Diverse Chemical Environments Research Articles

On the Performance of Second-Order Polarization Propagator Methods in the Calculation of 1JFC and nJFH NMR Spin-Spin Coupling Constants.

This study evaluates the performance of doubles-corrected random phase approximation (RPA) and higher random phase approximation (HRPA) approaches in predicting nuclear magnetic resonance (NMR) coupling constants involving fluorine. Their performance is benchmarked against experimental data and compared with that of higher-level theoretical methods, specifically second-order polarization propagator (SOPPA) and SOPPA(CCSD). Additionally, we discuss their performance relative to density functional theory (DFT). We find that RPA(D) is severely constrained by (near) triplet instabilities, while HRPA(D) demonstrates markedly improved stability. Statistical analysis reveals stronger patterns for carbon-fluorine couplings across the methods and systems investigated compared with fluorine-hydrogen couplings. While SOPPA-based methodologies prove to be superior in accuracy, HRPA(D) shows promising performance in reducing the computational burden of these calculations, albeit with a tendency to underestimate the coupling strength. These findings highlight the potential of HRPA(D) as a practical alternative to SOPPA methods, even for such difficult properties as NMR spin-spin coupling constants involving fluorine, emphasizing its role in improving predictive accuracy and efficiency across diverse chemical environments.

Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding.

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Syntheses and Applications of Coumarin-Derived Fluorescent Probes for Real-Time Monitoring of NAD(P)H Dynamics in Living Cells across Diverse Chemical Environments.

Fluorescent probes play a crucial role in elucidating cellular processes, with NAD(P)H sensing being pivotal in understanding cellular metabolism and redox biology. Here, the development and characterization of three fluorescent probes, A, B, and C, based on the coumarin platform for monitoring of NAD(P)H levels in living cells are described. Probes A and B incorporate a coumarin-cyanine hybrid structure with vinyl and thiophene connection bridges to 3-quinolinium acceptors, respectively, while probe C introduces a dicyano moiety for replacement of the lactone carbonyl group of probe A which increases the reaction rate of the probe with NAD(P)H. Initially, all probes exhibit subdued fluorescence due to intramolecular charge transfer (ICT) quenching. However, upon hydride transfer by NAD(P)H, fluorescence activation is triggered through enhanced ICT. Theoretical calculations confirm that the electronic absorption changes upon the addition of hydride to originate from the quinoline moiety instead of the coumarin section and end up in the middle section, illustrating how the addition of hydride affects the nature of this absorption. Control and dose-response experiments provide conclusive evidence of probe C's specificity and reliability in identifying intracellular NAD(P)H levels within HeLa cells. Furthermore, colocalization studies indicate probe C's selective targeting of mitochondria. Investigation into metabolic substrates reveals the influence of glucose, maltose, pyruvate, lactate, acesulfame potassium, and aspartame on NAD(P)H levels, shedding light on cellular responses to nutrient availability and artificial sweeteners. Additionally, we explore the consequence of oxaliplatin on cellular NAD(P)H levels, revealing complex interplays between DNA damage repair, metabolic reprogramming, and enzyme activities. In vivo studies utilizing starved fruit fly larvae underscore probe C's efficacy in monitoring NAD(P)H dynamics in response to external compounds. These findings highlight probe C's utility as a versatile tool for investigating NAD(P)H signaling pathways in biomedical research contexts, offering insights into cellular metabolism, stress responses, and disease mechanisms.

Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior.

Biomolecular condensates are essential in various cellular processes, and their mis-regulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Mixed-Layered Lead Halide Frameworks with High Stability and Efficient Room-Temperature Phosphorescence.

Room-temperature phosphorescent (RTP) materials play a crucial role in optical anticounterfeiting science and information security technologies. Ionically bonded organic metal halides have emerged as promising RTP material systems due to their excellent self-assembly and unique photophysical property, but their intrinsic instability largely hinders their advanced practical applications. Herein, we employ a coordination-driven synthetic strategy utilizing organocarboxylates for the synthesis of two isostructural layered lead halide frameworks. The frameworks adopt a new mixed-layered topology, consisting of alternating [Pb10X9]11+ (X = Cl-/Br-) layers and [Pb6XO3]11+ (X = Cl-/Br-) layers that are coordinatively sandwiched by organocarboxylate layers. The frameworks exhibit long-lived green afterglow emission with the long lifetime of up to 45.89 ms and the photoluminescence quantum yield (PLQY) of up to 43.13%. The Pb2+-carboxylate coordination accelerates the metal-to-ligand charge transfer from the light-harvesting lead halide layers to the phosphorescent organic component, promoting efficient spin-orbit coupling and intersystem crossing. Moreover, the coordination networks exhibit good structural robustness under ambient conditions for at least 12 months, as well as stability in boiling water, acidic and basic aqueous environments. The highly efficient afterglow and high structural integrity enable multiple anticounterfeiting applications across diverse chemical environments.

