Turab Lookman

发表

Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

Arun Mannodi-Kanakkithodi, Ghanshyam Pilania, Rampi Ramprasad, 2016, Scientific Reports.

Learning targeted materials properties from data

James Theiler, Turab Lookman, Prasanna V. Balachandran, 2016 .

Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning

Yumei Zhou, Yunfan Wang, Turab Lookman, 2020, Advanced science.

Handbook on Big Data and Machine Learning in the Physical Sciences

Kerstin Kleese van Dam, Ian Foster, Sergei V. Kalinin, 2020, Handbook on Big Data and Machine Learning in the Physical Sciences.

Adaptive Strategies for Materials Design using Uncertainties

James Theiler, Turab Lookman, Prasanna V. Balachandran, 2016, Scientific Reports.

Bayesian Global Optimization applied to the design of shape-memory alloys

Yuan Tian, Turab Lookman, Dezhen Xue, 2020 .

Inferring low-dimensional microstructure representations using convolutional neural networks

Nicholas Lubbers, Kipton Barros, Turab Lookman, 2016, Physical review. E.

Optimal experimental design for materials discovery

Edward R. Dougherty, Mohammadmahdi R. Yousefi, Roozbeh Dehghannasiri, 2017 .

The climate attractor over short timescales

Christopher Essex, Turab Lookman, M. A. H. Nerenberg, 1987, Nature.

Automatized convergence of optoelectronic simulations using active machine learning

Kipton Barros, Turab Lookman, Colin J. Humphreys, 2017 .

Multi-objective Optimization for Materials Discovery via Adaptive Design

Turab Lookman, Prasanna V Balachandran, Dezhen Xue, 2018, Scientific Reports.

Pareto fronts for multiobjective optimization design on materials data

James E. Gubernatis, Turab Lookman, Prasanna V. Balachandran, 2017 .

Numerical method for hydrodynamic transport of inhomogeneous polymer melts

Glenn H. Fredrickson, Turab Lookman, David M. Hall, 2007, J. Comput. Phys..

Steps towards improving the security of chaotic encryption.

Pei Yu, Turab Lookman, Blair Fraser, 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

Chaotic Time Series Analysis Using Short and Noisy Data Sets: Application to a Clinical Epilepsy Seizure

Turab Lookman, G. W. Frank, M. A. H. Nerenberg, 1989 .

Energy minimization related to the nonlinear Schrödinger equation

Shahid S. Siddiqi, Turab Lookman, Nauman Raza, 2009, J. Comput. Phys..

Accelerated search for materials with targeted properties by adaptive design

James Theiler, Turab Lookman, Prasanna V. Balachandran, 2016, Nature Communications.

Role of Features in the Adaptive Design of Materials

Turab Lookman, Prasanna V. Balachandran, 2017 .

Informatics guided Search for Magnetic Apatites

Turab Lookman, Prasanna V. Balachandran, 2015 .

Materials Prediction via Classification Learning

James Theiler, Turab Lookman, Prasanna V. Balachandran, 2015, Scientific Reports.

Spatial adaptive sampling in multiscale simulation

Kipton Barros, Christoph Junghans, Turab Lookman, 2014, Comput. Phys. Commun..

Handbook on Big Data and Machine Learning in the Physical Sciences: Volume 2: Advanced Analysis Solutions for Leading Experimental Techniques

Kerstin Kleese van Dam, Ian Foster, Sergei V. Kalinin, 2020 .

Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods

Ghanshyam Pilania, Turab Lookman, Prasanna V. Balachandran, 2020 .

National Lab and Government Talks

Renata Rawlings-Goss, Turab Lookman, Chuck Ward, 2016 .

Information Science for Materials Discovery and Design

Krishna Rajan, Turab Lookman, Francis J. Alexander, 2016 .

MaRIE theory, modeling and computation roadmap executive summary

Turab Lookman, 2010 .

Materials Classification & Accelerated Property Predictions using Machine Learning

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

Machine Learning Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homo- and Co-polymers.

Ghanshyam Pilania, Turab Lookman, Babetta L Marrone, 2019, Journal of chemical information and modeling.

Informatics-aided computational design of functional layered oxides

Turab Lookman, Prasanna V. Balachandran, James M. Rondinelli, 2016 .

