A5009821984- Quantum, superfluid, helium dynamics
- Spectroscopy and Quantum Chemical Studies
- Advanced Chemical Physics Studies
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Advanced NMR Techniques and Applications
- Crystallization and Solubility Studies
- Theoretical and Computational Physics
- Catalytic Processes in Materials Science
- Protein Structure and Dynamics
- Nanopore and Nanochannel Transport Studies
- Phase Equilibria and Thermodynamics
- Computational Drug Discovery Methods
- Thermal and Kinetic Analysis
- nanoparticles nucleation surface interactions
- Chemical Thermodynamics and Molecular Structure
- High-pressure geophysics and materials
- Hydrogen Storage and Materials
- Membrane-based Ion Separation Techniques
- Probabilistic and Robust Engineering Design
- Atomic and Subatomic Physics Research
- Electrostatics and Colloid Interactions
- Advanced Thermodynamics and Statistical Mechanics
- Spectroscopy and Laser Applications
- Mass Spectrometry Techniques and Applications
University of Cambridge
2021-2024
University College London
2024
London Centre for Nanotechnology
2024
Thomas Young Centre
2024
École Polytechnique Fédérale de Lausanne
2016-2022
Collaborative Innovation Center of Chemistry for Energy Materials
2021
Xiamen University
2021
Indian Institute of Technology Kanpur
2015-2016
Quantum sieves for hydrogen isotopes One method improving the efficiency of separation from deuterium (D) is to exploit kinetic quantum sieving with nanoporous solids. This requires ultrafine pore apertures (around 3 angstroms), which usually leads low volumes and D 2 adsorption capacities. Liu et al. used organic synthesis tune size internal cavities cage molecules. A hybrid cocrystal contained both a small-pore that imparted high selectivity larger-pore enabled uptake. Science , this issue p. 613
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) significant computational human effort that must go into development validation potentials for each particular system interest; (ii) a general lack transferability from one chemical to next. Here, using state-of-the-art MACE architecture we introduce single general-purpose ML model,...
Thermal engineering of metal-organic frameworks for adsorption-based applications is very topical in view their industrial potential, particular, since heat management and thermal stability have been identified as important obstacles. Hence, a fundamental understanding the structural chemical features underpinning intrinsic properties highly sought-after. Herein, we investigate nanoscale behavior diverse set using molecular simulation techniques critically compare such conductivity,...
Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature these models, reliability predictions depends on position in phase space, it is crucial obtain an estimate error that derives from finite number reference structures included during model training. When using machine-learning potential sample finite-temperature...
Predictions of relative stabilities (competing) molecular crystals are great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge modeling, as often minuscule free energy differences sensitively affected by description electronic structure, statistical mechanics nuclei and cell, thermal expansion. The importance these effects has been individually established, but rigorous calculations general compounds, which simultaneously...
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and dynamics, they generally lack the accuracy transferability required predictive modelling. In this paper, we introduce MACE-OFF23, a transferable field organic molecules created using state-of-the-art machine learning technology first-principles reference data computed with high level of quantum mechanical theory. MACE-OFF23...
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra challenge that requires first-principles simulations, including non-Condon quantum nuclear effects. We address this by developing machine-learning enhanced framework speed up predictive modelling of infrared, Raman, sum-frequency generation spectra. Our uses machine learning potentials encode effects generate trajectories...
The adsorption energy of a molecule onto the surface material underpins wide array applications, spanning heterogeneous catalysis, gas storage, and many more. It is key quantity where experimental measurements theoretical calculations meet, with agreement being necessary for reliable predictions chemical reaction rates mechanisms. prototypical molecule–surface system CO adsorbed on MgO, but despite intense scrutiny from theory experiment, there still no consensus its energy. In particular,...
Abstract The Bernal-Fowler ice rules stipulate that each water molecule in an crystal should form four hydrogen bonds. However, extreme or constrained conditions, the arrangement of molecules deviates from conventional rules, resulting properties significantly different bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism nanoconfined phases. Instead forming bonds, crystalline can quasi-one-dimensional...
Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to availability of machine-learning interatomic potentials. These potentials combine accuracy electronic structure calculations with ability reach extensive length and time scales. The i-PI package facilitates integrating latest developments in this field advanced modeling techniques a modular software architecture based on inter-process communication through socket interface. choice Python for...
