BetaCitation Atlas

RESEARCH PAPER · Journal of Materiomics · 2017

Materials discovery and design using machine learning

Yue Liu, Tianlu Zhao, Wangwei Ju, Siqi Shi

Read original paper ↗

See where this research leads.

1,260 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Materials discovery and design using machine learningOriginalPredictive and generative machine learning models for photonic crystals101Interface Characterization and Control of 2D Materials and Heterostructures212Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes279Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies134Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods332Combining electronic and structural features in machine learning models to predict organic solar cells properties177Rationalizing Perovskite Data for Machine Learning and Materials Design101Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures102Machine learning in materials design and discovery: Examples from the present and suggestions for the future198Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications84Band gap and band alignment prediction of nitride-based semiconductors using machine learning85Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases104First-principles study of alkali-metal intercalation in disordered carbon anode materials105Machine learning for renewable energy materials312Nanoinformatics, and the big challenges for the science of small things92Rationalizing the interphase stability of Li|doped-Li 7 La 3 Zr 2 O 12 via automated reaction screening and machine learning97Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Materials informatics: From the atomic-level to the continuum191From DFT to machine learning: recent approaches to materials science–a review849Nanomaterials Discovery and Design through Machine Learning113Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent135Machine learning for composite materials311Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists208Recent advances and applications of machine learning in solid-state materials science2607Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models179Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel493Data‐Driven Materials Science: Status, Challenges, and Perspectives700Recent Advances in Graphene-like 2D Materials for Spintronics Applications207Generative Counterfactual Introspection for Explainable Deep Learning83Machine Learning for Accelerated Discovery of Solar Photocatalysts179The chemical physics of sequential infiltration synthesis—A thermodynamic and kinetic perspective124Machine learning on density and elastic property of oxide glasses driven by large dataset96Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research104Machine learning-driven new material discovery296Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures205Opportunities for nanotechnology to enhance electrochemical treatment of pollutants in potable water and industrial wastewater – a perspective126Inverse design of porous materials using artificial neural networks354Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191A Critical Review of Machine Learning of Energy Materials606Semantic segmentation of the micro-structure of strain-hardening cement-based composites (SHCC) by applying deep learning on micro-computed tomography scans92Genetic algorithm-driven discovery of unexpected thermal conductivity enhancement by disorder90Machine learning: Accelerating materials development for energy storage and conversion370Machine learning as a tool to design glasses with controlled dissolution for healthcare applications98From chemical structure to quantitative polymer properties prediction through convolutional neural networks111Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning98Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210Experimental, computational and thermodynamic studies in perovskites metal oxides for thermochemical fuel production: A review94Chalcogenides by Design: Functionality through Metavalent Bonding and Confinement3103-D printing of concrete: Beyond horizons260Accelerated design and characterization of non-uniform cellular materials via a machine-learning based framework90Designing complex architectured materials with generative adversarial networks335Machine learning in materials genome initiative: A review231Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning271Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms197Deep learning for fabrication and maturation of 3D bioprinted tissues and organs161Prediction of surface chloride concentration of marine concrete using ensemble machine learning284Machine learning assisted materials design and discovery for rechargeable batteries466Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook550Towards controlled synthesis of 2D crystals by chemical vapor deposition (CVD)173Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends147Orbital graph convolutional neural network for material property prediction153Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm387Recent advances in hot tearing during casting of aluminium alloys234ALKEMIE: An intelligent computational platform for accelerating materials discovery and design156A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys148Online prediction of mechanical properties of hot rolled steel plate using machine learning215Artificial Intelligence in Materials Modeling and Design98Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering182Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning128Machine