale materials whose structural ordering directly influence emergent collective properties. However, universal control over the spatial and orientational ordering of their constitutive components remains an open challenge. One major barrier contributing to the lack of programmability in these nanoscale building blocks revolves around a gap in fundamental understanding of how ligand-mediated interactions at the particle level propagate to macroscopic and mesoscale behaviors. Here, we employ a combination of scaling theory and coarse-grained simulations to develop a multiscale modeling framework capable of bridging across hierarchical assembly length scales for a model system of ligand-functionalized nanocubes (here, Pd). We first employ atomistic simulations to characterize how specific ligand-ligand interactions influence the local behaviors between neighboring Pd nanocubes. We then utilize a mean-field scaling theory to both rationalize the observed behaviors as well as compute a coarse-grained effective pairwise potential between nanocubes capable of reproducing atomistic behaviors at the mesoscale. Furthermore, our simulations reveal that a complex interplay between ligand-ligand interactions is directly responsible for a shift in macroscopic ordering between neighboring nanocubes. Our results, therefore, provides a critical step forward in establishing a multiscale understanding of ligand-functionalized nanocrystalline assemblies that can be subsequently leveraged to design targeted structures exhibiting novel, emergent collective properties.

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning.

Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid-liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

Blended Conjugated Host and Unconjugated Dopant Polymers Towards N-type All-Polymer Conductors and High-ZT Thermoelectrics.

N-Type thermoelectrics typically consist of small molecule dopant+polymer host. Only a few polymer dopant+polymer host systems have been reported, and these have lower thermoelectric parameters. N-type polymers with high crystallinity and order are generally used for high-conductivity ( σ ${\sigma }$ ) organic conductors. Few n-type polymers with only short-range lamellar stacking for high-conductivity materials have been reported. Here, we describe an n-type short-range lamellar-stacked all-polymer thermoelectric system with highest σ ${\sigma }$ of 78 S-1 , power factor (PF) of 163 µW m-1 K-2 , and maximum Figure of merit (ZT) of 0.53 at room temperature with a dopant/host ratio of 75 wt%. The minor effect of polymer dopant on the molecular arrangement of conjugated polymer PDPIN at high ratios, high doping capability, high Seebeck coefficient (S) absolute values relative to σ ${\sigma }$ , and atypical decreased thermal conductivity ( κ ${\kappa }$ ) with increased doping ratio contribute to the promising performance.

Machine Learning Approach to Enable Spectral Imaging Analysis for Particularly Complex Nanomaterial Systems.

Scanning transmission electron microscopy-based electron energy loss spectroscopy spectral imaging (STEM-EELS-SI) has been widely used in material research to capture a wealth of information, including elemental, electron density, and bonding state distributions. However, its exploitation still faces many challenges due to the difficulty of extracting information from noisy and overlapping edges in the convoluted spatial and spectroscopic data set. A traditional EELS spectral imaging analysis lacks the capability to isolate noise and deconvolute such overlapping edges, which either limits the resolution or the signal-to-noise ratio of the maps generated by EELS-SI. Existing machine learning (ML) algorithms can achieve denoising and deconvolution to a certain extent, but the extracted spectra lack physical meaning. To address these challenges, we have developed a ML method tailored to a spectral imaging analysis system and based on a non-negative robust principal component analysis. This approach offers an effective way to analyze EELS spectral images with improved space-time resolution, signal-to-noise ratio, and the capability to separate subtle differences in the spectrum. We apply this algorithm to 13 nanomaterial systems to show that ML can greatly improve image quality compared to a traditional approach, especially for more challenging systems. This will expand the type of nanomaterial systems that can be characterized by EELS-SI, and aid the analysis of structural, chemical, and electronic properties that are otherwise difficult to obtain.

A comprehensive picture of roughness evolution in organic crystalline growth: the role of molecular aspect ratio.

