Ghanshyam Pilania

发表

Learning from learning machines: a new generation of AI technology to meet the needs of science pdf

Peter J. Bickel, Kristofer E. Bouchard, Prabhat, 2021, ArXiv.

Machine learning elastic constants of multi-component alloys

Alankar Alankar, Vivek P Revi, Anjana Talapatra, 2021 .

Machine learning for materials design and discovery

Rama Vasudevan, Prasanna V. Balachandran, Ghanshyam Pilania, 2021 .

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

Francesca Tavazza, Kamal Choudhary, Ghanshyam Pilania, 2020 .

Structure classification and melting temperature prediction in octet AB solids via machine learning

James E. Gubernatis, Turab Lookman, Ghanshyam Pilania, 2015 .

Dielectric Properties of Carbon, Silicon and Germanium Based Polymers: A First Principles Study

Rampi Ramprasad, Ghanshyam Pilania, R. Ramprasad, 2012 .

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

Chiho Kim, Ramamurthy Ramprasad, Ghanshyam Pilania, 2016 .

Dielectric permittivity of ultrathin PbTiO3 nanowires from first principles

Rampi Ramprasad, Ghanshyam Pilania, R. Ramprasad, 2012, Journal of Materials Science.

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Turab Lookman, Graeme J. Ackland, Xiangdong Ding, 2018, npj Computational Materials.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

James E. Gubernatis, Turab Lookman, Ghanshyam Pilania, 2017 .

Machine learning properties of binary wurtzite superlattices

Xiang-Yang Liu, Ghanshyam Pilania, G. Pilania, 2018, Journal of Materials Science.

A Perspective on Materials Informatics: State-of-the-Art and Challenges

James Theiler, Kipton Barros, James E. Gubernatis, 2016 .

Applications of Modern Density Functional Theory to Surfaces and Interfaces

Rampi Ramprasad, Hong Zhu, Ghanshyam Pilania, 2012 .

How critical are the van der Waals interactions in polymer crystals?

Ramamurthy Ramprasad, Ghanshyam Pilania, Chenchen Wang, 2012, The journal of physical chemistry. A.

How critical are the van der Waals interactions in polymer crystals

Ramamurthy Ramprasad, Ghanshyam Pilania, G. Pilania, 2012 .

Finding New Perovskite Halides via Machine Learning

Chiho Kim, Turab Lookman, Prasanna V. Balachandran, 2016, Front. Mater..

Computational strategies for polymer dielectrics design

Rampi Ramprasad, Steven A. Boggs, Curt M. Breneman, 2014 .

Machine Learning and Materials Informatics: Recent Applications and Prospects

Chiho Kim, Arun Mannodi-Kanakkithodi, R. Ramprasad, 2017 .

Machine learning in materials informatics: recent applications and prospects

Chiho Kim, Arun Mannodi-Kanakkithodi, Rampi Ramprasad, 2017, npj Computational Materials.

Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites

Chiho Kim, Rampi Ramprasad, Ghanshyam Pilania, 2016 .

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia.

Rampi Ramprasad, Rohit Batra, Blas P Uberuaga, 2019, ACS applied materials & interfaces.