learning glass transition temperature of styrenic random copolymers94Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91Yield strength prediction of high-entropy alloys using machine learning130Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm246Accelerating materials discovery using machine learning238Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach96Polymer informatics: Current status and critical next steps272Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures96Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach92Machine learning for advanced energy materials208Predicting stable crystalline compounds using chemical similarity113Machine learning for perovskite materials design and discovery536Machine learning for design of thin-film nanocomposite membranes88Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder99Applying machine learning approach in recycling129Machine learning for material characterization with an application for predicting mechanical properties139Wood Modification Technologies132Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors118The 2021 flexible and printed electronics roadmap213A machine-learning approach to predict creep properties of Cr–Mo steel with time-temperature parameters91A focused review on machine learning aided high-throughput methods in high entropy alloy134Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators155JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics85Machine learning on neutron and x-ray scattering and spectroscopies116Machine learning in predicting mechanical behavior of additively manufactured parts200Bayesian neural networks for uncertainty quantification in data-driven materials modeling191Systems level roadmap for solvent recovery and reuse in industries123Artificial Intelligence Applied to Battery Research: Hype or Reality?503Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator91Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review113Novel Materials for Urban Farming91High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research140Uncertainty Prediction for Machine Learning Models of Material Properties95Artificial intelligence: machine learning for chemical sciences104Machine learning for design principles for single atom catalysts towards electrochemical reactions116Recent trends in computational tools and data-driven modeling for advanced materials85Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design285Tackling Climate Change with Machine Learning1015Material research from the viewpoint of functional motifs103Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model112Machine learning analysis of alloying element effects on hydrogen storage properties of AB2 metal hydrides118Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172Machine learning in energy storage materials106Data‐Driven Materials Innovation and Applications187A review of the application of machine learning in water quality evaluation793Material machine learning for alloys: Applications, challenges and perspectives150Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning88Feature selection method reducing correlations among features by embedding domain knowledge101A Generative Approach to Materials Discovery, Design, and Optimization121Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives90Machine learning for battery research110Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119A review on the application of molecular descriptors and machine learning in polymer design108Electron Microscopy Studies of Soft Nanomaterials107Enhancing property prediction and process optimization in building materials through machine learning: A review161Designing All-Solid-State Batteries by Theoretical Computation: A Review122Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Data quantity governance for machine learning in materials science228Generative artificial intelligence and its applications in materials science: Current situation and future perspectives293Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design372Unleashing the Power of Artificial Intelligence in Materials Design179Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments124Go with the flow: Rheological requirements for direct ink write printability171From prediction to design: Recent advances in machine learning for the study of 2D materials90Machine learning-enabled constrained multi-objective design of architected materials172Scope of machine learning in materials research—A review230Recent advances in metal-organic framework (MOF)-based agricultural sensors for metal ions: a review103AI methods in materials design, discovery and manufacturing: A review142Machine learning for membrane design in energy production, gas separation, and water treatment: a review111Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments88Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites170Machine Learning Aided Design and Optimization of Thermal Metamaterials195AI-Based Metamaterial Design95Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage94Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes159Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation95SciAgents: Automating Scientific Discovery Through Bioinspired Multi‐Agent Intelligent Graph Reasoning146Wear- and Corrosion-Resistant Coatings for Extreme Environments: Advances, Challenges, and Future Perspectives92Review of machine learning approaches for predicting mechanical behavior of composite materials83

