BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2019

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques

Read original paper ↗

See where this research leads.

2,589 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

Recent advances and applications of machine learning in solid-state materials scienceOriginalMachine learning and artificial neural network accelerated computational discoveries in materials science151Machine learning guided appraisal and exploration of phase design for high entropy alloys327High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane178Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials187Graph convolutional neural networks with global attention for improved materials property prediction257Machine learning for quantum matter149Machine-enabled inverse design of inorganic solid materials: promises and challenges190Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures221Machine-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 perspective126A Critical Review of Machine Learning of Energy Materials606Machine learning for interatomic potential models363High-entropy ceramics2048Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys213An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts135Opportunities and Challenges for Machine Learning in Materials Science523Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials159Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses127Unsupervised Machine Learning and Band Topology159Deep learning analysis on microscopic imaging in materials science195Big-Data Science in Porous Materials: Materials Genomics and Machine Learning5992020 roadmap on solid-state batteries141Machine learning assisted materials design and discovery for rechargeable batteries466Emerging Multifunctional Single-Atom Catalysts/Nanozymes223A critical examination of compound stability predictions from machine-learned formation energies268Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning341Generative Adversarial Networks for Crystal Structure Prediction265Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts374Organic Bioelectronics: From Functional Materials to Next‐Generation Devices and Power Sources175Cavity quantum electrodynamics with color centers in diamond140Searching for high entropy alloys: A machine learning approach215Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning180Zeolite (In)Stability under Aqueous or Steaming Conditions156Computational thermodynamics and its applications173Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends147Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm387Pathways and challenges of the application of artificial intelligence to geohazards modelling188M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines220A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys148Solid state chemistry for developing better metal-ion batteries259Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering182Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation226Emerging materials intelligence ecosystems propelled by machine learning441Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset124The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design541Machine learning for landslides prevention: a survey207A critical examination of compound stability predictions from machine-learned formation energies194Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm246The 2021 battery technology roadmap269Machine learning-based prediction of phases in high-entropy alloys149Accelerating materials discovery using machine learning238Polymer informatics: Current status and critical next steps272Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications131Performance-Based Screening of Porous Materials for Carbon Capture277Toward autonomous design and synthesis of novel inorganic materials159Mechanical behavior of high-entropy alloys1321Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study133Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging174Applying machine learning approach in recycling129Machine learning for materials design and discovery128Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine165Machine learning for material characterization with an application for predicting mechanical properties139Machine learning in materials science: From explainable predictions to autonomous design281Polarons in materials665Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)143Phase transitions in 2D materials479Machine Learning Boosts the Design and Discovery of Nanomaterials138Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms205Open Catalyst 2020 (OC20) Dataset and Community Challenges765Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures152Nucleation and growth in solution synthesis of nanostructures – From fundamentals to advanced applications400Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review207Benchmarking graph neural networks for materials chemistry332Recent progress of the Computational 2D Materials Database (C2DB)575Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials144A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease170Disrupting 3D printing of medicines with machine learning122Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion264Machine Learning Force Fields: Recent Advances and Remaining Challenges127Machine learning for alloys698First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials350Autonomous experimentation systems for materials development: A community perspective398Ab Initio Machine Learning in Chemical Compound Space195Gaussian Process Regression for Materials and Molecules1221ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science153Accelerating materials discovery with Bayesian optimization and graph deep learning142Evaluation framework to guide implementation of AI systems into healthcare settings224Innovative Materials Science via Machine Learning194Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163The shape – morphing performance of magnetoactive soft materials185High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research140Evolution from Lead‐Based to Lead‐Free Piezoelectrics: Engineering of Lattices, Domains, Boundaries, and Defects Leading to Giant Response227Crystal graph attention networks for the prediction of stable materials153Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms186Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments244Data-Driven Methods for Accelerating Polymer Design123Intelligent metaphotonics empowered by machine learning169Machine-learning and high-throughput studies for high-entropy materials165Understanding, discovery, and synthesis of 2D materials enabled by machine learning171Rare-Earth Doping in Nanostructured Inorganic Materials1204Design high-entropy carbide ceramics from machine learning149Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design285Review of computational approaches to predict the thermodynamic stability of inorganic solids196A review of the recent progress in battery informatics178Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials134Application of machine learning for advanced material prediction and design123Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models147Artificial intelligence to bring nanomedicine to life189Recent advances and applications of deep learning methods in materials science1161EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms241Accelerating materials discovery using artificial intelligence, high performance computing and robotics462Machine learning in concrete science: applications, challenges, and best practices379QMugs, quantum mechanical properties of drug-like molecules144Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization160A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials222A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells263Machine learning for high-entropy alloys: Progress, challenges and opportunities397Explainable machine learning in materials science501Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications133Machine learning for a sustainable energy future517The Role of Machine Learning in Tribology: A Systematic Review137RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics133Predicting stress, strain and deformation fields in materials and structures with graph neural networks19714 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery148Machine learning enabled orthogonal camera goniometry for accurate and robust contact angle measurements167Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning125Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects378MAGUS: machine learning and graph theory assisted universal structure searcher140Theory of overparametrization in quantum neural networks176A review of machine learning methods applied to structural dynamics and vibroacoustic149Application of Machine Learning in Material Synthesis and Property Prediction166Advancements in Microwave Absorption Motivated by Interdisciplinary Research310Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review183Scope of machine learning in materials research—A review230Essay: Where Can Quantum Geometry Lead Us?184Revolutionizing the structural design and determination of covalent–organic frameworks: principles, methods, and techniques155Designing organic mixed conductors for electrochemical transistor applications188GPAW: An open Python package for electronic structure calculations287Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites170Machine Learning Aided Design and Optimization of Thermal Metamaterials195Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions405Advanced Computational Methods for Modeling, Prediction and Optimization—A Review132A generative model for inorganic materials design498Machine Learning in Polymer Research167Artificial Intelligence in Gas Sensing: A Review130

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

2589 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. 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 ↗ ·

  2. Transferable and extensible machine learning derived atomic charges for modeling metal-organic frameworks

    arXiv (Cornell University) · 2019-05-28

    Vadim Korolev, Artem A. Mitrofanov, Ekaterina I. Marchenko, Nickolay N. Eremin, Valery Tkachenko, Stepan Nikolaevich Kalmykov

    Read original paper ↗ ·

  3. Database of novel magnetic materials for high-performance permanent magnet development

    Computational Materials Science · 2019-06-19

    P. Nieves, S. Arapan, Jesús Maudes, Raúl Marticorena Sánchez, N.L. Del Brío, Alexander Kovacs, Cristina Echevarría–Bonet, Daniel Salazar, Jürgen Weischenberg, Hongbin Zhang, Olga Yu. Vekilova, Roberto Serrano-López, Jose Manuel Barandiaran, Konstantin P. Skokov, Oliver Gutfleisch, Olle Eriksson, Heike C. Herper, T. Schrefl, Santiago Cuesta‐López

    Read original paper ↗ ·

  4. Toward Interpretable Machine Learning Models for Materials Discovery

    Advanced Intelligent Systems · 2019-09-27

    Paulius Mikulskis, Morgan R. Alexander, David Alan Winkler

    Read original paper ↗ ·

  5. Machine Learning the Physical Nonlocal Exchange–Correlation Functional of Density-Functional Theory

    The Journal of Physical Chemistry Letters · 2019-10-09

    Jonathan Schmidt, Carlos L. Benavides-Riveros, Miguel A. L. Marques

    Read original paper ↗ ·

  6. A coarse-grained deep neural network model for liquid water

    Applied Physics Letters · 2019-11-04

    Tarak K Patra, Troy D. Loeffler, Henry Chan, Mathew J. Cherukara, Badri Narayanan, Subramanian K. R. S. Sankaranarayanan

    Read original paper ↗ ·

  7. Machine learning and artificial neural network accelerated computational discoveries in materials science

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2019-11-20

    Yang Hong, Bo Hou, Hengle Jiang, Jingchao Zhang

    Read original paper ↗ ·

  8. Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

    Minerals · 2019-12-20

    Anna Anglisano, Lluı̀s Casas, Marc Anglisano, I. Queralt

    Read original paper ↗ ·

  9. Machine learning guided appraisal and exploration of phase design for high entropy alloys

    npj Computational Materials · 2019-12-20

    Ziqing Zhou, Yeju Zhou, Quanfeng He, Zhaoyi Ding, Fucheng Li, Yong Gang Yang

    Read original paper ↗ ·

  10. High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

    ACS Catalysis · 2019-12-25

    Thanh Nhat Nguyen, Thuy Phuong Nhat Tran, Ken Takimoto, Ashutosh Thakur, Shun Nishimura, Junya Ohyama, Itsuki Miyazato, Lauren Takahashi, Jun Fujima, KEISUKE G. TAKAHASHI, Toshiaki Taniike

    Read original paper ↗ ·

  11. Cyber-Victimization Trends in Trinidad & Tobago: The Results of an Empirical Research

    International Journal of Information Security and Cybercrime · 2019-12-27

    Nikolaos Stamatakis, Troy SMITH

    Read original paper ↗ ·

  12. A library of ab initio Raman spectra for automated identification of 2D materials

    RePEc: Research Papers in Economics · 2020-01-01

    Alireza Taghizadeh, Ulrik Leffers, Thomas Garm Pedersen, Kristian Sommer Thygesen