Unraveling the Structure-Spectrum Relationship of Yeast Phenylalanine Transfer RNA: Insights from Theoretical Modeling of Infrared Spectroscopy.

Yeast phenylalanine tRNA (tRNAphe) is a paradigmatic model in structural biology. In this work, we combine molecular dynamics simulations and spectroscopy modeling to establish a direct link between its structure, conformational dynamics, and infrared (IR) spectra. Employing recently developed vibrational frequency maps and coupling models, we apply a mixed quantum/classical treatment of the line shape theory to simulate the IR spectra of tRNAphe in the 1600-1800 cm-1 region across its folded and unfolded conformations and under varying concentrations of Mg2+ ions. The predicted IR spectra of folded and unfolded tRNAphe are in good agreement with experimental measurements, validating our theoretical framework. We then elucidate how the characteristic L-shaped tertiary structure of the tRNA and its modulation in response to diverse chemical environments give rise to distinct IR absorption peaks and line shapes. These calculations effectively bridge IR spectroscopy experiments and atomistic molecular simulations, unraveling the molecular origins of the observed IR spectra of tRNAphe. This work presents a robust theoretical protocol for modeling the IR spectroscopy of nucleic acids, which will facilitate its application as a sensitive probe for detecting the fluctuating secondary and tertiary structures of these essential biological macromolecules.

Automated Chemical Reaction Network Generation and Its Application to Exoplanet Atmospheres

With the advent of JWST and the spectroscopic characterization of exoplanet atmospheres in unprecedented detail, there is a demand for more complete pictures of chemical and photochemical reactions and their impacts on atmospheric composition. Traditionally, building reaction networks for (exo)planetary atmospheres involves manually tracking relevant species and reactions, a time-consuming and error-prone process. This approach’s applicability is also often limited to specific conditions, making it less versatile for different planetary types (i.e., photochemical networks for Jupiters may not be directly applicable to water-rich exoplanets). We introduce an automated approach using a computer-aided chemical reaction network generator, combined with a 1D photochemical kinetic-transport model, offering significant advantages. This approach automatically selects reaction rates through a rate-based iterative algorithm and multiple refinement steps, enhancing model reliability. Also, this approach allows for the efficient simulation of diverse chemical environments, from hydrogen to water, carbon dioxide, and nitrogen-dominated atmospheres. Using WASP-39b and WASP-80b as examples, we demonstrate our approach’s effectiveness, showing good agreement with recent JWST data. Our WASP-39b model aligns with prior studies and JWST observations, capturing photochemically produced sulfur dioxide. The WASP-80b model reveals an atmosphere influenced by deep-interior thermochemistry and vertical mixing, consistent with JWST NIRCam observations. Furthermore, our model identifies a novel initial step for the N2–NH3–HCN pathway that enhances the efficiency of the conversion in high-temperature/high-pressure environments. This automated chemical network generation offers a novel, efficient, and precise framework for studying exoplanetary atmospheres, marking a significant advancement over traditional modeling techniques.

Theoretical study on the effect of oxidation states of phosphorus flame retardants on their mode of action

Polymers are ubiquitous and find applications in various facets of daily life and industrial production. However, the fire incidents induced by polymers jeopardize the safety of human life and property. Improving the fire resistance of polymeric materials through halogen-free flame retardants has become a prominent direction guided by the regulatory framework. In this paper, the decomposition pathways and mechanisms of five phosphorus-containing flame retardants, PPh3, DPPO, PDPP, TPPO and TPP, were investigated. The reaction pathways primarily encompassed the generation of PO, PO2, PO3, PO4, phenyl, and phenoxy radicals. Through a comprehensive analysis of the dissociation energies of P-C, P-O, and CO bonds under diverse chemical environments, phenoxy radicals was the principal decomposition products, with the cleavage of P-O bonds being the predominant choice. Increasing phosphorus oxidation states favors the accumulation of a carbon layer, tending to retard flame propagation through condensed-phase mechanisms. Phosphorus oxidation states primarily facilitated secondary dissociation reactions. The presence of oxygen favored the cleavage of P-C bonds, offering diverse flame retardant mechanisms that concurrently mitigated flame spread. This work provided an in-depth exploration of the flame retardant mechanisms of phosphorus-containing agents and summarized the impact of phosphorus oxidation states on flame retardants.