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

1260 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. The onset temperature (Tg) of As Se1 glasses transition prediction: A comparison of topological and regression analysis methods

    Computational Materials Science · 2017-09-15

    Yue Liu, Tianlu Zhao, Guang Yang, Wangwei Ju, Siqi Shi

    Read original paper ↗ ·

  2. Substantiation of the structure theory of design of technological machines and devices

    Technology audit and production reserves · 2017-09-21

    Л. В. Лось, Savelii Kukharets, Nataliya Tsyvenkova, Anna Нolubenko, Marina Tereshchuk

    Read original paper ↗ ·

  3. Challenges for Theory and Computation

    Computation · 2017-12-04

    Karlheinz Schwarz

    Read original paper ↗ ·

  4. Materials genome: research progress, challenges and outlook

    Scientia Sinica Chimica · 2017-12-08

    Yunqi Li, Lunyang Liu, Wenduo Chen, Lijia An

    Read original paper ↗ ·

  5. Substatiation of quantitative criteria of structural parts and units manufacturability evaluation

    Technology audit and production reserves · 2017-12-28

    Yaroslav Yarosh, Nataliya Tsyvenkova, Savelii Kukharets, Anna Нolubenko, Л. В. Лось

    Read original paper ↗ ·

  6. A Mathematical Framework for Deep Learning in Elastic Source Imaging

    SIAM Journal on Applied Mathematics · 2018-01-01

    Jaejun Yoo, Abdul Wahab, Jong Chul Ye

    Read original paper ↗ ·

  7. FS4RVDD: A Feature Selection Algorithm for Random Variables with Discrete Distribution

    Communications in computer and information science · 2018-01-01

    Fiorella Cravero, Santiago A. Schustik, María Jimena Martínez, Mónica F. Díaz, Ignacio Ponzoni

    Read original paper ↗ ·

  8. Towards the systematic crystallisation of molecular ionic cocrystals: insights from computed crystal form landscapes

    Faraday Discussions · 2018-01-01

    Sharmarke Mohamed, Ahmad A. Alwan, Tomislav Friščić, Andrew J. Morris, Mihails Arhangelskis

    Read original paper ↗ ·

  9. Understanding structural adaptability: a reactant informatics approach to experiment design

    Molecular Systems Design & Engineering · 2018-01-01

    Rosalind J. Xu, Jacob H. Olshansky, Philip Adler, Yongjia Huang, Matthew D. Smith, Mat­thias Zeller, Joshua Schrier, Alexander J. Norquist

    Read original paper ↗ ·

  10. Using neural networks to predict the low curves and processing maps of TNM-B1

    Computer Methods in Materials Science. · 2018-01-01

    Johan Andreas Stendal, Aliakbar Emdadi, Irina Sizova, Markus� Bambach

    Read original paper ↗ ·

  11. Modeling and characterization of mechanical properties in laser powder bed fusion additive manufactured Inconel 718

    Digital Collections of Colorado (Colorado State University) · 2018-03-05

    Senthamilaruvi Moorthy

    Read original paper ↗ ·

  12. Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

    Materials Research Express · 2018-03-16

    Yifan Deng, Huidan Zeng, Yejia Jiang, Guorong Chen, Jianding Chen, Luyi Sun

    Read original paper ↗ ·

  13. A living foundry for Synthetic Biological Materials: A synthetic biology roadmap to new advanced materials

    Synthetic and Systems Biotechnology · 2018-04-17

    Rosalind Le Feuvre, Nigel Shaun Scrutton

    Read original paper ↗ ·

  14. Predictive and generative machine learning models for photonic crystals

    Nanophotonics · 2018-06-01

    Thomas Christensen, Charlotte Loh, Stjepan Picek, Domagoj Jakobović, Li Jing, Sophie Fisher, Vladimir Čeperić, John D. Joannopoulos, Marin Soljacic A

    Read original paper ↗ ·

  15. Comparative study of various cathodes for lithium ion batteries using an enhanced Peukert capacity model

    Journal of Power Sources · 2018-06-23

    Ryan O'Malley, Lin Liu, Christopher Depcik

    Read original paper ↗ ·

  16. Applying machine learning to accelerate new materials development

    Zhongguo kexue. Wulixue Lixue Tianwenxue · 2018-07-05

    Wei Wu, Qiang Sun

    Read original paper ↗ ·

  17. Interface Characterization and Control of 2D Materials and Heterostructures

    Advanced Materials · 2018-07-23

    Xiaolong Liu, Mark C. Hersam

    Read original paper ↗ ·

  18. Rapid Evaluation of Dynamic Electronic Disorder in Molecular Semiconductors

    The Journal of Physical Chemistry C · 2018-07-24

    Alessandro Landi, Alessandro Troisi

    Read original paper ↗ ·

  19. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

    ACS Central Science · 2018-08-10

    Zeeshan Ahmad, Tian Xie, Chinmay Maheshwari, Jeffrey C. Grossman, Venkatasubramanian Viswanathan