Exploiting the capabilities of organic semiconductors for applications ranging from light-emitting diodes to photovoltaics to lasers relies on the creation of ordered, smooth layers for optimal charge carrier mobilities and exciton diffusion. This, in turn, creates a demand for organic small molecules that can form smooth thin film crystals via homoepitaxy. We have studied a set of small-molecule organic semiconductors that serve as templates for homoepitaxy. The surface roughness of these materials is measured as a function of adlayer film thickness from which the growth exponent (ß) is extracted. Notably, we find that three-dimensional molecules that have low molecular aspect ratios (AR) tend to remain smooth as thickness increases (small ß). This is in contrast to planar or rod-like molecules with high AR that quickly roughen (large ß). Molecular dynamics simulations find that the Ehrlich-Schwöbel barrier (EES) alone is unable to fully explain this trend. We further investigated the mobility of ad-molecules on the crystalline surface to categorize their diffusion behaviors and the effects of aggregation to account for the different degrees of roughness that we observed. Our results suggest that low AR molecules have low molecular mobility and moderate EES which creates a downward funneling effect leading to smooth crystal growth.

Untying the Cesium "Not": Cesium-Iodoplumbate Complexation in Perovskite Solution-Processing Inks Has Implications for Crystallization.

We illustrate the critical importance of the energetics of cation-solvent versus cation-iodoplumbate interactions in determining the stability of ABX3 perovskite precursors in a dimethylformamide (DMF) solvent medium. We have shown, through a complementary suite of nuclear magnetic resonance (NMR) and computational studies, that Cs+ exhibits significantly different solvent vs iodoplumbate interactions compared to organic A+-site cations such as CH3NH3+ (MA+). Two NMR studies were conducted: 133Cs NMR analysis shows that Cs+ and MA+ compete for coordination with PbI3- in DMF. 207Pb NMR studies of PbI2 with cationic iodides show that perovskite-forming Cs+ (and, somewhat, Rb+) do not comport with the 207Pb chemical shift trend found for Li+, Na+, and K+. Three independent computational approaches (density functional theory (DFT), ab initio Molecular Dynamics (AIMD), and a polarizable force field within Molecular Dynamics) yielded strikingly similar results: Cs+ interacts more strongly with the PbI3- iodoplumbate than does MA+ in a polar solvent environment like DMF. The stronger energy preference for PbI3- coordination of Cs+ vs MA+ in DMF demonstrates that Cs+ is not simply a postcrystallization cation "fit" for the perovskite A+-site. Instead, it may facilitate preorganization of the framework precursor that eventually transforms into the crystalline perovskite structure.

Protocol for Directing Nudged Elastic Band Calculations to the Minimum Energy Pathway: Nurturing Errant Calculations Back to Convergence.

The combination of density functional theory (DFT) and the nudged elastic band (NEB) method offers a practical tool for the discovery of underlying reaction mechanisms related to the synthesis of functional materials. However, in practice, the lack of a standardized protocol for minimum energy pathway determination too often leads to an inefficient and computationally intensive design process. To that end, we define a verifiable DFT+NEB protocol for efficiently locating and confirming the transition state of a reaction. To test this assertion, we curate 226 unique reactions within 14 classes of reactions and investigate their performance in terms of the number of NEB iterations they require to locate the transition state and an estimate of the associated mean absolute error. Leveraging this protocol, we demonstrate its application for an initial set of parameters: number of frames, Nframes = 11; maximum step size, Smax = 0.04 Å; optimizer = LBFGS; and spring constant, kspr = 0.1 eV/Å2. We report a convergence rate of 73% and find that a root-mean-square force (FRMS) of 0.01 eV/Å provides a "rule of thumb" below which NEB simulations are likely to converge. Venturing beyond this baseline enquiry, we delineate the effect on performance of altering the number of frames, maximum step size, choice of optimizer and spring constant. We find improvements in performance with increasing Nframes and Smax, ostensibly approaching some asymptotic limit. We also see substantial improvement in efficiency with the LBFGS optimizer and a clear minimum in performance for the spring constant value of 0.1 eV/Å2. Finally, we provide five case studies that demonstrate typical convergence issues for NEB simulations and suggest methods to overcome them. Our results provide specific and transferable recommendations, offering a transparent and practical tool for beginner and expert researchers alike toward a more rational NEB simulation design.

