Welcome to
Christopher Sutton's
research page on 
computational chemistry and materials science

Welcome to
Christopher Sutton's
research page on
computational chemistry and materials science

Machine
Learning Methods
Development of high-throughput methods and statistical learning methods for the computational screening of new materials
Computational Materials Discovery
Reliable screening of materials at relevant temperatures and realistic experimental conditions using efficient numerical models and fast stochastic algorithms to enable compositional space exploration
Electronic Structure Calculations
 Ab initio examination of excited states and non-covalent interactions in materials and molecules
Machine Learning Methods
Development of high-throughput methods and statistical learning methods for the computational screening of new materials
Computational Materials Discovery
Reliable screening of materials at relevant temperatures and realistic experimental conditions using efficient numerical models and fast stochastic algorithms to enable compositional space exploration
Electronic Structure Calculations
 Ab initio examination of excited states and non-covalent interactions in materials and molecules

New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

We used a novel data analytics approach called SISSO (sure independence screening and sparsifying operator), to derive an improved descriptor …
  • …read more

    … (or tolerance factor) τ, that correctly classifies 92% of compounds as perovskite or nonperovskite for an experimental dataset containing 576 ABX3 materials (X = O2-, F-, Cl-, Br-, I-). In comparison, the widely used Goldschmidt tolerance factor, t, achieves a maximum accuracy of only 74% for the same set of materials. Currently our work is focused on investigating our predictions with DFT on a double perovskites with formula A2BB’X6.

Read here
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

Button

New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

We used a novel data analytics approach called SISSO (sure independence screening and sparsifying operator), to derive an improved descriptor (or tolerance factor) τ, that correctly classifies 92% of compounds as perovskite or nonperovskite for an experimental dataset containing 576 ABX3 materials (X = O2-, F-, Cl-, Br-, I-).
  • …read more

    … (or tolerance factor) τ, that correctly classifies 92% of compounds as perovskite or nonperovskite for an experimental dataset containing 576 ABX3 materials (X = O2-, F-, Cl-, Br-, I-). In comparison, the widely used Goldschmidt tolerance factor, t, achieves a maximum accuracy of only 74% for the same set of materials. Currently our work is focused on investigating our predictions with DFT on a double perovskites with formula A2BB’X6.

Read here

Noncovalent intermolecular interactions in organic electronic materials: implications for the molecular packing vs electronic properties of acenes

Noncovalent intermolecular interactions, which can be tuned through the toolbox of synthetic chemistry, determine not only the molecular packing but also the resulting electronic, optical, and mechanical properties of materials derived from π-conjugated molecules, oligomers, and polymers.
  • …read more

    … Here, we provide an overview of the theoretical underpinnings of noncovalent intermolecular interactions and briefly discuss the computational chemistry approaches used to understand the magnitude of these interactions. These methodologies are then exploited to illustrate how noncovalent intermolecular interactions impact important electronic propertiesî—¸such as the electronic coupling between adjacent molecules, a key parameter for charge-carrier transport through a comparison between the prototype organic semiconductor pentacene with a series of N-substituted heteropentacenes.

Read here

Machine Learning for Heterogeneous Catalyst Design and Discovery

We recently highlighted several examples where machine learning is making an impact on hetero- geneous catalysis research, such as: accelerating the determination of catalyst active sites and catalyst screening; finnding descriptors and patterns in catalysis data; deter- mining interatomic potentials for catalyst simulation; and discovering and analyzing catalytic mechanisms.
Read here
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

Button
Machine learning for heterogeneous catalyst design and discovery

Machine learning for heterogeneous catalyst design and discovery

Button

Noncovalent intermolecular interactions in organic electronic materials: implications for the molecular packing vs electronic properties of acenes

Noncovalent intermolecular interactions, which can be tuned through the toolbox of synthetic chemistry, determine not only the molecular packing but also the resulting electronic, optical, and mechanical properties of materials derived from π-conjugated molecules, oligomers, and polymers.
  • …read more

    … Here, we provide an overview of the theoretical underpinnings of noncovalent intermolecular interactions and briefly discuss the computational chemistry approaches used to understand the magnitude of these interactions. These methodologies are then exploited to illustrate how noncovalent intermolecular interactions impact important electronic propertiesî—¸such as the electronic coupling between adjacent molecules, a key parameter for charge-carrier transport through a comparison between the prototype organic semiconductor pentacene with a series of N-substituted heteropentacenes.

Read here

Machine Learning for Heterogeneous Catalyst Design and Discovery

We recently highlighted several examples where machine learning is making an impact on hetero- geneous catalysis research, such as: accelerating the determination of catalyst active sites and catalyst screening; finnding descriptors and patterns in catalysis data; deter- mining interatomic potentials for catalyst simulation; and discovering and analyzing catalytic mechanisms.
Read here
Machine learning for heterogeneous catalyst design and discovery

Machine learning for heterogeneous catalyst design and discovery

Button