    Read original paper ↗ ·

  13. Accelerating copolymer inverse design using monte carlo tree search

    Nanoscale · 2020-01-01

    Tarak K. Patra, Troy D. Loeffler, Subramanian K. R. S. Sankaranarayanan

    Read original paper ↗ ·

  14. Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study

    RSC Advances · 2020-01-01

    Alireza Baghban, Sajjad Habibzadeh, Farzin Zokaee Ashtiani

    Read original paper ↗ ·

  15. Efficient sampling of high-energy states by machine learning force fields

    Physical Chemistry Chemical Physics · 2020-01-01

    Wojciech Płaziński, Anita Płazińska, Agnieszka Brzyska

    Read original paper ↗ ·

  16. Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials

    LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2020-01-01

    Bohayra Mortazavi, Ivan Sergeyevich Novikov, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Alexander V. Shapeev, Xiaoying Zhuang

    Read original paper ↗ ·

  17. Genetic Algorithm Based Optimized Feature Engineering and Hybrid Machine Learning for Effective Energy Consumption Prediction

    IEEE Access · 2020-01-01

    Prince Waqas Khan, Yung-Cheol Byun

    Read original paper ↗ ·

  18. Graph convolutional neural networks with global attention for improved materials property prediction

    Physical Chemistry Chemical Physics · 2020-01-01

    Steph-Yves Louis, Yong Zhao, Alireza Nasiri, Xiran Wang, Yuqi Song, Fei Liu, Jianjun Hu

    Read original paper ↗ ·

  19. Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks

    IEEE Access · 2020-01-01

    Pablo Ribalta Lorenzo, Łukasz Tulczyjew, Michal Marcinkiewicz, Jakub Nalepa

    Read original paper ↗ ·

  20. Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study

    Physical Chemistry Chemical Physics · 2020-01-01

    Min-Hsuan Lee

    Read original paper ↗ ·

  21. Insights into one-body density matrices using deep learning

    Faraday Discussions · 2020-01-01

    Jack Wetherell, Andrea Costamagna, Matteo Gatti, Lucia Reining

    Read original paper ↗ ·

  22. Large Scale Integrated Materials Modeling Programs

    Elsevier eBooks · 2020-01-01

    L. Malerba

    Read original paper ↗ ·

  23. Machine learning approach for materials technologies

    Elsevier eBooks · 2020-01-01

    Jatindra Kumar Dash, Mohit Sharma, Goutam Kumar Dalapati

    Read original paper ↗ ·

  24. Machine learning for quantum matter

    Advances in Physics X · 2020-01-01

    Juan Carrasquilla

    Read original paper ↗ ·

  25. Machine-enabled inverse design of inorganic solid materials: promises and challenges

    Chemical Science · 2020-01-01

    Juhwan Noh, Geun Ho Gu, Sungwon Kim, Yousung Jung

    Read original paper ↗ ·

  26. Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

    Materials Horizons · 2020-01-01

    Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev

    Read original paper ↗ ·

  27. Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

    LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2020-01-01

    Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev

    Read original paper ↗ ·

  28. Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials

    CINECA IRIS Institutial Research Information System (University of Genoa) · 2020-01-01

    Andrea Bacigalupo, Giorgio Gnecco, Marco Lepidi, Luigi Gambarotta

    Read original paper ↗ ·

  29. Machine-learning-assisted search for functional materials over extended chemical space

    Materials Horizons · 2020-01-01

    Vadim Korolev, Artem A. Mitrofanov, Artem A. Eliseev, Valery Tkachenko

    Read original paper ↗ ·

  30. Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants

    Latin American Journal of Solids and Structures · 2020-01-01

    Yan Zhang, Ning Wang, Jingyi Li, Mohsen Mesbah, Kuan Yew Wong, Alireza Fallahpour, Bahman Nasiri‐Tabrizi, Jiangfei Yang

    Read original paper ↗ ·

  31. Nanoengineering in biomedicine: Current development and future perspectives

    Nanotechnology Reviews · 2020-01-01

    Wei Jian, David Hui, Denvid Lau

    Read original paper ↗ ·

  32. Opportunities for nanotechnology to enhance electrochemical treatment of pollutants in potable water and industrial wastewater – a perspective

    Environmental Science Nano · 2020-01-01

    Sergi Garcia‐Segura, Xiaolei Qu, Pedro J. J. Alvarez, Brian P. Chaplin, Wei Chen, John C. Crittenden, Yujie Feng, Guandao Gao, Zhen He, Chia‐Hung Hou, Xiao Hu, Guibin Jiang, Jae‐Hong Kim, Jiansheng Li, Qilin Li, Jie Ma, Jinxing Ma, Alec Brockway Nienhauser, Junfeng Niu, Bingcai Pan, Xie Quan, Filippo Ronzani, D. Villagrán, T. David Waite, W. Shane Walker, Can Wang, Michael S. Wong, Paul K. Westerhoff

