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Jonathan Ting

0000-0002-3889-5597

PhD candidate at the Australian National University (Knowledge representation and reasoning; Nanomaterials; Computational chemistry; Modelling and simulation; Bioinformatics and computational biology not elsewhere classified)

Canberra, AUS

Self-motivated PhD candidate with a particular interest in the application of computational technologies to natural sciences. Independent, hard-working, and fast-learning team player equipped with essential skills for researching and problem solving. My PhD project aims to apply machine learning techniques to the design of multimetallic nanoparticles as catalysts.

Publications

  • How does cross-conjugation influence thiol additions to enones? A computational study of thiol trapping by the naturally occurring divinyl ketones zerumbone and α-santonin
  • Data-driven causal inference of process-structure relationships in nanocatalysis
  • Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning
  • Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference
  • AuPd Nanoparticle Data Set
  • AuCo Nanoparticle Data Set
  • AuPt Nanoparticle Data Set
  • PtAu Nanoparticle Data Set
  • PtPd Nanoparticle Data Set
  • PtCo Nanoparticle Data Set
  • PdCo Nanoparticle Data Set
  • PdAu Nanoparticle Data Set
  • PdPt Nanoparticle Data Set
  • CoAu Nanoparticle Data Set
  • CoPd Nanoparticle Data Set
  • CoPt Nanoparticle Data Set
  • AuPdPt Nanoparticle Data Set
  • Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference
  • Sphractal: Estimating the Fractal Dimension of Surfaces Computed from Precise Atomic Coordinates via Box‐Counting Algorithm
  • Fractal Characterization of Simulated Metal Nanocatalysts in 3D

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