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Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity
The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (K OW ) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc) 0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.
Calibrating a Peridynamic Material Model with Molecular Dynamics.
Abstract not provided.
Structure of the Polymer Backbones in polyMOF Materials
The molecular connectivity of polymer–metal–organic framework (polyMOF) hybrid materials was investigated using density functional theory calculations and solid-state NMR spectroscopy. The architectural constraints that dictate the formation of polyMOFs were assessed by examining poly(1,4-benzenedicarboxylic acid) (pbdc) polymers in two archetypical MOF lattices (UiO-66 and IRMOF-1). Modeling of the polyMOFs showed that in the IRMOF-1-type lattice, six, seven, and eight methylene (–CH 2 –) groups between 1,4-benzenedicarboxylate (terephthalate, bdc 2– ) units can be accommodated without significant distortions, while in the UiO-66-type lattice, an optimal spacing of seven methylene groups between bdc 2– units is needed to minimize strain. Solid-state NMR supports these predictions and reveals pronounced spectral differences for the same polymer in the two polyMOF lattices. With seven methylene groups, polyUiO-66-7a shows 7 ± 3% of uncoordinated terephthalate linkers, while these are undetectable (<4%) in the corresponding polyIRMOF-1-7a. Additionally, NMR-detected backbone mobility is significantly higher in the polyIRMOF-1-7a than in the corresponding polyUiO-66-7a, again indicative of taut chains in the latter.
Full-film dry transfer of MBE-grown van der Waals materials
Abstract Molecular beam epitaxy (MBE) has been used to create high-quality, large-scale two-dimensional van der Waals (2D vdW) materials. However, due to the strong adhesion between the substrate and deposited materials, the peel-off and dry transfer of MBE-grown vdW films onto other substrates has been challenging. This limits the study and use of MBE films for heterogeneous integration including stacked and twisted heterostructures. In this work, we develop a polymer-assisted dry transfer method and successfully perform full-film transfer of various MBE-grown 2D vdW materials including transition metal dichalcogenides, topological insulators and 2D magnets. In particular, we transfer air-sensitive 2D magnets, characterize their magnetic properties, and compare them with as-grown materials. The results show that the transfer technique does not degrade the magnetic properties, with the Curie temperature and hysteresis loops exhibiting similar behaviors after the transfer. Our results enable further development of heterogeneous integration of 2D vdW materials based on MBE growth.
Workhorse minimally empirical dispersion-corrected density functional with tests for weakly bound systems: r 2 SCAN + rVV 10
SCAN+rVV10 has been demonstrated to be a versatile van der Waals (vdW) density functional that delivers good predictions of both energetic and structural properties for many types of bonding. Recently, the r 2 SCAN functional was devised as a revised form of SCAN with improved numerical stability. In this work, we refit the rVV10 functional to optimize the r 2 SCAN+rVV10 vdW density functional and test its performance for molecular interactions and layered materials. Our molecular tests demonstrate that r 2 SCAN+rVV10 outperforms its predecessor SCAN+rVV10 in both efficiency (numerical stability) and accuracy. This good performance is also found in lattice-constant predictions. In comparison with benchmark results from higher-level theories or experiments, r 2 SCAN+rVV10 yields excellent interlayer binding energies and phonon dispersions for layered materials.
Quantum mechanics based non-bonded force field functions for use in molecular dynamics simulations of materials and systems: The nitrogen and oxygen columns
Accurate Force Fields (FFs) are essential for Molecular Dynamics (MD) simulations of the dynamics of realistic materials in terms of atomic-level interactions. The FF parameters of short-range valence interactions can be derived through Quantum Mechanical (QM) calculations on model systems practical for QM (<300 atoms). Similarly, the dynamic electrostatic interactions can be described with methods such as QEq or PQEq that allow charges and polarization to adjust dynamically. However, accurately extracting long-range van der Waals (vdW) interactions from QM calculations poses challenges due to the absence of a definitive method to distinguish between the different energetic components of electrostatics, polarization, vdW, hydrogen bonding, and valence interactions. To do this we use the Perdew–Burke–Ernzerhof flavor of Density Functional Theory, including empirical D3 vdW corrections, to predict the Equation of State for each element (keeping any covalent bonds fixed), from which we obtain the two-body vdW nonbond potential. Here, we extend these calculations to include non-bonded parameters for the N and O columns of the periodic table so that we now describe columns 15 (N), 16 (O), 17 (F), and 18 (Ne) of the periodic table. For these 20 elements, we find that the two-body vdW potentials can all be mapped to a single universal two-body curve, with just three scaling parameters: Re, De, and L. We refer to this as the Universal NonBond (UNB) potential. We expect this to be useful for new MD simulations and a helpful starting point to obtain UNB parameters for the remainder of the periodic table.
Molecular orbital symmetry-driven trimer formation in Kagome correlated electron materials
Correlated electron materials with molecular orbital states extending over transition metal clusters can host multiferroicity, spin frustration, and unconventional insulating phases. However, the fundamental criteria that govern cluster formation and stability remain unclear. Here, we identify a symmetry, correlation, and electron-filling-driven criteria that stabilize triangular metal trimers in materials displaying transition metal Kagome patterns. Using density functional theory and chemical bonding analysis, we show that trimer formation emerges when 6–8 electrons occupy molecular orbitals derived from transition metal d-states, achieving near-complete filling of bonding states while avoiding antibonding occupation, and correlations are of intermediate strength. This principle explains the stability of Nb 3 X 8 (X = Cl, Br, I), and more broadly, our findings offer a general design rule to obtain quantum materials with quantum states extended across transition metal clusters.
Single photon emitters in van der Waals solids for quantum photonics: materials, theory and molecular-scale characterization probes
Strong light–matter interactions in two-dimensional layered materials (2D materials) have attracted the interest of researchers from interdisciplinary fields for more than a decade now. A unique phenomenon in some 2D materials is their large exciton binding energies (BEs), increasing the likelihood of exciton survival at room temperature. It is this large BE that mediates the intense light–matter interactions of many of the 2D materials, particularly in their monolayer limit, where the interplay of excitonic phenomena poses a wealth of opportunities for high-performance optoelectronics and quantum photonics. Within quantum photonics, quantum information science (QIS) is growing rapidly, where photons are a promising platform for information processing due to their low-noise properties, excellent modal control, and long-distance propagation. A central element for QIS applications is a single photon emitter (SPE) source, where an ideal on-demand SPE emits exactly one photon at a time into a given spatiotemporal mode. Recently, 2D materials have shown practical appeal for QIS which is directly driven from their unique layered crystalline structure. This structural attribute of 2D materials facilitates their integration with optical elements more easily than the SPEs in conventional three-dimensional solid state materials, such as diamond and SiC. In this review article, we will discuss recent advances made with 2D materials towards their use as quantum emitters, where the SPE emission properties maybe modulated deterministically. Here, the use of unique scanning tunneling microscopy tools for the in-situ generation and characterization of defects is presented, along with theoretical first-principles frameworks and machine learning approaches to model the structure-property relationship of exciton–defect interactions within the lattice towards SPEs. Given the rapid progress made in this area, the SPEs in 2D materials are emerging as promising sources of nonclassical light emitters, well-poised to advance quantum photonics in the future.
Storing molecule within porous materials with a surface molecular barrier layer
In some aspects, the present disclosure provides compositions comprising a nanoporous material such as a metal organic framework and an amine containing compound. In some aspects, these compositions may be used to improve the affinity of a guest molecule to the nanoporous material relative a nanoporous material which had not been treated with the amine containing compound.
A database of molecular properties integrated in the Materials Project
A new infrastructure for computed molecular properties, including a web app and API, is incorporated into the Materials Project, enabling the creation of a database currently containing over 170 000 molecules.
