BetaCitation Atlas

RESEARCH PAPER · Machine Learning Science and Technology · 2020

Scientific AI in materials science: a path to a sustainable and scalable paradigm

Brian DeCost, Jason R. Hattrick-Simpers, Zachary Trautt, Aaron Gilad Kusne, Eva M. Campo, ML Green

Read original paper ↗

See where this research leads.

120 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

Scientific AI in materials science: a path to a sustainable and scalable paradigmOriginalArtificial Intelligence in Materials Science5Machine-Learning Interatomic Potentials for Materials Science397Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids1Materials data :1Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion19Materials Representation and Transfer Learning for Multi-Property Prediction1Materials representation and transfer learning for multi-property prediction71Nobel Turing Challenge: creating the engine for scientific discovery140Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials144Catalyze Materials Science with Machine Learning67Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics43Autonomous experimentation systems for materials development: A community perspective398Discovery of complex oxides via automated experiments and data science37CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry0Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23Data-Driven Methods for Accelerating Polymer Design123Compositionally graded crystals as a revived approach for new crystal engineering for the exploration of novel functionalities8Failure, Uncertainty, and Risk in Materials AI — How Negative Outcomes Are Handled Across the Literature: A Review Study0Machine learning guided study of composition-coefficient of thermal expansion relationship in oxide glasses using a sparse dataset4Machine-learning and high-throughput studies for high-entropy materials165Synthetic Data as Scientific Intervention: A Conceptual Framework for Materials AI0The Coordination Problem in Multi-Model Materials AI Pipelines0Toward an Autonomous Workflow for Single Crystal Neutron Diffraction1Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process52CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry31The materials tetrahedron has a “digital twin”34Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping10Knowledge formalization and recommendation in computational design synthesis for 4D printing0An end-to-end computer vision methodology for quantitative metallography30Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes7On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning55AI-Driven Solutions for Energy Optimization and Environmental Conservation in Digital Business Environments14Leveraging Theory for Enhanced Machine Learning24The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation19Accelerating the adoption of research data management strategies16Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules13A knowledge recommendation approach in design for multi-material 4D printing based on semantic similarity vector space model and case-based reasoning22Conceptual Foundations for Value-Sensitive Design of Materials AI Systems0Representation Compression and Scientific Loss in Materials AI0Scientific Blind Spots in Materials AI—A Systematic Conceptual Mapping: A Review Study0Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations7Hybrid interatomic potential for Sn23A critical examination of robustness and generalizability of machine learning prediction of materials properties146Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products9Accelerated and interpretable prediction of local properties in composites3Exploiting redundancy in large materials datasets for efficient machine learning with less data118Size-Dependent Elastic Properties of Nanofilms: Exponentially Decreased Surface Elasticity Model1QTN-VQC: an end-to-end learning framework for quantum neural networks37A Conceptual Framework for Trustworthy AI Decisions Across the Materials Design Lifecycle1Conceptual Models of Trust in Materials AI — Frameworks, Dimensions, and Open Questions0Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys0Knowledge Graphs vs Property Predictors: Competing Infrastructures of Materials Intelligence0Overview of Applications of Artificial Intelligence (AI) in Diverse Fields9The Scaffolding Problem: How Materials AI Depends on Unstated Scientific Infrastructure0What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing0Leveraging our Teacher’s Experience to Improve Machine Learning: Application to pKa Prediction1Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework21Graph Similarity-Based Data Management Platform for Advancing Material Informatics0PROCESS DEVELOPMENT IN MECHANICAL ENGINEERING: INNOVATIONS, CHALLENGES, AND OPPORTUNITIES18Glass waste circular economy - Advancing to high-value glass sheets recovery using industry 4.0 and 5.0 technologies76Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy2Illustrating an Effective Workflow for Accelerated Materials Discovery14Research integrity in the era of