Molecular modeling of interfacial layer-by-layer assembly towards functionalized capsule materials.

Encapsulated nanomaterials, such as polymer-coated nanoemulsions, have highly tunable properties leading to versatile applications. A current lack of understanding of the fundamentals governing the choice of "capsule" materials (polyelectrolyte + surfactant) and its ensuing performance effectively precludes their widespread use. Computational methods can start to redress this by discovering molecule-scale attributes that significantly control the design of capsule materials tuned to fit desired properties. We use molecular dynamics (MD) to carry out the layer-by-layer (LbL) assembly of six unique polyelectrolyte bilayer systems at a surfactant-mediated interface, modeling early-stage capsule synthesis. Monolayer thickness is related to layer density and polyelectrolyte/surfactant interaction energy through polyelectrolyte molecular weight and radius of gyration, respectively, yielding a simple relationship between absorption kinetics and layer structure. For the second monolayer, faster absorption kinetics are observed for pairings of polyelectrolytes with similarly sized functional groups. Surfactants with a more delocalized charge on the head-group catalyze the build-up of ions at the interface, resulting in faster absorption kinetics and greater confinement of the encapsulated material but leading to thicker, less uniform bilayers. These relationships between capsule building block molecules and nanomaterial capsule properties provide a foundation for property prediction and rational design of optimized multi-functional capsule materials.

3,4,5-Trimethoxy Substitution on an N-DMBI Dopant with New N-Type Polymers: Polymer-Dopant Matching for Improved Conductivity-Seebeck Coefficient Relationship.

Achieving high electrical conductivity and thermoelectric power factor simultaneously for n-type organic thermoelectrics is still challenging. By constructing two new acceptor-acceptor n-type conjugated polymers with different backbones and introducing the 3,4,5-trimethoxyphenyl group to form the new n-type dopant 1,3-dimethyl-2-(3,4,5-trimethoxyphenyl)-2,3-dihydro-1H-benzo[d]imidazole (TP-DMBI), high electrical conductivity of 11 S cm-1 and power factor of 32 µW m-1 K-2 are achieved. Calculations using Density Functional Theory show that TP-DMBI presents a higher singly occupied molecular orbital (SOMO) energy level of -1.94 eV than that of the common dopant 4-(1, 3-dimethyl-2, 3-dihydro-1H-benzoimidazol-2-yl) phenyl) dimethylamine (N-DMBI) (-2.36 eV), which can result in a larger offset between the SOMO of dopant and lowest unoccupied molecular orbital (LUMO) of n-type polymers, though that effect may not be dominant in the present work. The doped polymer films exhibit higher Seebeck coefficient and power factor than films using N-DMBI at the same doping levels or similar electrical conductivity levels. Moreover, TP-DMBI doped polymer films offer much higher electron mobility of up to 0.53 cm2 V-1 s-1 than films with N-DMBI doping, demonstrating the potential of TP-DMBI, and 3,4,5-trialkoxy DMBIs more broadly, for high performance n-type organic thermoelectrics.

Ab Initiomodeling of Near-Edge EELS spectra for chemisorbed molecules.