    Read original paper ↗ ·

  20. Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies

    ACS Central Science · 2018-08-20

    Masato Sumita, Xiufeng Yang, Shinsuke Ishihara, Ryo Tamura, Koji Tsuda

    Read original paper ↗ ·

  21. Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods

    International Journal of Heat and Mass Transfer · 2018-08-25

    Han Wei, Shuaishuai Zhao, Qingyuan Rong, Hua Bao

    Read original paper ↗ ·

  22. Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach

    Organic Electronics · 2018-09-25

    Muhammad Asyraf Bin Janai, Kai Lin Woon, Chee Seng Chan

    Read original paper ↗ ·

  23. Towards Simulation-Data Science – A Case Study on Material Failures

    IEEE International Conference on Data Science and Advanced Analytics (DSAA) · 2018-10-01

    Holger Trittenbach, Martin Gauch, Klemens Böhm, Katrin Schulz

    Read original paper ↗ ·

  24. Modelling feasibility constraints for materials design: Application to inverse crystallographic texture problem

    Computational Materials Science · 2018-10-17

    Jaimyun Jung, Jae Ik Yoon, Seong‐Jun Park, Jun‐Yun Kang, Gwang Lyeon Kim, Yi Hwa Song, Sung Taek Park, Kyeong Won Oh, Hyoung Seop Kim

    Read original paper ↗ ·

  25. Machine learning band gap and alignment of nitride semiconductors

    arXiv (Cornell University) · 2018-10-24

    Yang Huang, Fei Wang, Changyou Yu, Yuhuai Liu, Chong Li, Chunyao Niu, Yu Jia

    Read original paper ↗ ·

  26. Committee machine that votes for similarity between materials

    IUCrJ · 2018-10-29

    Duong‐Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, KEISUKE G. TAKAHASHI, Hieu‐Chi Dam

    Read original paper ↗ ·

  27. Important Descriptors and Descriptor Groups of Curie Temperatures of Rare-earth Transition-metal Binary Alloys

    Journal of the Physical Society of Japan · 2018-10-29

    Hieu‐Chi Dam, Viet-Cuong Nguyen, Tien-Lam Pham, Anh Tuan Nguyen, Kiyoyuki Terakura, Takashi Miyake, Hiori Kino

    Read original paper ↗ ·

  28. Combining electronic and structural features in machine learning models to predict organic solar cells properties

    Materials Horizons · 2018-10-30

    Daniele Padula, Jack D. Simpson, Alessandro Troisi

    Read original paper ↗ ·

  29. Applications of Knowledge Technology in Construction Industry

    International Conference on Emerging eLearning Technologies and Applications (ICETA) · 2018-11-01

    Peter Mésároš, Tomáš Mandičák, Marcel Behún, Jana Smetanková

    Read original paper ↗ ·

  30. The material influence factors on the actual capacity of lithium ion battery

    Chinese Science Bulletin (Chinese Version) · 2018-11-01

    Ning Chen, Bo Wang, Yang Liu, Lijun Wang, Yang Li, Fushen Li, Hailei Zhao

    Read original paper ↗ ·

  31. Synthesis of Lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening

    Chemistry Letters · 2018-11-16

    Kan Hatakeyama‐Sato, Toshiki Tezuka, Yoshinori Nishikitani, Hiroyuki Nishide, Kenichi Oyaizu

    Read original paper ↗ ·

  32. Thermal performance of a novel ultrasonic evaporator based on machine learning algorithms

    Applied Thermal Engineering · 2018-11-22

    Jitian Song, Wei Tian, Xiaofei Xu, Yening Wang, Zhanyong Li

    Read original paper ↗ ·

  33. 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys

    arXiv (Cornell University) · 2018-11-23

    Rahul Singh, Aayush Sharma, Onur Rauf Bingöl, Aditya Balu, Ganesh Balasubramanian, Duane D. Johnson, Soumik Sarkar

