iamrp.net-fI4D-1791561621-s350974414 1791561621 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 2024 3 1 Ravi Kumar Neha Sharma Aniket Deshmukh Arjun Nair 2024 10.68159/s350974414 https://iamrp.net/pub/journal/1/article/s350974414 Korolev V, Nevolin I, Protsenko P. A universal similarity based approach for predictive uncertainty quantification in materials science. Sci Rep. 2022;12:14931. Wen M, Tadmor EB. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Comput Mater. 2020;6:124. Zhang H, Chen W, Iyer A, Apley DW, Chen W. Uncertainty-aware mixed-variable machine learning for materials design. Sci Rep. 2022;12:19760. Tan AR, Urata S, Goldman S, Dietschreit JCB, Gómez-Bombarelli R. Single-model uncertainty quantification in neural network potentials does not always mean the same. npj Comput Mater. 2023;9:225. Tavazza F, DeCost B, Choudhary K. Uncertainty Prediction for Machine Learning Models of Material Properties. ACS Omega. 2021;6:32431-40. Janet JP, Duan C, Yang T, Nandy A, Kulik HJ. Leveraging uncertainty from deep learning for trustworthy materials discovery. ACS Omega. 2021;6:33149-58. Scalia G, Grambow CA, Pernici B, Li YP, Green WH. Uncertainty quantification using neural networks for molecular property prediction. J Chem Inf Model. 2020;60:3775-85. Heid E, McGill CJ, Vermeire FH, Green WH. Characterizing uncertainty in machine learning for chemistry. J Chem Inf Model. 2023;63:4012-29. Yin T, Panapitiya G, Coda ED, Saldanha EG. Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction. J Cheminform. 2023;15:105. Chen H, Frey NC, Bowman D, et al. Machine learning-based inverse design for electrochemically active molecules. Proc Natl Acad Sci U S A. 2022;119(32):e2206321119. Zhou G, Lubbers N, Barros K, Tretiak S, Nebgen B. Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics. Proc Natl Acad Sci U S A. 2022;119(27):e2120333119. Mansbach RA, Ferguson AL, Kilian KA, Keten S, Lequieu J, de Pablo JJ, et al. Conformal prediction under feedback covariate shift for machine learning on streaming data. Proc Natl Acad Sci U S A. 2022;119(28):e2204569119. Blei DM, Kucukelbir A, McAuliffe JD. Comparing methods for statistical inference with model uncertainty. Proc Natl Acad Sci U S A. 2021;118(10):e2120737118. Han H, Wang W-Y, Mao B-H. Automated crystal system identification from electron diffraction patterns using multimodal data fusion. Proc Natl Acad Sci U S A. 2023;120(45):e2309240120. Angelino E, Larus-Stone N, Alabi D, Seltzer M, Rudin C. Cross-prediction-powered inference. Proc Natl Acad Sci U S A. 2023;120(38):e2322083120. Tavazza F, Choudhary K, DeCost B. Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison. arXiv preprint arXiv:2310.13136. 2023. Basu K, Hao J, Hintz D, Shah D, Palmer A, Hora GS, et al. Uncertainty quantification methods for ML-based surrogate models of scientific applications. NeurIPS Workshop. 2022. Otis R. Uncertainty reduction and quantification in computational thermodynamics. Comput Mater Sci. 2022;212:111590. Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Mach Learn. 2021;110:457-506. Nemani V, Biggio L, Huan X, Hu Z, Fink O, Tran A, et al. Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial. Mech Syst Signal Process. 2023;205:110796. Wimmer L, Sale Y, Hofman P, Bischl B, Hüllermeier E. Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? PMLR. 2023;216:2282-92. Boström H. Aleatoric and epistemic uncertainty with random forests. Adv Intell Data Anal XVIII. 2020:444-56. National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST. 2023. Aristi Baquero J, Burkhardt R, Govindarajan A, Wallace T. Derisking AI by design: How to build risk management into AI development. McKinsey. 2020. Hariri-Ardebili MA, Salazar F. Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems. Soft Comput. 2020;24:11583-604. Chu K, Foster ME, Sills RB, Zhou X, