Hydrogen adsorption in metal-organic frameworks: the role of nuclear quantum effects

Vibrational spectroscopy of Cu+(H2)4: about anharmonicity and fluxionality

The vibrational spectra of the copper(I) cation-dihydrogen complexes Cu⁺(H₂)₄, Cu⁺(D₂)₄ and Cu⁺(D₂)₃H₂ are studied using cryogenic ion trap vibrational spectroscopy in combination with quantum chemical calculations. The infrared photodissociation (IRPD) spectra (2500-7300 cm^-1) are assigned based on a comparison to IR spectra calculated using vibrational second-order perturbation theory (VPT2). The IRPD spectra exhibit ≈60 cm^-1 broad bands that lack rotational resolution, indicative of rather floppy complexes even at an ion trap temperature of 10 K. The observed vibrational features are assigned to the excitations of dihydrogen stretching fundamentals, combination bands of these fundamentals with low energy excitations as well as overtone excitations of a minimum-energy structure with C_s symmetry. The three distinct dihydrogen positions present in the structure can interconvert via pseudorotations with energy barriers less than 10 cm^-1, far below the zero-point vibrational energy. Ab initio Born-Oppenheimer molecular dynamics (BOMD) simulations confirm the fluxional behavior of these complexes and yield an upper limit for the timeframe of the pseudorotation on the order of 10 ps. For Cu⁺(D₂)₃H₂, the H₂ and D₂ loss channels yield different IRPD spectra indicating non-ergodic behavior.

Learning the Quantum Centroid Force Correction in Molecular Systems: A Localized Approach

Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems.

Isotope-selective pore opening in a flexible metal-organic framework

Flexible metal-organic frameworks that show reversible guest-induced phase transitions between closed and open pore phases have enormous potential for highly selective, energy-efficient gas separations. Here, we present the gate-opening process of DUT-8(Ni) that selectively responds to D₂, whereas no response is observed for H₂ and HD. In situ neutron diffraction directly reveals this pressure-dependent phase transition. Low-temperature thermal desorption spectroscopy measurements indicate an outstanding D₂-over-H₂ selectivity of 11.6 at 23.3 K, with high D₂ uptake. First-principles calculations coupled with statistical thermodynamics predict the isotope-selective gate opening, rationalized by pronounced nuclear quantum effects. Simulations suggest DUT-8(Ni) to remain closed in the presence of HT, while it also opens for DT and T₂, demonstrating gate opening as a highly effective approach for isotopolog separation.

Predicting hydrogen storage in MOFs via machine learning

The H2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (>5,300 m2 g-1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g-1). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

Quantum driven proton diffusion in brucite-like minerals under high pressure

Transport of hydrogen in hydrous minerals under high pressure is a key step for the water cycle within the Earth interior. Brucite Mg(OH)2 is one of the simplest minerals containing hydroxyl groups and is believed to decompose under the geological condition of the deep Earth's mantle. In the present study, we investigate the proton diffusion in brucite under high pressure, which results from a complex interplay between two processes: the O-H reorientations motion around the c axis and O-H covalent bond dissociations. First-principle path-integral molecular dynamics simulations reveal that the increasing pressure tends to lock the former motion, while, in contrast, it activates the latter which is mainly triggered by nuclear quantum effects. These two competing effects therefore give rise to a pressure sweet spot for proton diffusion within the mineral. In brucite Mg(OH)2, proton diffusion reaches a maximum for pressures close to 70GPa, while the structurally similar portlandite Ca(OH)2 never shows proton diffusion within the pressure range and time scale that we explored. We analyze the different behavior of brucite and portlandite, which might constitute two prototypes for other minerals with same structure.

Rational Design of a Low-Cost, High-Performance Metal-Organic Framework for Hydrogen Storage and Carbon Capture

We present the in silico design of a MOF-74 analogue, hereon known as M2(DHFUMA) [M = Mg, Fe, Co, Ni, Zn], with enhanced small-molecule adsorption properties over the original M2(DOBDC) series. Constructed from 2,3-dihydroxyfumarate (DHFUMA), an aliphatic ligand which is smaller than the aromatic 2,5-dioxidobenzene-1,4-dicarboxylate (DOBDC), the M2(DHFUMA) framework has a reduced channel diameter, resulting in higher volumetric density of open metal sites and significantly improved volumetric hydrogen (H2) storage potential. Furthermore, the reduced distance between two adjacent open metal sites in the pore channel leads to a CO2 binding mode of one molecule per two adjacent metals with markedly stronger binding energetics. Through dispersion-corrected density functional theory (DFT) calculations of guest-framework interactions and classical simulation of the adsorption behavior of binary CO2:H2O mixtures, we theoretically predict the M2(DHFUMA) series as an improved alternative for carbon capture over the M2(DOBDC) series when adsorbing from wet flue gas streams. The improved CO2 uptake and humidity tolerance in our simulations is tunable based upon metal selection and adsorption temperature which, combined with the significantly reduced ligand expense, elevates this material's potential for CO2 capture and H2 storage. The dynamical and elastic stabilities of Mg2(DHFUMA) were verified by hybrid DFT calculations, demonstrating its significant potential for experimental synthesis.