Modelling ligand exchange in metal complexes with machine learning potentials.

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.

Characteristics of cell motility during cell collision

Quantitative examination of cellular motion and intercellullar interactions possesses substantial relevance for both biology and medicine. However, the effects of intercellular interactions during cellular locomotion remain under-explored in experimental research. As such, this study seeks to bridge this research gap, adopting Dictyostelium discoideum (Dicty) cells as a paradigm to investigate variations in cellular motion during reciprocal collisions. We aim to attain a comprehensive understanding of how cell interactions influence cell motion. By observing and processing the motion trajectories of colliding cells under diverse chemical environments, we calculated the diffusion coefficient (D) and the persistence time (τ), using mean square displacement. Our analysis of the relationship dynamics between D and τ prior to the collisions reveals intricate and non-monotonic alterations in cell movements during collisions. By quantitatively scrutinizing the τ trend, we were able to categorize the cellular responses to interactions under different conditions. Importantly, we ascertained that the effect of cell interactions during collisions in Dicty cells emulates a classical sigmoid function. This discovery suggests that cellular responses might comply with a pattern akin to the Weber–Fechner law.

Virus-Based Pyroelectricity.

The first observation of heat-induced electrical potential generation on a virus and its detection through pyroelectricity are presented. Specifically, the authors investigate the pyroelectric properties of the M13 phage, which possesses inherent dipole structures derived from the noncentrosymmetric arrangement of the major coat protein (pVIII) with an α-helical conformation. Unidirectional polarization of the phage is achieved through genetic engineering of the tail protein (pIII) and template-assisted self-assembly techniques. By modifying the pVIII proteins with varying numbers of glutamate residues, the structure-dependent tunable pyroelectric properties of the phage are explored. The most polarized phage exhibits a pyroelectric coefficient of 0.13 µC m-2 °C-1 . Computational modeling and circular dichroism (CD) spectroscopy analysis confirm that the unfolding of α-helices within the pVIII proteins leads to changes in phage polarization upon heating. Moreover, the phage is genetically modified to enable its pyroelectric function in diverse chemical environments. This phage-based approach not only provides valuable insights into bio-pyroelectricity but also opens up new opportunities for the detection of various viral particles. Furthermore, it holds great potential for the development of novel biomaterials for future applications in biosensors and bioelectric materials.

Fluorescence Decay Analysis of the Model Compounds as an Approach to Photophysical Engineering of Fluorescent Proteins

Studying of structure-function relationships between a chromophore and its protein environment plays a key role in photophysical engineering of fluorescent proteins (FPs), specifically, in the guided designing of their new variants with a higher fluorescence quantum yield (FQY). Known approaches to FQY increasing mostly rely on suppression of the excited state nonradiative processes, but no tools have been suggested for the tuning of the radiative rate constant (kr), which is also a potentially “adjustable” value. Here, we propose an experimental approach in which the synthetic chromophore of FP models the “fixation” of the most important radiationless constants and allows monitoring of the fluorescence lifetime flexibility (as an indicator of the kr value). As a proof-of-concept, we studied the time-resolved fluorescence behavior of the green and blue FP chromophore analogs in diverse chemical environments. The conformationally locked analog of the GFP chromophore in most cases showed monophasic fluorescence decay kinetics with a lifetime of 2.7–3.0 ns, thus adequately modeling the typical behavior of GFPs with the highest FQYs. Under the conditions of stimulated ionization of this chromophore, we observed increased (up to 4.3–4.6 ns) fluorescence lifetimes, which can be interpreted in terms of an increase in the radiative constant (kr). The conformationally locked analog of the Sirius chromophore showed biexponential fluorescence decay kinetics, partly simulating the properties of the blue FPs. In an acetic acid solution, this compound exhibited distinct fluorescent properties (elevated fluorescence intensity with a major lifetime population of ~4 ns), which can be interpreted as the emission of an unusual cationic form of the chromophore.

Superstructure-dependent stability of DNA origami nanostructures in the presence of chaotropic denaturants.

