iamrp.net-KDRr-1790527237-c294355934 1790527237 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2025 4 1 Daniel Fischer Laura Meier Thomas Braun Stefan Koch 2025 10.68159/c294355934 https://iamrp.net/pub/journal/1/article/c294355934 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. Ward L, et al. Machine learning for materials science: Recent progress and future opportunities. Annu Rev Mater Res. 2021;51:125-51. Butler KT, et al. Machine learning for molecular and materials science. Nature. 2020;582(7811):193-8. Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: status, challenges, and perspectives. Adv Sci. 2020;7(22):1900808. Chen CT, et al. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2022;2(11):718-728. Batra R, et al. Active learning for accelerated design of high-entropy alloys. npj Comput Mater. 2021;7(1):1-10. Gao H, et al. Bayesian optimization for materials discovery. Matter. 2022;5(11):3631-52. Tran K, et al. Uncertainty Prediction for Machine Learning Models of Material Properties. ACS Omega. 2021;6(33):21419-30. Musil F, et al. Uncertainty quantification in machine learning interatomic potentials. npj Comput Mater. 2021;7(1):1-12. Morgan D, et al. Uncertainty quantification in machine-learned interatomic potentials. Annu Rev Mater Res. 2023;53:1-25. Psaros AF, et al. Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. J Comput Phys. 2023;466:111412. Pernot P. Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity. arXiv (journal version in AIP Adv or similar 2023 analogs). 2023. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2021;7(1):1-17. Zhang Y, et al. Physics-informed neural networks for uncertainty quantification in materials modeling. J Comput Phys. 2023;472:111678. Wang R, Wu D, Li G, Liu Z, Tong J, Chen X, et al. Machine learning aided uncertainty quantification for engineering structures. Comput Methods Appl Mech Eng. 2022;392:114678. Oviedo F, et al. Bayesian neural networks for uncertainty quantification in data-driven materials modeling. Comput Methods Appl Mech Eng. 2021;386:114079. Liu Y, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun. 2021;12(1):6512. Zhang Y, et al. Physics-informed machine learning for materials science. Comput Mater Sci. 2022;208:111345. Palmer G, et al. Calibration after bootstrap for accurate uncertainty quantification in regression models. npj Comput Mater. 2022;8(1):9. Chen C, et al. Uncertainty quantification for machine learning models of molecular properties. J Chem Inf Model. 2023;63(5):1425-37. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Mater. 2022;7(3):175-84. Wang X, et al. A universal similarity based approach for predictive uncertainty quantification in materials science. Sci Rep. 2022;12(1):15800. Jablonka KM, et al. Uncertainty-aware mixed-variable machine learning for materials design. Sci Rep. 2022;12(1):23431. Aouichaoui ARN, et al. Uncertainty estimation in deep learning-based property models: Graph neural networks applied to the critical properties. AIChE J. 2022;68(10):e17696. Jha D, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nat Commun. 2021;12(1):1-13. Xie T, et al. Uncertainty-aware machine learning for high-throughput screening of metal-organic frameworks. ACS Appl Mater Interfaces. 2022;14(12):14412-23. Smith JS, Nebgen B, Mathew N, Chen J, Lubbers N, Burakovsky L, et al. Automated discovery of a robust interatomic potential for aluminum. Nat Commun. 2021;12(1):1257. Pilania G, et al. Machine learning for materials design and discovery. J Mater Sci. 2022;57(45):1-28. Schweidtmann AM, et al. Machine learning meets continuous flow chemistry: Automated optimization for the synthesis of pharmaceuticals. Chem Eng J. 2020;399:125934. Schmidt J, et al. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2020;6(1):1-14. Xie Y, et al. Uncertainty quantification in deep learning-based property prediction of polymers. Macromolecules. 2023;56(4):1456-67. Rupp M, et al. Machine learning for quantum mechanical properties of atoms in molecules. J Chem Theory Comput. 2021;17(1):1-10. Janet JP, et al. Machine learning for high-throughput experimental materials discovery. Acc Chem Res. 2021;54(3):687-97. Wang AY, et al. Uncertainty quantification for machine learning potentials. J Chem Phys. 2023;158(12):124109. Fung V, et al. Machine learning for materials discovery: Recent advances and future directions. Chem Rev. 2023;123(12):7851-90. Saal JE, et al. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database. JOM. 2021;73(11):3395-406.