Edirisuriya M. Dilanga Siriwardane

University of Colombo — LK

Materials Science · Physical Sciences

16h-index793citations64works3.92yr avg
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Research Topics

Machine Learning in Materials Science(40), MXene and MAX Phase Materials(17), X-ray Diffraction in Crystallography(16), 2D Materials and Applications(12), Computational Drug Discovery Methods(9)

Publications64 total

Influence of Stacking and Pressure on Bilayer and 3D Y2XT2 (X= C, N and T= O, F, Cl) MXenes
2025
Exploiting Transformer-Based Networks and Boosting Algorithms for Ultralow Compressible Boride Design
The Journal of Physical Chemistry C·2025
Exploiting Transformer-Based Networks and Boosting Algorithms for Ultralow Compressible Boride Design
2025Open Access
Modulation of the electronic and magnetic properties of an MnCrNO<sub>2</sub> ferromagnetic semiconductor MXene
Physical Chemistry Chemical Physics·2024· 1 cited
<i>Ab initio</i> prediction of phase stability of quaternary Mo1−<i>x</i>M<i>x</i>AlB (M = Cr, Fe, Mn, Nb, Sc, Ta, Ti, V, and W) MAB solid solutions
Journal of Applied Physics·2024· 2 cited
Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials
Advanced Science·2024· 22 citedOpen Access
Generative Design of Inorganic Compounds Using Deep Diffusion Language Models
The Journal of Physical Chemistry A·2024· 4 cited
CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction
arXiv (Cornell University)·2024· 2 citedOpen Access
Discovery of 2D Materials using Transformer Network‐Based Generative Design
Advanced Intelligent Systems·2023· 14 citedOpen Access
Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials
ACS Omega·2023· 13 citedOpen Access
Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints
npj Computational Materials·2023Open Access
Physics guided deep learning for generative design of crystal materials with symmetry constraints
npj Computational Materials·2023· 99 citedOpen Access
Data-Driven Deep Generative Design of Stable Spintronic Materials
2023Open Access
Discovery of 2D materials using Transformer Network based Generative Design
arXiv (Cornell University)·2023Open Access
Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network
Digital Discovery·2023· 15 citedOpen Access
Data-driven deep generative design of stable spintronic materials
CrystEngComm·2023· 4 cited
Nanolaminated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi mathvariant="normal">Fe</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>A</mml:mi></mml:mrow><mml:msub><mml:mi mathvariant="normal">B</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi mathvariant="normal">Mn</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>A</mml:mi></mml:mrow><mml:msub><mml:mi mathvariant="normal">B</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo> </mml:mo><mml:mo>(</mml:mo><mml:mi>A</mml:mi><mml:mo> </mml:mo><mml:mn>=</mml:mn><mml:mo> </mml:mo><mml:mi>Al</mml:mi><mml:mo>,</mml:mo><mml:mi>Si</mml:mi><mml:mo>,</mml:mo><mml:mi>Ga</mml:mi><mml:mo>,</mml:mo><mml:mi>In</mml:mi><mml:mo>)</mml:mo></mml:math> materials and the assessment of their electronic correlations
Physical Review Materials·2022· 6 cited
Material transformers: deep learning language models for generative materials design
Machine Learning Science and Technology·2022· 32 citedOpen Access
Generative design of stable semiconductor materials using deep learning and density functional theory
npj Computational Materials·2022· 23 citedOpen Access
Materials Transformers Language Models for Generative Materials Design: a benchmark study
arXiv (Cornell University)·2022· 3 citedOpen Access
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Frequent Co-authors

Jianjun Hu(36), Yong Zhao(16), Deniz Çakır(15), Yuqi Song(13), Rongzhi Dong(13), Nihang Fu(11), Sadman Sadeed Omee(9), Steph-Yves Louis(7), Wenhui Yang(7), Lai Wei(6), Rajendra P. Joshi(5), Neeraj Kumar(5), Rui Xin(5), Mohammed Al‐Fahdi(3), Ming Hu(3), Cem Sevik(3), Pragalv Karki(3), Yuxin Li(3), Alireza Nasiri(3), Zhenyao Wu(2)