The development and implementation of increasingly accurate methods for electronic structure calculations mean that, many atomistic simulation problems, treating light nuclei as classical particles is now one the most serious approximations. Even though recent developments have significantly reduced overhead modelling quantum nature nuclei, cost still prohibitive when combined with advanced methods. Here we present how multiple time step integrators can be ring-polymer contraction techniques...
The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need simulating multiple replicas system. Many approaches have been suggested reduce required number replicas. Among these, high-order factorizations Boltzmann operator are particularly attractive for high-precision and low-temperature scenarios. Unfortunately, date, several technical challenges...
Generalized Langevin Equation (GLE) thermostats have been used very effectively as a tool to manipulate and optimize the sampling of thermodynamic ensembles associated static properties. Here we show that similar, exquisite level control can be achieved for dynamical properties computed from thermostatted trajectories. We develop quantitative measures disturbance induced by GLE Hamiltonian dynamics harmonic oscillator, these analytical results accurately predict behavior strongly anharmonic...
Quantitative evaluation of the thermodynamic properties materials-most notably their stability, as measured by free energy-must take into account role thermal and zero-point energy fluctuations. While these effects can easily be estimated within a harmonic approximation, corrections arising from anharmonic nature interatomic potential are often crucial require computationally costly path integral simulations to obtain results that essentially exact for given potential. Consequently,...
The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime high reactivity, need high-level electronic structure achieve predictive accuracy. lack classical atomistic structural formula makes it exceedingly difficult model solvated using conventional empirical force fields, which describe system in terms interactions between point particles associated with atomic nuclei. Here we overcome this problem machine-learning model, that is...
Metadynamics (MTD) is a very powerful technique to sample high‐dimensional free energy landscapes, and due its self‐guiding property, the method has been successful in studying complex reactions conformational changes. MTD sampling based on filling basins by biasing potentials thus for cases with flat, broad, unbound wells, computational time them becomes large. To alleviate this problem, we combine standard Umbrella Sampling (US) orthogonal collective variables (CVs) simultaneous way....
The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling these nuclear effects requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost such simulations using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path equation methods, however, have this far limited study static, thermodynamic due large perturbation system's induced...
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-mechanical behavior light nuclei. Full-dimensional simulations approximate quantum dynamics possible thanks to the imaginary time path-integral (PI) formulation statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop PI method reduced classical simulation. We also propose simple...
Vibrational spectroscopy is key in probing the interplay between structure and dynamics of aqueous systems. To map different regions experimental spectra to microscopic a system, it important combine them with first-principles atomistic simulations that incorporate quantum nature nuclei. Here we show large cost calculating vibrational systems can be dramatically reduced compared standard path integral methods by using approximate based on high-order integrals. Together state-of-the-art...
The O vacancy (Ov) formation energy, EOv, is an important property of a metal-oxide, governing its performance in applications such as fuel cells or heterogeneous catalysis. These defects are routinely studied with density functional theory (DFT). However, it well-recognized that standard DFT formulations (e.g., the generalized gradient approximation) insufficient for modeling Ov, requiring higher levels theory. embedded cluster method offers promising approach to compute EOv accurately,...
An understanding of the CO2 + H2O hydration reaction is crucial for modeling effects ocean acidification, enabling novel carbon storage solutions, and as a model process in geosciences. While mechanism this has been investigated extensively condensed phase, its at air-water interface remains elusive, leaving uncertain contribution that surface-adsorbed makes to overall acidification reaction. In study, we employ state-of-the-art machine-learned potentials provide molecular-level interface....
Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and dynamics, they generally lack the accuracy transferability required first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable organic molecules created using state-of-the-art machine learning technology reference data computed with high level quantum mechanical theory....
Metal-organic frameworks show both fundamental interest and great promise for applications in adsorption-based technologies, such as the separation storage of gases. The flexibility complexity molecular scaffold poses a considerable challenge to atomistic modeling, especially when also considering presence guest molecules. We investigate role played by quantum anharmonic fluctuations archetypical case MOF-5, comparing material at various levels methane loading. Accurate path integral...