Electron energy loss spectroscopy (EELS) has recently been applied to probe chemisorbed molecules on metal nanostructures, but a fundamental understanding of the correlation between these spectra and the electronic structures of the adsorbates has been limited. We report here on the insights afforded by time-dependent density functional theory to decipher the energy loss near edge structure (ELNES) of EELS spectra associated with chemisorption. These first-principles calculations simulate the ELNES-EELS spectra for chemisorbed CO on various facets of Au and Pt. Computational predictions of key signatures such as the 'red shift' and reductions in the peak intensity of the 2π* and 6σ* peaks, as compared to free CO in the gas phase, are validated in comparison to experimentally collected EELS spectra. These signatures are revealed to arise from changes in the electronic structure in terms of unoccupied density of states associated with the chemisorption process. They are consistent with a Blyholder model that incorporates donation and back-donation of electrons. They are also characteristic of the chemisorption process, such as the choice of metal, site of adsorption and the coverage and distribution of adsorbates. Our simulations thus provide guidelines for the use of ELNES-EELS to characterize the atomic structure and adsorption property of nanostructured surfaces and facilitate the development of advanced nanomaterials for catalytic applications.

Transferable Molecular Model of Woven Covalent Organic Framework Materials.

Two woven covalent organic framework materials (COF-505 and COF-506) have been synthesized since 2016, and the latter demonstrated the ability to take up dyes and other small molecules. This opens the door to applications such as separations, sensing, and catalysis. However, accelerating the design of future woven materials by changing the chemistry of the "threads" will require a computational model for these materials. Since no such atomic-scale model exists, we have developed a protocol for optimizing a force field for woven materials which can be used as the input to molecular dynamics simulations. Their high density and elasticity made these COFs challenging to model at a semiempirical level. Our modeling approach required simultaneous optimization of lattice parameters and elasticity using density functional theory-derived energy barriers and available experimental results. We used this force field, parameterized to fit COF-505, without change, to predict the structure of COF-506. This model allowed us to predict an anisotropy in 505's elasticity and preferred directions for diffusion which cannot be seen experimentally. The pore size distribution for 506 is dominated by small pores (80% <10 Å dia.), though 5% of the pores are up to 20 Å in diameter. We confirmed the experimental result that gases (barring helium) do not diffuse appreciably in COF-505. We validated our (unaltered) force field model to accurately predict experimental uptake data for tetrahydrofuran and methyl orange dye in COF-506. We proposed an atomic-scale mechanism by which COF-505 becomes metallated and demetallated. In addition, in advance of experimental studies, we determined the ability of 505 to incorporate other metals, such as Zn and Fe, which might be considered artificial photosynthesis agents. These predictions validate that Cu was a particularly appropriate choice of metal center for the synthesis, showcasing the ability of this model to play a role in designing woven materials a priori.

The Role of Dimer Formation in the Nucleation of Superlattice Transformations and Its Impact on Disorder.

The formation of defect-free two-dimensional nanocrystal (NC) superstructures remains a challenge as persistent defects hinder charge delocalization and related device performance. Understanding defect formation is an important step toward developing strategies to mitigate their formation. However, specific mechanisms of defect formation are difficult to determine, as superlattice phase transformations that occur during fabrication are quite complex and there are a variety of factors influencing the disorder in the final structure. Here, we use Molecular Dynamics (MD) and electron microscopy in concert to investigate the nucleation of the epitaxial attachment of lead chalcogenide (PbX, where X = S, Se) NC assemblies. We use an updated implementation of an existing reactive force field in an MD framework to investigate how initial orientational (mis)alignment of the constituent building blocks impacts the final structure of the epitaxially connected superlattice. This Simple Molecular Reactive Force Field (SMRFF) captures both short-range covalent forces and long-range electrostatic forces and allows us to follow orientational and translational changes of NCs during superlattice transformation. Our simulations reveal how robust the oriented attachment is with regard to the initial configuration of the NCs, measuring its sensitivity to both in-plane and out-of-plane misorientation. We show that oriented attachment nucleates through the initial formation of dimers, which corroborate experimentally observed structures. We present high-resolution structural analysis of dimers at early stages of the superlattice transformation and rationalize their contribution to the formation of defects in the final superlattice. Collectively, the simulations and experiments presented in this paper provide insights into the nucleation of NC oriented attachment, the impact of the initial configuration of NCs on the structural fidelity of the final epitaxially connected superlattice, and the propensity to form commonly observed defects, such as missing bridges and atomic misalignment in the superlattice due to the formation of dimers. We present potential strategies to mitigate the formation of superlattice defects.

