相关论文: Towards explainable message passing networks for p…
Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming…
Metal-organic frameworks (MOFs) are highly promising for carbon capture, yet navigating their vast design space remains challenging. Recent deep generative models enable de novo MOF design but primarily act as feed-forward structure…
Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the…
We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based models are accurate but computationally…
Controlling the transport of thermal energy is key to most applications of metal-organic frameworks. Analyzing the evolution of the effective local temperature, the interfaces between the metal nodes and the organic linkers are identified…
The building climate industry and its influence on energy consumption have consequences on the environment due to the emission of greenhouse gasses. Improving the efficiency of this sector is essential to reduce the effect on climate…
The extra CO2 that has already been released into the atmosphere has to be removed in order to create a world that is carbon neutral. Technologies have been created to remove carbon dioxide from wet flue gas or even directly from ambient…
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although convolution neural networks (CNNs) have excelled in many vision tasks, they are still limited in capturing long-range structured…
Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationships between the initial MOF and the resulting MOF-derived…
This work presents a physics-based machine learning framework to predict and analyze oxides of nitrogen (NOx) emissions from compression-ignition engine-powered vehicles using on-board diagnostics (OBD) data as input. Accurate NOx…
Electrocatalytic CO2 reduction technology is key to mitigating greenhouse gas emissions and the energy crisis. However, controlling the selectivity of CO2RR products at low overpotential remains a challenge. In this paper, we predicted five…
Metal-organic frameworks (MOFs) can provide exceptional porosity for molecular guest encapsulation useful for emergent applications in sensing, gas storage, drug delivery and optoelectronics. Central to the realisation of such applications…
Message passing neural networks (MPNNs) operate on graphs by exchanging information between neigbouring nodes. MPNNs have been successfully applied to various node-, edge-, and graph-level tasks in areas like molecular science, computer…
This research was focused on the efficient collection of experimental Metal-Organic Framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available…
This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks. Our main contributions in…
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide (CO2), specifically targeting novel metal organic framework materials. Our proposed SMF is hierarchical,…
Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches…
Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. Trial-and-error approach that relies on a chemist's intuition and…
Luminescent metal-organic frameworks (LMOFs) are promising materials for organic light-emitting diode (OLED) alternatives to silicate-based LEDs due to their tuneable structure and programmability. Yet, the 3D nature of LMOFs creates…
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially…