Prakriti Kayastha

Hello and welcome to my personal website!

I am Prakriti Kayastha, a PhD student at Northumbria University, UK. I am part of the Center for Doctoral Training in Renewable Energy Northeast Universities (ReNU). My work is based on modelling materials using density functional theory and machine learning.

My PhD advisor Lucy and I are interested in modelling materials for next-generation photovoltaics. This is a very broad topic in itself and I hope I can make a humble contribution to the field during my PhD years. Currently, I am working on modelling the properties of chalcogenide perovskites for solar cell applications. I am interested in modelling the phonons and the properties that can be derived from them - thermodynamics, IR, and Raman spectroscopy. Here is a list of topics I’m closely working with at the moment:

I have recently started to use machine learning potentials to model chalcogenide perovskites and related systems. However, I am always thinking about information theory, dataset curation and descriptor space.

I am also very interested in crystallography and symmetries of materials and I am always excited to talk about it, reach out!

Publications

Google Scholar
Orcid

• A first-principles thermodynamic model for the Ba−Zr−S system in equilibrium with sulfur vapour ACS Appl Energy Mater (2024)
• High temperature equilibrium of 3D and 2D chalcogenide perovskites Solar RRL (2023)
• The Physical Significance of Imaginary Phonon Modes in Crystals Electron Struct (2022)
• Quantum Machine Learning Transition Probabilities in Electronic Excitation Spectra across Chemical Space: The Resolution-vs.-Accuracy Dilemma Digital Discov (2022)
• Machine learning modeling of materials with a group-subgroup structure Mach Learn: Sci Technol (2021)
• High-throughput design of Peierls and charge density wave phases in Q1D organometallic materials J Chem Phys (2021)
• The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling J Chem Phys (2019)

Talks/Posters:

• Invited talk: Phase transitions in perovskites using machine learning potentials
  FHI-aims UK meeting, Warwick University 15th-16th May 2024
• Invited talk: Chalcogenide perovskites for photovolatics: guiding synthesis from first-principles
  Linköping University, Sweden 16th-17th April 2024
• Poster: Understanding the phase stability of BaZrS₃ using ab initio thermodynamics
  RSC Materials Chemistry Poster Symposium, London 17th November 2022
• Poster: Following the reaction: Computational spectroscopy of perovskite BaZrS₃
  IOP Advances in Photovoltaics conference, London on 23rd March 2022
  Emerging inorganic materials in thin-film photovoltaics Faraday Discussion, Bath on 4th and 5th July 2022
  CAMD summer school, Helsingør 15th August 2022
  Fall MRS, Boston 29th November 2022
  RSC 16th Materials Chemistry (MC16) conference, Dublin 5th July 2023 TDEP summer school, Linköping, 22nd August 2023
  PSDI ML Autumn school, Warrington 19th September 2023
• Talk: Symmetry constrained relaxation with FHI-aims, 10th June 2022 Imperial College London and 13th June 2022 University College London
• Poster: Phonopy-Spectroscopy: A computational spectroscopy modelling package
  Royal Society Software Solutions to the Challenges of Materials Modelling satellite meeting, Northampton on 8th June 2022
• Talk: Following the reaction: Computational spectroscopy of the BaZrS₃ perovskite
• Talk: Group theory of crystal structure preferences
  International Day of Women and Girls in Science, Hyderabad on 13th February 2020
• Poster: Data-driven chemical insights into stability of 18e- AB₂X₂ chalcogenides
  TIFR Annual Chemistry Conference, Mumbai on 17th October 2019
• Poster: Big Data of AB₂X₂ and AA’B₄X₄ materials
  Pan-TIFR chemistry meeting, Hyderabad on 30th November 2018 Note: If you find an error/inconsistency please reach out