The structural stability of DNA origami nanostructures in various chemical environments is an important factor in numerous applications, ranging from biomedicine and biophysics to analytical chemistry and materials synthesis. In this work, the stability of six different 2D and 3D DNA origami nanostructures is assessed in the presence of three different chaotropic salts, i.e., guanidinium sulfate (Gdm2SO4), guanidinium chloride (GdmCl), and tetrapropylammonium chloride (TPACl), which are widely employed denaturants. Using atomic force microscopy (AFM) to quantify nanostructural integrity, Gdm2SO4 is found to be the weakest and TPACl the strongest DNA origami denaturant, respectively. Despite different mechanisms of actions of the selected salts, DNA origami stability in each environment is observed to depend on DNA origami superstructure. This is especially pronounced for 3D DNA origami nanostructures, where mechanically more flexible designs show higher stability in both GdmCl and TPACl than more rigid ones. This is particularly remarkable as this general dependence has previously been observed under Mg2+-free conditions and may provide the possibility to optimize DNA origami design toward maximum stability in diverse chemical environments. Finally, it is demonstrated that melting temperature measurements may overestimate the stability of certain DNA origami nanostructures in certain chemical environments, so that such investigations should always be complemented by microscopic assessments of nanostructure integrity.

Supramolecular Activation of S8 by Cucurbiturils in Water and Mechanism of Reduction to H2S by Thiols: Insights into Biological Sulfane Sulfur Trafficking.

Reactive sulfur species (RSS) play critical roles in diverse chemical environments. Molecules containing sulfane sulfur (S0) have emerged as key species involved in cellular redox buffering as well as RSS generation, translocation, and action. Using cucurbit[7]uril (CB[7]) as a model hydrophobic host, we demonstrate here that S8 can be encapsulated to form a 1:1 host guest complex, which was confirmed by solution state experiments, mass spectrometry, and X-ray crystallography. The solid state structure of CB[7]/S8 shows that the encapsulated S8 is available to nucleophiles through the carbonyl portals of the host. Treatment of CB[7]/S8 with thiols results in efficient reduction of S8 to H2S in water at physiological pH. We establish that encapsulated S8 is attacked by a thiol within the CB[7] host and that the resultant soluble hydropolysulfide is ejected into solution, where it reacts further with thiols to generate soluble sulfane sulfur carriers and ultimately H2S. The formation of these intermediate is supported by observed kinetic saturation behavior, competitive inhibition experiments, and alkylative trapping experiments. We also demonstrate that CB[7]/S8 can be used to increase sulfane sulfur levels in live cells using fluorescence microscopy. More broadly, this work suggests a general activation mechanism of S8 by hydrophobic motifs, which may be applicable to proteins, membranes, or other bimolecular compartments that could transiently bind and solubilize S8 to promote reaction with thiols to solubilize and shuttle S8 back into the redox labile sulfane sulfur pool. Such a mechanism would provide an attractive manifold in which to understand the RSS translocation and trafficking.

Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite

The human gut microbiota resides within a diverse chemical environment challenging our ability to understand the forces shaping this ecosystem. Here, we reveal that fitness of the Bacteroidales, the dominant order of bacteria in the human gut, is an emergent property of glycans and one specific metabolite, butyrate. Distinct sugars serve as strain-variable fitness switches activating context-dependent inhibitory functions of butyrate. Differential fitness effects of butyrate within the Bacteroides are mediated by species-level variation in Acyl-CoA thioesterase activity and nucleotide polymorphisms regulating an Acyl-CoA transferase. Using invivo multi-omic profiles, we demonstrate Bacteroides fitness in the human gut is associated together, but not independently, with Acyl-CoA transferase expression and butyrate. Our data reveal that each strain of the Bacteroides exists within a unique fitness landscape based on the interaction of chemical components unpredictable by the effect of each part alone mediated by flexibility in the core genome.

Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials

The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. To overcome this hurdle, methods are being proposed that automatically generate training data. Herein, we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout these examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications.

Accurate Atom-Dipole Interaction Model for Prediction of Electro-optical Properties: From van der Waals Aggregates to Covalently Bonded Clusters.