    Read original paper ↗ ·

  34. Predictive Analytics for Biomineralization Peptide Binding Affinity

    BioNanoScience · 2018-11-23

    Jose Isagani B. Janairo

    Read original paper ↗ ·

  35. Rationalizing Perovskite Data for Machine Learning and Materials Design

    The Journal of Physical Chemistry Letters · 2018-11-27

    Qichen Xu, Zhenzhu Li, Miao Liu, Wan‐Jian Yin

    Read original paper ↗ ·

  36. Machine learning of ab-initio energy landscapes for crystal structure predictions

    Computational Materials Science · 2018-11-28

    Shreyas J. Honrao, Bryan E. Anthonio, Rohit Ramanathan, Joshua J. Gabriel, Richard G. Hennig

    Read original paper ↗ ·

  37. Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures

    Modelling and Simulation in Materials Science and Engineering · 2018-12-17

    Anurag Jha, Anand R. Chandrasekaran, Chiho Kim, Rampi Ramprasad

    Read original paper ↗ ·

  38. Machine learning in materials design and discovery: Examples from the present and suggestions for the future

    Physical Review Materials · 2018-12-20

    James E. Gubernatis, Turab Lookman

    Read original paper ↗ ·

  39. Artificial neural networks and vector regression models for prediction of lattice constants of half-Heusler compounds

    Materials Research Express · 2018-12-21

    Rashid Ahmad, Aqsa Gul, Nasir Mehmood

    Read original paper ↗ ·

  40. Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications

    Advanced Materials · 2018-12-27

    Jinyang Li, Gustavo Doubek, Lyndsey McMillon‐Brown, André D. Taylor

    Read original paper ↗ ·

  41. Optimization of Machine Learning in Predicting Fracture Behaviour of Materials in AI

    Türk bilgisayar ve matematik eğitimi dergisi · 2018-12-30

    Gaurav Joshi

    Read original paper ↗ ·

  42. Band gap and band alignment prediction of nitride-based semiconductors using machine learning

    Journal of Materials Chemistry C · 2019-01-01

    Yang Huang, Changyou Yu, Weiguang Chen, Yuhuai Liu, Chong Li, Chunyao Niu, Fei Wang, Yu Jia

    Read original paper ↗ ·

  43. Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases

    Physical Chemistry Chemical Physics · 2019-01-01

    Deepak Jain, Suryanaman Chaube, Prerna Khullar, Sriram Goverapet Srinivasan, Beena Rai

    Read original paper ↗ ·

  44. Deformation Correlations and Machine Learning: Microstructural inference and crystal plasticity predictions

    Unknown journal · 2019-01-01

    Michail Tzimas

    Read original paper ↗ ·

  45. First-principles study of alkali-metal intercalation in disordered carbon anode materials

    Journal of Materials Chemistry A · 2019-01-01

    Jianxing Huang, Gábor Cśanyi, Jinbao Zhao, Jun Jie Cheng, Volker L. Deringer

    Read original paper ↗ ·

  46. Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes

    Chemical Science · 2019-01-01

    Alexander M. Chang, Jessica G. Freeze, Víctor S. Batista

    Read original paper ↗ ·

  47. Machine learning for renewable energy materials

    Journal of Materials Chemistry A · 2019-01-01

    Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung

    Read original paper ↗ ·

  48. Mathematical Strategies for Design Optimization of Multiphase Materials

    Mathematical Problems in Engineering · 2019-01-01

    Rick Catania, Abdalla Diraz, Dominic Maier, Armani Tagle, Pınar Acar

    Read original paper ↗ ·

  49. Nanoinformatics, and the big challenges for the science of small things

    Nanoscale · 2019-01-01

    Amanda Susan Barnard, Benyamin Motevalli, Amanda J. Parker, Julia Melisande Theresa Agatha Fischer, Christopher A. Feigl, George Opletal

    Read original paper ↗ ·

  50. Rationalizing the interphase stability of Li|doped-Li 7 La 3 Zr 2 O 12 via automated reaction screening and machine learning

    Journal of Materials Chemistry A · 2019-01-01

    Bo Liu, Jiong Yang, Hongliang Yang, Caichao Ye, Yuanqing Mao, Jiping Wang, Siqi Shi, Jihui Yang, Wenqing Zhang

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.