    Read original paper ↗ ·

  33. Smart crossover mechanism for parallel neuroevolution method of medical diagnostic models synthesis

    Unknown journal · 2020-01-01

    Serhii Leoshchenko, С. А. Субботин, Аndrii Oliinyk, Viktor Lytvyn, Matviy Ilyashenko

    Read original paper ↗ ·

  34. Two-dimensional organic–inorganic superlattice-like heterostructures for energy storage applications

    Energy & Environmental Science · 2020-01-01

    Pan Xiong, Yunyan Wu, Yifan Liu, Renzhi Ma, Takayoshi Sasaki, Xin Wang, Junwu Zhu

    Read original paper ↗ ·

  35. Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining

    Chemical Science · 2020-01-01

    Thomas C. Nicholas, Andrew L. Goodwin, Volker L. Deringer

    Read original paper ↗ ·

  36. Topological photonic crystals: a review

    Frontiers of Optoelectronics · 2020-01-07

    Hongfei Wang, Samit Kumar Gupta, Biye Xie, Ming‐Hui Lu

    Read original paper ↗ ·

  37. Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation

    Microprocessors and Microsystems · 2020-01-07

    Jakub Nalepa, Marek Antoniak, Michał Myller, Pablo Ribalta Lorenzo, Michal Marcinkiewicz

    Read original paper ↗ ·

  38. A Critical Review of Machine Learning of Energy Materials

    Advanced Energy Materials · 2020-01-29

    Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong

    Read original paper ↗ ·

  39. Combining Quantum Mechanics and Machine-Learning Calculations for Anharmonic Corrections to Vibrational Frequencies

    Journal of Chemical Theory and Computation · 2020-01-31

    Julien Lam, Saleh Abdul-Al, Abdul-Rahman Allouche

    Read original paper ↗ ·

  40. Materials informatics approach to understand aluminum alloys

    Science and Technology of Advanced Materials · 2020-01-31

    Ryo Tamura, Makoto Watanabe, Hiroaki Mamiya, Kota Washio, Masao Yano, Katsunori Danno, Akira Kato, Tetsuya Shoji

    Read original paper ↗ ·

  41. MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films

    Journal of Chemical Theory and Computation · 2020-01-31

    Andrew Z. Summers, Justin Gilmer, Christopher R. Iacovella, Peter T. Cummings, Clare McCabe

    Read original paper ↗ ·

  42. Neue Materialien vorhersagen: Maschinelles Lernen als Werkzeug

    Nachrichten aus der Chemie · 2020-02-01

    Janine George, Markus Suta

    Read original paper ↗ ·

  43. Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially Resolved Multi‐Modal Optoelectronic Spectroscopy

    Advanced Materials · 2020-02-03

    Yida Lin, Tina Gao, Xiaoyun Pan, Maria Kamenetska, Susanna M. Thon

    Read original paper ↗ ·

  44. Machine learning for interatomic potential models

    The Journal of Chemical Physics · 2020-02-05

    Tim Mueller, Alberto Hernández, Chuhong Wang

    Read original paper ↗ ·

  45. High-entropy ceramics

    Nature Reviews Materials · 2020-02-12

    Corey Oses, Cormac Toher, Stefano Curtarolo A

    Read original paper ↗ ·

  46. The Limited Predictive Power of the Pauling Rules

    Angewandte Chemie International Edition · 2020-02-17

    Janine George, David Waroquiers, Davide Di Stefano, Guido Petretto, Gian‐Marco Rignanese, Geoffroy Hautier

    Read original paper ↗ ·

  47. The Limited Predictive Power of the Pauling Rules

    Angewandte Chemie · 2020-02-17

    Janine George, David Waroquiers, Davide Di Stefano, Guido Petretto, Gian‐Marco Rignanese, Geoffroy Hautier

    Read original paper ↗ ·

  48. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys

    Computational Materials Science · 2020-02-24

    Dongbo Dai, Tao Xu, Xiao Wei, Guangtai Ding, Xu Yan, Jincang Zhang, Huiran Zhang

    Read original paper ↗ ·

  49. Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

    Journal of Physics Materials · 2020-03-04

    Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan Sergeyevich Novikov, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev

    Read original paper ↗ ·

  50. Metastable Group IV Allotropes and Solid Solutions: Nanoparticles and Nanowires

    Chemistry of Materials · 2020-03-16

    Sven Barth, Michael Stanislaus Seifner, Stephen Maldonado

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