This work aims at the accurate estimation of the electro-optical properties of atoms and functional groups in organic crystals. A better understanding of the nature of building blocks and the way they interact with each other enables more efficient prediction of self-assembly, and thus physical properties in condensed matter. We propose a modified version of an atom-dipole interaction model that is based on atomic dipole moments calculated from the quantum theory of atoms in molecules. The method is very reliable for the prediction of various optical and electric properties in diverse chemical environments, ranging from hydrocarbon molecules bonded by dispersive interactions to polar rings connected by hydrogen bonds, or even polymeric structures whose monomers are covalently linked. Distributed polarizabilities and electrostatic potentials are compared to those obtained using a complete quantum-mechanical approach on finite-size aggregates. Our electrostatic approximation recovers isotropic polarizabilities with an accuracy of ca. 5 au and electrostatic potentials of ca. 0.05 au, even in the worst-case scenario in which polarization and charge-transfer effects are large. Functional groups are highly exportable, estimating the properties of small peptides and polyaromatics with a maximum deviation as low as ca. 15%.

Solution-Processed Stretchable Ag2 S Semiconductor Thin Films for Wearable Self-Powered Nonvolatile Memory.

Compared with the large plastic deformation observed in ductile metals and organic materials, inorganic semiconductors have limited plasticity (<0.2%) due to their intrinsic bonding characters, restricting their widespread applications in stretchable electronics. Herein, the solution-processed synthesis of ductile α-Ag2 S thin films and fabrication of all-inorganic, self-powered, and stretchable memory devices, is reported. Molecular Ag2 S complex solution is synthesized by chemical reduction of Ag2 S powder, fabricating wafer-scale highly crystalline Ag2 S thin films. The thin films show stretchability due to the intrinsic ductility, sustaining the structural integrity at a tensile strain of 14.9%. Moreover, the fabricated Ag2 S-based resistive random access memory presents outstanding bipolar switching characteristics (Ion /Ioff ratio of ≈105 , operational endurance of 100 cycles, and retention time >106 s) as well as excellent mechanical stretchability (no degradation of properties up to stretchability of 52%). Meanwhile, the device is highly durable under diverse chemical environments and temperatures from -196 to 300 °C, especially maintaining the properties for 168 h in 85% relative humidity and 85 °C. A self-powered memory combined with motion sensors for use as a wearable healthcare monitoring system is demonstrated, offering the potential for designing high-performance wearable electronics that are usable in daily life in a real-world setting.

Ubiquitous selection for mecA in community-associated MRSA across diverse chemical environments

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is threatening public health as it spreads worldwide across diverse environments. Its genetic hallmark, the mecA gene, confers resistance to many β-lactam antibiotics. Here, we show that, in addition, mecA provides a broad selective advantage across diverse chemical environments. Competing fluorescently labelled wild-type and mecA-deleted CA-MRSA USA400 strains across ~57,000 compounds supplemented with subinhibitory levels of the β-lactam drug cefoxitin, we find that mecA provides a widespread advantage across β-lactam and non β-lactam antibiotics, non-antibiotic drugs and even diverse natural and synthetic compounds. This advantage depends on the presence of cefoxitin and is strongly associated with the compounds’ physicochemical properties, suggesting that it may be mediated by differential compounds permeability into the cell. Indeed, mecA protects the bacteria against increased cell-envelope permeability under subinhibitory cefoxitin treatment. Our findings suggest that CA-MRSA success might be driven by a cell-envelope mediated selective advantage across diverse chemical compounds.

Modeling of metal nanoparticles: Development of neural-network interatomic potential inspired by features of the modified embedded-atom method

Interatomic potential plays a key role in ensuring the accuracy and reliability of molecular-dynamics simulation results. While most empirical potentials are benchmarked against a set of carefully chosen bulk material properties, recent advances in machine learning have seen the emergence of neural-network-based mathematical potentials capable of describing highly complex potential energy surfaces for a variety of systems. We report here the development of a neural-network interatomic potential (NNIP) with modified embedded-atom method background density as fingerprint functions, which could accurately model the energetics of metallic nanoparticles and clusters (Cu as a representative example) widely used in catalysis. To appropriately account for the diverse chemical environments encountered in nanoparticles/nanoclusters, an extensive set of atomic configurations (totaling 18 084) were calculated using density-functional-theory (DFT) at the Perdew-Burke-Ernzerhof level. In addition to standard bulk properties such as cohesive energies and elastic constants, the sampled configurations also include a substantial number of differently oriented crystal facets and differently sized nanoparticles and nanoclusters, greatly expanding the value range of NNIP features that was otherwise quite limited. The complex energy potential surface of Cu can be faithfully reproduced, with an average error of 0.011 eV/at for energy states within 3 eV of the ground state. As an illustration, the developed NNIP is used to simulate the molecular dynamics of copper nanoparticles, and good agreement is achieved between DFT and the NNIP.