Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (MOFs) represent a rich platform for exploring NTE. However, uncovering the structural motifs that govern NTE across the enormous MOF design space remains experimentally challenging, and large-scale first-principles phonon calculations are computationally prohibitive. Here, we comprehensively evaluate the factors influencing NTE in MOFs by utilizing a high-throughput workflow based on MACE-MP-MOF0, a machine learning interatomic potential fine-tuned for MOFs with near-ab initio accuracy, to construct PhononMOFdb, a database of phonons, inelastic neutron scattering spectra, bulk moduli, and heat capacities for over 12,000 MOFs. High-throughput screening of this database reveals that highly porous cubic topology frameworks with heavier, lower-valent metal nodes favor strong NTE, while linker functionalization provides a practical handle for tuning NTE magnitude and sign without compromising mechanical stability. Experimental validation via high-resolution temperature-dependent synchrotron powder X-ray diffraction on the Ce-UiO-66 MOF and its brominated variants confirms the design recipe and yields volumetric NTE coefficients surpassing current records. This work establishes a data-driven strategy for engineering NTE in MOFs, showing how machine learning-accelerated discovery and targeted experimental validation together unlock predictive materials design.
A semiempirical extended tight-binding approach (GFN1-xTB) is employed to compute the electronic properties of a dataset of MOFs, and it is shown that GFN1-xTB approximates MOF band gaps well, as compared to semilocal DFT.
A. Jose, A. Walsh· Journal of Chemical Theory a...· 0 citations
Metal–organic frameworks (MOFs) can exhibit pronounced negative thermal expansion (NTE) through thermal population of distortions that contract the lattice. In conventional framework NTE, these distortions are dynamic, involving transverse vibrations of bridging ligands; however, in Zr-based MOFs, a distinct mechanism for NTE has recently emerged that involves static distortion of Zr6-oxo cluster nodes. Here, we show that MOF-808, a Zr-based MOF with 6-connected Zr6-oxo nodes, exhibits colossal NTE with a volumetric coefficient of thermal expansion (CTE) whose magnitude exceeds 600 × 10–6 K–1, more than six times larger than existing benchmark NTE materials. In situ synchrotron X-ray scattering, combining powder diffraction and pair distribution function (PDF) analyses, shows that this large lattice contraction is coupled to an increasing population of a distorted Zr-node state, with pronounced thermal hysteresis and ramp rate dependencies reflecting frustration of the node distortions within the framework. Quantitative analysis of the relationship between lattice contraction and node distortion shows that the coupling varies and depends on both the temperature and the capping ligand coordinated at the node. We propose that the extreme NTE in MOF-808 reflects an amplified form of the node-distortion NTE mechanism, in which lower, anisotropic node connectivity preferentially orients the elongated node axes toward the pores while aligning the compressed node axis with framework-connected directions, maximizing the lattice contraction.
Jan Hofmann, Ayman Roslend, Jack G. Ajello et al.· Journal of the American Chem...· 0 citations
Metallenes have appealing properties, but stabilizing them in a monolayer phase poses challenges for their synthesis. A recent experiment showed that the van der Waals squeezing method can stabilize certain metallenes in a MoS2 sandwich. This pioneering work motivates systematic studies, but such studies are experimentally impractical, while first-principles modeling remains prohibitive. Here, armed with universal machine-learning interatomic potentials, we constructed 1620 metallene sandwich heterostructures containing 6 different sandwich layers and 45 metals. We performed phonon calculations, which revealed 1208 dynamically stable structures. We found that transition-metal dichalcogenides, particularly MoSe2, are highly effective in stabilizing metallenes. Specifically, buckled hexagonal and honeycomb crystal lattices exhibit the greatest stability. We further evaluated the thermal stability of selected heterostructures with density-functional theory molecular dynamics simulations at room temperature. By uncovering the physical and chemical factors governing the stabilization of metallenes, our results provide systematic insights to guide and accelerate synthesis for future applications.
Accelerating data-driven materials design is critical for advancing sustainable energy, optoelectronics, and catalysis, yet traditional approaches suffer from computational inefficiency, poor generalization, or inadequate capture of complex structure-property relationships, highlighting the urgent need for systematic and effective feature engineering. Herein, we present a series of our recent work to address this challenge. We proposed the LESS classifier, utilizing bond orientational order (BOO) parameters as lightweight features for crystal structure classification, enabling efficient recognition of mono/binary/amorphous and other crystal structures with over 98.8% accuracy and low retraining cost for new phases. Building on the LESS framework, we further developed luMOD, a 24-dimensional universal descriptor integrating convoluted BOO parameters and neighbor type encoding, delivering superior performance in multispecies systems (e.g., perovskites, olivines) with minimal computational overhead. Harnessing the robust encoding capacity of large language models (LLMs), we introduced EvoMD-LLM, which abstracts MD trajectories into macro-symbolic sequences to model species-level reaction dynamics, bridging static linguistic knowledge with dynamic temporal evolution of chemical systems. Additionally, we proposed a transferable bandgap prediction framework for perovskites, integrating ChemGPT-derived atomic embeddings with global attention graph networks to capture organic-inorganic synergies and enable reliable extrapolation to unseen compositions. Collectively, these studies advance feature engineering across static/dynamic, single/multispecies, and equilibrium/reactive systems, unifying interpretability, efficiency, and transferability. They aim to nhance the reliability and scalability of data-driven materials discovery, propelling autonomous functional materials design.
An adaptation of the Δ-ML strategy for quantum property prediction of transition metal complexes is presented, which consistently achieves higher accuracy in the prediction of high-fidelity targets, while demonstrating improved data efficiency and out-of-domain transferability.
Hannes Kneiding, David Balcells· Chemistry· 0 citations
Thermoelectric materials, which enable the direct conversion of waste heat into electricity, offer a sustainable pathway to address energy scarcity and environmental concerns. The efficiency of thermoelectric conversion is characterized by the dimensionless figure of merit, where optimizing electrical transport while suppressing lattice thermal conductivity is crucial. In this study, we investigate the structural, bonding, phonon, and thermoelectric properties of CuGaTe2 using first-principles calculations combined with self-consistent phonon theory and four-phonon scattering analysis. Our findings reveal distinct layered bonding features, consisting of ionic Cu–Te bonds and polar covalent Ga–Te bonds, which create a pronounced bonding hierarchy and induce strong local anharmonicity. Based on Heyd–Scuseria–Ernzerhof hybrid functional band structures and ab initio scattering and transport electronic transport calculations, p-type CuGaTe2 achieves a maximum power factor of 6.65 mWm–1 K–2 and a peak ZT of approximately 1.76 at 900 K, surpassing its n-type counterpart. These results establish CuGaTe2 as a promising p-type thermoelectric material for intermediate- to high-temperature applications and highlight the critical role of four-phonon scattering in accurately predicting thermal transport in strongly anharmonic materials.
Liufu Yuan, Lang Chen, Wenjie Yuan et al.· Journal of Physical Chemistr...· 0 citations