artificial intelligence: Challenges and responses107One-Dimensional and Two-Dimensional Nanomaterials for Sensor Applications5Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories42Machine learning-assisted design of high-entropy alloys with superior mechanical properties51Learnability of state spaces of physical systems is undecidable0A DFT exploration of the stabilities, physical properties, and tensile strength of new synthesized Nb2AC (A=Ni and Co) MAX phases12Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies30AI for Property Modeling, Solvent Tailoring, and Process Design0Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery9Transformer-Driven Inverse Learning for AI-Powered Ceramic Material Innovation With Advanced Data Preprocessing3A Conceptual Framework for Anticipating Second-Order Effects of Materials AI Deployment0Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement9AI in material science and metallurgy1Conceptual Foundations for Scientific Audit Trails in Materials AI Systems0Current advances in data-driven modeling and computational tools for novel materials0Governance and Responsible Use of Materials AI — Standards, Transparency, and Risk Management0Scientific Risk in Autonomous Materials Discovery: A Conceptual Framework for Anticipating Unsafe Recommendations1The Hidden Cost of Acceleration: A Conceptual Analysis of Time Compression in AI-Driven Materials Research0The Problem of Representational Harm in Materials Dataset Construction0The Use of Artificial Intelligence (AI) in Nanotechnology: A Systematic Review0Towards Data-Driven Constitutive Models for Engineering Applications: A Transfer Learning-Assisted Physical-Encoded Neural Network0Transforming Carbon-Based Material: The Role of AI and ML Regression Techniques in Material Science0Accelerating materials recipe acquisition via LLM-mediated reinforcement learning2Prediction of Biomass Chemical Looping Gasification Performance Using the Extra Tree Ensemble Model3Data Science and AI for Sustainable Futures: Opportunities and Challenges5Effect of Mutations on Smlt1473 Binding to Various Substrates Using Molecular Dynamics Simulations1Automated image segmentation for accelerated nanoparticle characterization5Numerical modeling of flexoelectricity: Status, opportunities and challenges0Smart-responsive chitosan-based materials for precise degradation control and environmental remediation: A review6Synthesis of Two-Dimensional Metal Nanomaterials for Electrocatalytic Multielectron Transfer Reactions6Enhancing textile industry quality monitoring: integrating ChatGPT and OCT for advanced AI-driven solutions2Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Explainable machine learning-guided design of high-performance thermoelectric materials5Artificial intelligence applications in mission-critical domains0Artificial intelligence ethical and societal impacts0Artificial intelligence integration with cloud platforms: A focus on Azure, AWS, and GCP AI ecosystems0Governance and compliance frameworks for responsible artificial intelligence deployment0MLOps and lifecycle management0Inteligencia artificial en la investigación científica: Avances, desafíos y futuro0DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction1Tooling Artificial Intelligence in Polymeric Nanofillers: A Review0AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects16Machine learning approaches for creep rupture life prediction of metallic materials: A comprehensive review11The critical role of artificial intelligence in biomaterials design and development0Conceptual Foundations for Scientific Falsifiability of AI-Generated Materials Claims0Perspective: Why Materials AI Needs More Failure Reporting — A Position on Negative Results as Community Infrastructure0Physics-Integrated Machine Learning for Materials Science — Conceptual Taxonomies and Open Questions0The Problem of Scientific Consensus in AI-Driven Materials Science—Conceptual Approaches: A Review Study0Artificial Intelligence as the Next Visionary in Liquid Crystal Research3The US university-industry link in the R&D of AI: Back to the origins?4Development of a pKa predictor (pKaLearn) by leveraging teaching experience to improve machine learning1Verification and validation for trustworthy scientific machine learning2Artificial intelligence shaping the future of hydrogen technologies6Machine learning screening of magnetocaloric high-entropy alloys using a field-normalized ΔSref target0Chasing the Rabbit: A Systematic Exploration of Software-Defined HPC Storage0Accelerating Computational Material Discovery and Learning: First Experiences with MatterSim0Managing autonomous materials labs with multi-agent AI and its implications for the science of science1Cognitive Materials Engineering for Bioelectricity and Bioelectronics: Research and Applications0

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

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

  1. Artificial Intelligence in Materials Science

    International Journal of Advances in Scientific Research and Engineering · 2021-01-01