From Coherence to Function: Exploring the Connection in Chemical Systems
The role of quantum mechanical coherences or coherent superposition states in excited state processes has received considerable attention in the last two decades largely due to advancements in ultrafast laser spectroscopy. These coherence effects hold promise for enhancing the efficiency and robustness of functionally relevant processes, even when confronted with energy disorder and environmental fluctuations. Understanding coherence deeply drives us to unravel mechanisms and dynamics controlled by order and synchronization at a quantum mechanical level, envisioning optical control of coherence to enhance functions or create new ones in molecular and material systems. In this frontier, the interplay between electronic and vibrational dynamics, specifically the influence of vibrations in directing electronic dynamics, has emerged as the leading principle. Here, two energetically disparate quantum degrees of freedom work in-sync to dictate the trajectory of an excited state reaction. Moreover, with the vibrational degree being directly related to the structural composition of molecular or material systems, new molecular designs could be inspired by tailoring certain structural elements. In the realm of chemical kinetics, our understanding of the dynamics of chemical transformations is underpinned by fundamental theories, such as transition state theory, activated rate theory, and Marcus theory. These theories elucidate reaction rates by considering the energy barriers that must be overcome for reactants to transform into products. Those barriers are surmounted by the stochastic nature of energy gap fluctuations within reacting systems, emphasizing that the reaction coordinate, the pathway from reactants to products, is not rigidly defined by a specific vibrational motion but encompasses a diverse array of molecular motions. While less is known about the involvement of specific intramolecular vibrational modes, their significance in certain cases cannot be overlooked. In this Account, we summarize key experimental findings that offer deeper insights into the complex electronic–vibrational trajectories encompassing excited states afforded from state-of-the-art ultrafast laser spectroscopy in three exemplary processes: photoinduced electron transfer, singlet–triplet intersystem crossing, and intramolecular vibrational energy flow in molecular systems. We delve into the rapid decoherence, or loss of phase and amplitude correlations, of vibrational coherences along promoter vibrations during subpicosecond intersystem crossing dynamics in a series of binuclear platinum complexes. This rapid decoherence illustrates the vibration-driven reactive pathways from the Franck–Condon state to the curve crossing region. We also explore the generation of new vibrational coherences induced by impulsive reaction dynamics rather than by the laser pulse in these systems, which sheds light on specific energy dissipation pathways and thereby on the progression of the reaction trajectory in the vicinity of the curve crossing on the product side. Another property of vibrational coherences, amplitude, reveals how energy can flow from one vibration to another in the electronic excited state of a terpyridine–molybdenum complex hosting a nonreactive dinitrogen substrate. In conclusion, a slight change in vibrational energy triggers a quasi-resonant interaction, leading to constructive wavepacket interference and ultimately intramolecular vibrational redistribution from a Franck–Condon active terpyridine vibration to a dinitrogen stretching vibration, energizing the dinitrogen bond.
Characterizing Electron Flow through Catechol‐Graphene Composite Hydrogels
Abstract Electronic materials that allow the controlled flow of electrons in aqueous media are required for emerging applications that require biocompatibility, safety, and/or sustainability. Here, a composite hydrogel film composed of graphene and catechol is electrofabricated, and that this composite offers synergistic properties is reported. Graphene confers metal‐like conductivity and enables charge‐storage through an electrical double layer mechanism. Catechol confers redox‐activity and enables charge‐storage through a redox mechanism. Importantly, there are two functional populations of catechols: conducting‐catechols (presumably in intimate contact with graphene) allow direct electron‐transfer; and non‐conducting‐catechols (presumably physically separated from graphene) require diffusible mediators to enable electron‐transfer. Using a variety of spectroelectrochemical measurements, that the capacity of the composite for charge‐storage increases in proportion to the extent by which the catechol‐groups can undergo redox‐state switching is demonstrated. To illustrate the broad relevance of this work, how the redox‐state switching can be related to both the charge storage of energy materials and the memory of molecular electronic materials is discussed. The authors believe this work is significant because it demonstrates that: conducting and redox‐active components enable distinctly different mechanisms for charge‐storage and electron‐transfer; these components act synergistically; and mediators provide unique opportunities to extend the capabilities of electronic materials.
Covalent Triazine-Based Frameworks with Ru-tda Based Catalyst Anchored via Coordination Bond for Photoinduced Water Oxidation
Light-induced water splitting (hν-WS) for the production of hydrogen as a solar fuel is considered a promising sustainable strategy for the replacement of fossil fuels. An efficient system for hν-WS involves a photoactive material that, upon shining light, is capable of separating and transferring charges to catalysts for the hydrogen and oxygen evolution processes. Covalent triazine-based frameworks (CTFs) represent an interesting class of 2D organic light-absorbing materials that have recently emerged thanks to their tunable structural, optical and morphological properties. Typically, catalysts (Cat) are metallic nanoparticles generated in situ after photoelectroreduction of metal precursors or directly drop-casted on top of the CTF material to generate Cat-CTF assemblies. Here, in this work, the synthesis, characterization and photocatalytic performance of a novel hybrid material, Ru-CTF, is reported, based on a CTF structure featuring dangling pyridyl groups that allow the Ru-tda (tda is [2,2':6',2'“-terpyridine]-6,6'”-dicarboxylic acid) water oxidation catalyst (WOC) unit to coordinate via covalent bond. The Ru-CTF molecular hybrid material can carry out the light-induced water oxidation reaction efficiently at neutral pH, reaching values of maximum TOF of 17 h -1 and TONs in the range of 220 using sodium persulfate as a sacrificial electron acceptor.
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery
Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
Molecular catalyst coordinatively bonded to organic semiconductors for selective light-driven CO 2 reduction in water
The selective photoreduction of CO 2 in aqueous media based on earth-abundant elements only, is today a challenging topic. Here we present the anchoring of discrete molecular catalysts on organic polymeric semiconductors via covalent bonding, generating molecular hybrid materials with well-defined active sites for CO 2 photoreduction, exclusively to CO in purely aqueous media. The molecular catalysts are based on aryl substituted Co phthalocyanines that can be coordinated by dangling pyridyl attached to a polymeric covalent triazine framework that acts as a light absorber. This generates a molecular hybrid material that efficiently and selectively achieves the photoreduction of CO 2 to CO in KHCO 3 aqueous buffer, giving high yields in the range of 22 mmol g -1 (458 μmol g -1 h -1 ) and turnover numbers above 550 in 48 h, with no deactivation and no detectable H 2 . The electron transfer mechanism for the activation of the catalyst is proposed based on the combined results from time-resolved fluorescence spectroscopy, in situ spectroscopies and quantum chemical calculations.
Understanding Fragmentation of Organic Small Molecules in Atom Probe Tomography
In atom probe tomography of molecular organic materials, field ionization of either entire molecules or molecular fragments can occur, but the mechanism governing this behavior was not previously understood. This work explains when a doubly ionized small molecule organic material is expected to undergo fragmentation. Here, we find that multiple detection events arising from post-ionization fragmentation of a parent molecular dication into two daughter ions is well explained by the free energy and geometries of the molecules computed using density functional theory. Of the systems studied, exergonic free energies for formation of the daughter ions, smaller activation energies for dissociation, and increases in bond length are all found to be quantitative predictors for ion fragmentation. This work expands the applicability of atom probe tomography to organic materials by increasing the fundamental understanding of processes occurring during this analysis technique.
HydraGNN_GFM_FineTuning4Materials v1.0
This repository enables fine-tuning of the HydraGNN Predictive GFM 2026 — an open-source ensemble of pre-trained graph foundation models for atomistic materials modeling, developed at Oak Ridge National Laboratory. The GFM 2026 is freely available and downloadable via Globus from the OLCF Data Constellation (DOI: 10.13139/OLCF/2562660). Starting from these pre-trained weights, this repository provides a complete transfer learning pipeline for adapting the GFM ensemble to domain-specific molecular and materials property prediction tasks. It includes: 1) Utilities for ensemble fine-tuning with task-specific output heads 2) Example pipelines for eight widely-used materials and molecular datasets 3) Tools for model adaptation and head configuration 4) Data preprocessing utilities for each supported dataset 5) Benchmarking and evaluation scripts