A5089299263- Machine Learning in Materials Science
- Scientific Computing and Data Management
- Catalysis and Oxidation Reactions
- Advanced Chemical Physics Studies
- Innovative Microfluidic and Catalytic Techniques Innovation
- Advanced Data Storage Technologies
- Synthesis and Catalytic Reactions
- Catalytic C–H Functionalization Methods
- Oxidative Organic Chemistry Reactions
- Computational Drug Discovery Methods
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
University of Cambridge
2020-2023
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,...
ConspectusThe visualization of data is indispensable in scientific research, from the early stages when human insight forms to final step communicating results. In computational physics, chemistry and materials science, it can be as simple making a scatter plot or straightforward looking through snapshots atomic positions manually. However, result "big data" revolution, these conventional approaches are often inadequate. The widespread adoption high-throughput computation for discovery...
Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use ML interatomic potentials new systems is often more demanding than that established density-functional theory (DFT) packages. Here, we describe computational methodology to combine CASTEP first-principles simulation software with on-the-fly fitting evaluation potential models. Our approach based on regular checking against DFT reference data, which provides a direct measure accuracy...
Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number individual calculations needed in such studies, workflow management packages have been developed a rapidly user base. These predominantly designed to computationally heavy ab initio calculations, usually with focus on provenance and reproducibility. However, related simulation communities, e.g.,...
Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number individual calculations needed in such studies, workflow management packages have been developed a rapidly user base. These predominantly designed to computationally heavy ab initio calculations, usually with focus on provenance and reproducibility. However, related simulation communities, e.g....
We present a computational mechanistic study on the copper(III)-catalysed carboarylation-ring closure reactions leading to formation of functionalised heterocycles. have performed DFT calculations along selected routes and compared their free energy profiles. The considered two viable options for underlying mechanism which differ in order oxazoline ring aryl transfer steps. In our model transformation, it was found that reaction generally features transfer-ring closing sequence this shows...