    Matthew N. O. Sadiku, Guddi K. Suman, Sarhan M. Musa

    Read original paper ↗ ·

  2. Machine-Learning Interatomic Potentials for Materials Science

    Acta Materialia · 2021-01-01

    Yuri M. Mishin

    Read original paper ↗ ·

  3. Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids

    arXiv (Cornell University) · 2021-03-16

    Jaber Rezaei Mianroodi, Nima Hamidi Siboni, Dierk Raabe

    Read original paper ↗ ·

  4. Materials data :

    Unknown journal · 2021-04-01

    Debra L. Kaiser, R. J. Hanisch, James A. Warren, Zachary Trautt

    Read original paper ↗ ·

  5. Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

    Journal of Chemical Information and Modeling · 2021-04-02

    Yuanqing Tang, Zhi Li, Mansoor Ani Najeeb, Hamed Eramian, Emory M. Chan, Alexander J. Norquist, D. Frank Hsu, Joshua Schrier

    Read original paper ↗ ·

  6. Materials Representation and Transfer Learning for Multi-Property Prediction

    ChemRxiv · 2021-05-24

    Shufeng Kong, Dan Guevarra, Carla Pedro Gomes, John M. Gregoire

    Read original paper ↗ ·

  7. Materials representation and transfer learning for multi-property prediction

    Applied Physics Reviews · 2021-06-01

    Shufeng Kong, Dan Guevarra, Carla Pedro Gomes, John M. Gregoire

    Read original paper ↗ ·

  8. Nobel Turing Challenge: creating the engine for scientific discovery

    npj Systems Biology and Applications · 2021-06-18

    Hiroaki Kitano

    Read original paper ↗ ·

  9. Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials

    npj Computational Materials · 2021-07-01

    Jaber Rezaei Mianroodi, Nima Hamidi Siboni, Dierk Raabe

    Read original paper ↗ ·

  10. Catalyze Materials Science with Machine Learning

    ACS Materials Letters · 2021-07-02

    Jaehyun Kim, Donghoon Kang, Sang‐Bum Kim, Ho Won Jang

    Read original paper ↗ ·

  11. Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

    ACS Nano · 2021-07-06

    Rama Krishnan Vasudevan, Kyle P. Kelley, Jacob D Hinkle, Hiroshi Funakubo, Stephen Jesse, Sergei V. Kalinin, Maxim A Ziatdinov

    Read original paper ↗ ·

  12. Autonomous experimentation systems for materials development: A community perspective

    Matter · 2021-07-26

    Eric A. Stach, Brian DeCost, Aaron Gilad Kusne, Jason R. Hattrick-Simpers, Keith A. Brown, Kristofer G. Reyes, Joshua Schrier, Simon J. L. Billinge, Tonio Buonassisi, Ian T Foster, Carla Pedro Gomes, John M. Gregoire, Apurva Mehta, Joseph H. Montoya, Elsa Olivetti, Chiwoo Park, Eli Rotenberg, Semion K. Saikin, Sylvia Smullin, Valentin Stanev, Benji Maruyama

    Read original paper ↗ ·

  13. Discovery of complex oxides via automated experiments and data science

    Proceedings of the National Academy of Sciences · 2021-09-10

    Lusann Yang, Joel A. Haber, Zan Armstrong, Samuel Yang, Kevin Kan, Lan Zhou, Matthias Hermann Richter, C. Roat, Nicholas Wagner, Marc Coram, Marc Berndl, Patrick Riley, John M. Gregoire

    Read original paper ↗ ·

  14. CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry

    Unknown journal · 2021-10-21

    Anthony Wang, Mahamad Salah Mahmoud, Mathias Czasny, Aleksander Gurlo

    Read original paper ↗ ·

  15. Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

    Nanomaterials · 2021-12-21

    Maria A. Butakova, Andrey V. Chernov, Oleg O. Kartashov, Alexander V. Soldatov

    Read original paper ↗ ·

  16. Data-Driven Methods for Accelerating Polymer Design

    ACS Polymers Au · 2021-12-28

    Tarak K. Patra

    Read original paper ↗ ·

  17. Compositionally graded crystals as a revived approach for new crystal engineering for the exploration of novel functionalities

    CrystEngComm · 2022-01-01

    Yuji Matsumoto, Shingo Maruyama, Kenichi Kaminaga

    Read original paper ↗ ·

  18. Failure, Uncertainty, and Risk in Materials AI — How Negative Outcomes Are Handled Across the Literature: A Review Study

