High-throughput Materials Discovery through Materials Genomics

Stories about this project:

Discovering better materials is essential for tackling the enormous challenges in developing new renewable energy sources. The experimental variables that must be considered to optimize a given property are too many and their relationships are too complex to allow anything but incremental improvements to be made. So how do we think "outside the box" to find entirely new materials? As part of the larger effort known as the "Materials Genome Initiative," approaches based on data-mining and materials informatics techniques can help screen new compounds with desired properties and features, at greatly accelerated rates, and provide insights into the design principles required to engineer improved materials. These tools will be used, in particular, to find better photovoltaics (in collaboration with Buriak and Shankar groups) and catalysts for solar fuels (in collaboration with Bergens group).

Project Files

Excellence in Undergraduate Teaching

Arthur Mar

Award

Excellence in Undergraduate Teaching

Arthur Mar

Award

Students' Choice Honour Roll

Arthur Mar

Award

A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family

Master Thesis

High-­throughput Materials Discovery through Materials Genomics

Stories about this project:

Status

Theme

Principal Investigator

Co-Investigator

Graduate students, Post-Doctoral Fellows, and Highly Qualified Personnel

Alumni

Partners

Faculties

Project Files

T12 P01 Poster 2017 Open House

T12 P01 AdutwumL Poster 2018 Symposium

T12 P01 GzylA Poster 2018 Symposium

T12 P01 MishraV Poster 2018 Symposium

Excellence in Undergraduate Teaching

Excellence in Undergraduate Teaching

Students' Choice Honour Roll

A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family

Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods

Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them

Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them

Accelerating the Discovery of Materials: Machine-Learning Approach

Accelerating the Discovery of Solid State Materials

Accelerating the Discovery of Solid State Materials

Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches

Accelerating the discovery of solid state materials: From traditional to machine- learning approaches

Active Learning for Optimum Experimental Design - Insight into Perovskite Oxides

Artificial Neural Network-Based Prediction of the Optical Properties of Spherical Core\textendash Shell Plasmonic Metastructures

Cerium-containing chalcohalides as tunable photoluminescent materials

Classification of Half-Heusler Compounds through Machine Learning Approaches

Classification of Half-Heusler Compounds through Machine-Learning Approaches

Classification of crystal structures: Machine-learning predictions and experimental validation

Coloured Li-containing intermetallic compounds

Coloured intermetallic compounds Li2ZnGa and Li2ZnIn

Coloured intermetallic compounds LiCu2Al and LiCu2Ga

Comparison of computational and experimental inorganic crystal structures

Comparison of computational and experimental inorganic crystal structures

Compositional and elemental descriptors for perovskite materials

Computational workshop

Crystallography in Chemistry and Materials Science

Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach

Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proof

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC

Effect of aliovalent bismuth substitution on structure and optical properties of CsSnBr3

Experimental Validation of High Thermoelectric Performance in RECuZnP2 Predicted by High-Throughput DFT Calculations

Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculations

Explainable machine learning in materials chemistry: Decision trees as scoring function

Exploring the colours of gold alloys with machine learning

Gordon Research Conference in Solid State Chemistry

Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

Hexagonal Double Perovskite Cs2AgCrCl6

High-Throughput Approaches for Discovering Thermoelectric Materials

Hot Carrier-Mediated Generation of Solar Fuels Using Plasmon-Semiconductor Nanoheterojunctions

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

How large is an atom?

How to look for compounds

How to look for compounds

How to look for compounds

In Search of Coloured Intermetallics

Insights into the Machine Learning Predictions of the Optical Response of Plasmon@Semiconductor Core-Shell Nanocylinders

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Introduction to Machine Learning: A Practical Workshop

Investigating ordering in chalcogenide solid electrolytes using solid-state NMR spectroscopy

Investigation of Li-Zn-X (X = Ga, In) coloured intermetallics

La4Ga2Se6O3: A rare-earth oxyselenide built from one-dimensional strips

Lost horses on the frontier: K2BiCl5 and K3Bi2Br9

Low bandgap carbon nitride nanoparticles incorporated in titania nanotube arrays by in situ electrophoretic anodization for photocatalytic CO 2 reduction

Luminescence properties of rare-earth chalcohalides RE3Ge2-xSixS8I (RE = La, Ce, Pr) and Ce3Si2S8-ySeyI for potential application in phosphor-converted white LEDs

High-throughput Materials Discovery through Materials Genomics