Efficient sampling for decision making in materials discovery

Turab Lookman, Dezhen Xue, Yuan Tian, 2021, Chinese Physics B.

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

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

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Turab Lookman, Prasanna V Balachandran, Alp Sehirlioglu, 2018, Nature Communications.

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

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

Thermally activated avalanches: Jamming and the progression of needle domains

Turab Lookman, Xiangdong Ding, A. Saxena, 2011 .

Recovering the attractor: A review of chaotic time-series analysis

Turab Lookman, M. A. H. Nerenberg, G. W. Frank, 1990 .

Material descriptors for morphotropic phase boundary curvature in lead-free piezoelectrics

Jun Sun, Yumei Zhou, Turab Lookman, 2017 .

Spinodal decomposition in binary fluids under shear flow

Turab Lookman, Shiyi Chen, T. Lookman, 1997 .

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

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

Design of High Temperature Ti-Pd-Cr Shape Memory Alloys with Small Thermal Hysteresis

Jun Sun, Yumei Zhou, Guojun Zhang, 2016, Scientific Reports.

An informatics approach to transformation temperatures of NiTi-based shape memory alloys

Jun Sun, Yumei Zhou, Turab Lookman, 2017 .

Metastable phase transformation and hcp-ω transformation pathways in Ti and Zr under high hydrostatic pressures

Jun Sun, Turab Lookman, Xiangdong Ding, 2016 .

Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory

Christopher M Wolverton, James E. Gubernatis, Turab Lookman, 2018 .

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

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

Origins of Elastic Properties in Ordered Block Copolymer/Nanoparticle Composites

Turab Lookman, Kim Ø. Rasmussen, T. Lookman, 2004 .

Statistical error in a chord estimator of correlation dimension: The rule of five''

James Theiler, Turab Lookman, T. Lookman, 1993 .

Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design

Yumei Zhou, Turab Lookman, Dezhen Xue, 2019, Advanced science.

Elastic deformation of polycrystals.

Turab Lookman, Avadh Saxena, Rajeev Ahluwalia, 2003, Physical review letters.

Pattern formation in Ferroelastic Transitions

Turab Lookman, A. Saxena, T. Lookman, 2004 .

Machine learning assisted design of high entropy alloys with desired property

Turab Lookman, Dezhen Xue, Yanjing Su, 2019, Acta Materialia.

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

Turab Lookman, Prasanna V. Balachandran, Dezhen Xue, 2019, npj Computational Materials.

Dynamic strain loading of cubic to tetragonal martensites

Turab Lookman, Avadh Saxena, A. Saxena, 2006 .

Learning from data to design functional materials without inversion symmetry

Turab Lookman, Prasanna V. Balachandran, James M. Rondinelli, 2017, Nature Communications.

Predictions of New ABO3 Perovskite Compounds by Combining Machine Learning and Density Functional Theory

Christopher M Wolverton, James E. Gubernatis, Turab Lookman, 2018 .

Finding New Perovskite Halides via Machine Learning

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

Uniaxial stress-driven coupled grain boundary motion in hexagonal close-packed metals: A molecular dynamics study

Jun Sun, Turab Lookman, Xiangdong Ding, 2015 .

Computer simulation of martensitic textures

Alan R. Bishop, Turab Lookman, Avadh Saxena, 1998 .

Perspective: Codesign for materials science: An optimal learning approach

Alan R. Bishop, Turab Lookman, Francis J. Alexander, 2016 .

Role of Complex Energy Landscapes and Strains in Multiscale Inhomogeneities in Perovskite Manganites

A. R. Bishop, Turab Lookman, T. Lookman, 2013 .

The Cavity Method for the Rigidity Transition

Alan R. Bishop, Turab Lookman, A. Saxena, 2005 .

Importance of Feature Selection in Machine Learning and Adaptive Design for Materials

James Theiler, Turab Lookman, Prasanna V. Balachandran, 2018 .

Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning.

James Theiler, Turab Lookman, Prasanna V Balachandran, 2017, Acta crystallographica Section B, Structural science, crystal engineering and materials.

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

Kipton Barros, Turab Lookman, Bertrand Rouet-Leduc, 2016, Scientific Reports.

Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Xiaoning Qian, Edward R Dougherty, Turab Lookman, 2016, Proceedings of the National Academy of Sciences.