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Ravi Menon, Arjun Nair, Meera Pillai, Suresh Varma

    Read original paper ↗ ·

  19. Machine learning guided study of composition-coefficient of thermal expansion relationship in oxide glasses using a sparse dataset

    Materials Today Proceedings · 2022-01-01

    Biranchi M. Tripathi, Amit Sinha, Tarasankar Mahata

    Read original paper ↗ ·

  20. Machine-learning and high-throughput studies for high-entropy materials

    Materials Science and Engineering R Reports · 2022-01-01

    E‐Wen Huang, Wen‐Jay Lee, Sudhanshu Shekhar Singh, Poresh Kumar, Chih‐Yu Lee, Tu‐Ngoc Lam, Hsu-Hsuan Chin, Bi‐Hsuan Lin, Peter Kaehuei Liaw

    Read original paper ↗ ·

  21. Synthetic Data as Scientific Intervention: A Conceptual Framework for Materials AI

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Ravi Kumar, Neha Sharma, Aniket Deshmukh, Arjun Nair

    Read original paper ↗ ·

  22. The Coordination Problem in Multi-Model Materials AI Pipelines

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Li Zhang, Wei Chen

    Read original paper ↗ ·

  23. Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

    Communications in computer and information science · 2022-01-01

    Junqi Yin, Guannan Zhang, Huibo B. Cao, Sajal Dash, Bryan C. Chakoumakos, Feiyi Wang

    Read original paper ↗ ·

  24. Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

    Machine Learning Science and Technology · 2022-01-14

    Maxim A Ziatdinov, Ayana Ghosh, Sergei V. Kalinin

    Read original paper ↗ ·

  25. CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry

    Integrating materials and manufacturing innovation · 2022-01-17

    Anthony Wang, Mahamad Salah Mahmoud, Mathias Czasny, Aleksander Gurlo

    Read original paper ↗ ·

  26. The materials tetrahedron has a “digital twin”

    MRS Bulletin · 2022-02-01

    Michael E. Deagen, L. Catherine Brinson, Richard A. Vaia, Linda S. Schadler

    Read original paper ↗ ·

  27. Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

    Frontiers in Physics · 2022-02-16

    Aaron Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo A, Apurva Mehta, Ichiro Takeuchi

    Read original paper ↗ ·

  28. Knowledge formalization and recommendation in computational design synthesis for 4D printing

    Unknown journal · 2022-03-04

    Saoussen Dimassi

    Read original paper ↗ ·

  29. An end-to-end computer vision methodology for quantitative metallography

    Scientific Reports · 2022-03-21

    Matan Rusanovsky, Ofer Beeri, Gal Oren

    Read original paper ↗ ·

  30. Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

    npj Computational Materials · 2022-04-05

    Dan Guevarra, Lan Zhou, Matthias Hermann Richter, Aniketa Shinde, Di Chen, Carla Pedro Gomes, John M. Gregoire

    Read original paper ↗ ·

  31. On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

    Applied Physics Reviews · 2022-04-29

    Austin McDannald, Matthias Frontzek, Andrei T. Savici, Mathieu Doucet, Efrain E. Rodriguez, Kate Meuse, Jessica Opsahl‐Ong, Daniel V. Samarov, Ichiro Takeuchi, William Ratcliff, Aaron Gilad Kusne

    Read original paper ↗ ·

  32. AI-Driven Solutions for Energy Optimization and Environmental Conservation in Digital Business Environments

    Asia Pacific Journal of Energy and Environment · 2022-06-30

    Aleena Varghese

    Read original paper ↗ ·

  33. Leveraging Theory for Enhanced Machine Learning

    ACS Macro Letters · 2022-08-26

    Debra J. Audus, Austin McDannald, Brian DeCost

    Read original paper ↗ ·

  34. The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation

    MRS Bulletin · 2022-09-01

    Logan Saar, Haotong Liang, Alex Y. Wang, Austin McDannald, Efrain E. Rodriguez, Ichiro Takeuchi, Aaron Gilad Kusne

    Read original paper ↗ ·

  35. Accelerating the adoption of research data management strategies

    Matter · 2022-11-01

    Johanne Medina, Abdul Wahab Ziaullah, Heesoo Park, Ivano E. Castelli, Arif Shaon, Halima Bensmail, Fedwa El‐Mellouhi

    Read original paper ↗ ·

  36. Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules

    Advanced Materials · 2022-11-14

    Kyeong‐Jun Jeong, Seungwon Jeong, Sangmin Lee, Chang Yun Son

    Read original paper ↗ ·

  37. A knowledge recommendation approach in design for multi-material 4D printing based on semantic similarity vector space model and case-based reasoning

    Computers in Industry · 2022-11-23

    Saoussen Dimassi, Frédéric Demoly, Hadrien Belkebir, Christophe Cruz, Kyoung-Yun Joseph Kim, Samuel Gomes, Hang Jerry Qi, Jean–Claude André

    Read original paper ↗ ·

  38. Conceptual Foundations for Value-Sensitive Design of Materials AI Systems

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Claire Dupont, Julien Martin

    Read original paper ↗ ·

  39. Representation Compression and Scientific Loss in Materials AI

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Claire Dupont, J. Martin

    Read original paper ↗ ·

  40. Scientific Blind Spots in Materials AI—A Systematic Conceptual Mapping: A Review Study

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    James Walker, Olivia Harris

    Read original paper ↗ ·

  41. Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

    Journal of Materials Informatics · 2023-01-01

    Max Dreger, Kourosh Malek, Mohammed J. Eslamibidgoli, Michael Hermann Eikerling

    Read original paper ↗ ·

  42. Hybrid interatomic potential for Sn

    Physical Review Materials · 2023-04-03

    Mashroor S. Nitol, Khanh Dang, Saryu Jindal Fensin, Michael I. Baskes, Doyl Dickel, Christopher D. Barrett

    Read original paper ↗ ·

  43. A critical examination of robustness and generalizability of machine learning prediction of materials properties

    npj Computational Materials · 2023-04-07

    Kangming Li, Brian DeCost, Kamal Kumar Choudhary, Michael Greenwood, Jason R. Hattrick-Simpers

    Read original paper ↗ ·

  44. Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

    Materials Today Communications · 2023-07-05

    Georgios Konstantopoulos, Costas A. Charitidis, Miguel Á. Bañares, Raquel Portela, Nikolaj Zangenberg, E. Capria, Marco Sebastiani, Gerhard Goldbeck, Elias P. Koumoulos, Bojan O. Boskovic, Spyros Diplas, Natalia Konchakova, Ilian T. Todorov, Donna Dykeman, Silvia Giovanna Avataneo, Marco Falzetti, Enrique Lozano Diz, J.J. Reinosa, Ekaterina Burov, D. Skrelic, Nina Jeliazkova, Bastian Barton, Giovanni Masotti, Tommaso Tamarozzi, Iván Moya Alcón

    Read original paper ↗ ·

  45. Accelerated and interpretable prediction of local properties in composites

    APL Machine Learning · 2023-09-01

    Shengtong Zhang, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley

    Read original paper ↗ ·

  46. Exploiting redundancy in large materials datasets for efficient machine learning with less data

    Nature Communications · 2023-11-10

    Kangming Li, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers

    Read original paper ↗ ·

  47. Size-Dependent Elastic Properties of Nanofilms: Exponentially Decreased Surface Elasticity Model

    Mechanics of Solids · 2023-12-01

    Jiangang Li, Xiao Lei, Huihui Xu, Zhixiang Gao, Hua Wang

    Read original paper ↗ ·

  48. QTN-VQC: an end-to-end learning framework for quantum neural networks

    Physica Scripta · 2023-12-12

    Jun Qi, Chao-Han Huck Yang, Pin‐Yu Chen

    Read original paper ↗ ·

  49. A Conceptual Framework for Trustworthy AI Decisions Across the Materials Design Lifecycle

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Wei Chen, Li Zhang

    Read original paper ↗ ·

  50. Conceptual Models of Trust in Materials AI — Frameworks, Dimensions, and Open Questions

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Victor Santos, Rafael Costa, Bruno Teixeira

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