Decomplexation as a rate limitation in the thiol-Michael addition of N-acrylamides.

The thiol-Michael addition is a popular, selective, high-yield "click" reaction utilized for applications ranging from small-molecule synthesis to polymer or surface modification. Here, we combined experimental and quantum mechanical modeling approaches using density functional theory (DFT) to examine the thiol-Michael reaction of N-allyl-N-acrylamide monomers used to prepare sequence-defined oligothioetheramides (oligoTEAs). Experimentally, the reaction was evaluated with two fluorous tagged thiols and several monomers at room temperature (22 °C and 40 °C). Using the Eyring equation, the activation energies (enthalpies) were calculated, observing a wide range of energy barriers ranging from 28 kJ mol-1 to 108 kJ mol-1 within the same alkene class. Computationally, DFT coupled with the Nudged Elastic Band method was used to calculate the entire reaction coordinate of each monomer reaction using the B97-D3 functional and a hybrid implicit-explicit methanol solvation approach. The thiol-Michael reaction is traditionally rate-limited by the propagation or chain-transfer steps. However, our test case with N-acrylamides and fluorous thiols revealed experimental and computational data produced satisfactory agreement only when we considered a previously unconsidered step that we termed "product decomplexation", which occurs as the product physically dissociates from other co-reactants after chain transfer. Five monomers were investigated to support this finding, capturing a range of functional groups varying in alkyl chain length (methyl to hexyl) and aromaticity (benzyl and ethylenephenyl). Increased substrate alkyl chain length increased activation energy, explained by the inductive effect. Aromatic ring-stacking configurations significantly impacted the activation energy and contributed to improved molecular packing density. Hydrogen-bonding between reactants increased the activation energy emphasizing the rate-limitation of the product decomplexation. Our findings begin to describe a new structure-kinetic relationship for thiol-Michael acceptors to enable further design of reactive monomers for synthetic polymers and biomaterials.

A Quantitative Metric for the Design of Selective Supercritical CO2 Extraction of Lithium from Geothermal Brine.

As demand grows for lithium, its recovery from geothermal brines provides an attractive alternative to slow mining. One promising extraction method uses crown ethers as extractants in supercritical carbon dioxide with cation exchangers to facilitate extraction from brine. Molecular dynamics modeling is used to understand the mechanism of binding between lithium (or sodium) and combinations of 14-crown-4 ethers and cation exchangers, and the predictive capability of computational modeling to test lithium selectivity is established for four combinations of crown ethers [methylene-14-crown-4 (M14C4) and a fluorinated 14-crown-4 (F14C4)] and cation exchangers [di(2-ethyl-hexyl)phosphoric acid (HDEHP) and tetraethylammonium perfluoro-1-octanesulfonate (TPFOS)]. Binding free energies (given in kcal mol-1 ) of lithium and sodium, respectively, to crown ether-cation exchangers are 85 and 71 for M14C4-HDEHP, 90 and 71 for F14C4-HDEHP, 93 and 80 for M14C4-TPFOS, and 104 and 93 for F14C4-TPFOS. Good agreement is found between computational predictions and supercritical carbon dioxide extraction experiments at 60 °C and 250 bar. Binding free energy gives a suitable metric to describe extraction efficiency. Differences in the binding free energies of sodium and lithium to crown ethers determine the extraction selectivity. Fluorine groups are found to exert a positive influence to optimize extraction efficiency. Of the systems studied, F14C4 with TPFOS offers the most selective and efficient extraction system.

Finite element and analytical solutions for van der Pauw and four-point probe correction factors when multiple non-ideal measurement conditions coexist.

This paper presents an extensive collection of calculated correction factors that account for the combined effects of a wide range of non-ideal conditions often encountered in realistic four-point probe and van der Pauw experiments. In this context, "non-ideal conditions" refer to conditions that deviate from the assumptions on sample and probe characteristics made in the development of these two techniques. We examine the combined effects of contact size and sample thickness on van der Pauw measurements. In the four-point probe configuration, we examine the combined effects of varying the sample's lateral dimensions, probe placement, and sample thickness. We derive an analytical expression to calculate correction factors that account, simultaneously, for finite sample size and asymmetric probe placement in four-point probe experiments. We provide experimental validation of the analytical solution via four-point probe measurements on a thin film rectangular sample with arbitrary probe placement. The finite sample size effect is very significant in four-point probe measurements (especially for a narrow sample) and asymmetric probe placement only worsens such effects. The contribution of conduction in multilayer samples is also studied and found to be substantial; hence, we provide a map of the necessary correction factors. This library of correction factors will enable the design of resistivity measurements with improved accuracy and reproducibility over a wide range of experimental conditions.

Ab Initio Studies of the Diffusion of Intrinsic Defects and Silicon Dopants in Bulk InAs.

We expose the predominant diffusional pathways for In and As in InAs, as well as dopant Si atoms in InAs, using Nudged Elastic Band calculations in conjunction with accurate Density Functional Theory calculations of the energy of defective systems. Our results show that As is a very fast diffuser compared to In and Si for both vacancy-assisted and interstitially mediated mechanisms. Larger indium atoms, on the other hand, are very slow diffusers and strongly prefer to remain on the In sublattice. Silicon also prefers to stay in substitutional sites in the In sublattice, in agreement with the fact that Si is used to create n-doped InAs. We find that the mechanism by which Si diffuses within the InAs lattice is very unlikely to proceed via vacancy-assisted jumps, since these routes encounter energy barriers above 2 eV. In contrast, silicon can readily make interstitial jumps since they occur with energy barriers as small as 0.23 eV. This suggests that an interstitial diffusion mechanism is strongly preferred for Si diffusion in InAs which challenges the common presumption made for another similar III-V compound, namely GaAs, that Si diffusion takes place via a vacancy-assisted mechanism.

Computational Implementation of Nudged Elastic Band, Rigid Rotation, and Corresponding Force Optimization.

The nudged elastic band (NEB) algorithm is the leading method of calculating transition states in chemical systems. However, the current literature lacks adequate guidance for users wishing to implement a key part of NEB, namely, the optimization method. Here, we provide details of this implementation for the following six gradient descent algorithms: steepest descent, quick-min Verlet, FIRE, conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno (BFGS), and limited-memory BFGS (LBFGS). We also construct and implement a new, accelerated backtracking line search method in concert with a partial Procrustes superimposition to improve upon existing methods. Validation is achieved through benchmark calculations of two test cases, the isomerization of CNX and BOX (where X ∈ {H, Li, Na}) and the study of a conformational change within an alanine dipeptide. We also make direct comparisons to the well-established codebase known as the atomic simulation environment.

Synthesis and Solution-Phase Characterization of Sulfonated Oligothioetheramides.

Nature has long demonstrated the importance of chemical sequence to induce structure and tune physical interactions. Investigating macromolecular structure and dynamics is paramount to understand macromolecular binding and target recognition. To that end, we have synthesized and characterized flexible sulfonated oligothioetheramides (oligo-TEAs) by variable temperature pulse field gradient (PFG) NMR, double electron-electron resonance (DEER), and molecular dynamics (MD) simulations to capture their room temperature structure and dynamics in water. We have examined the contributions of synthetic length (2-12mer), pendant group charge, and backbone hydrophobicity. We observe significant entropic collapse, driven in part by backbone hydrophobicity. Analysis of individual monomer contributions revealed larger changes due to the backbone compared to pendant groups. We also observe screening of intramolecular electrostatic repulsions. Finally, we comment on the combination of DEER and PFG NMR measurements via Stokes-Einstein-Sutherland diffusion theory. Overall, this sensitive characterization holds promise to enable de novo development of macromolecular structure and sequence-structure-function relationships with flexible, but biologically functional macromolecules.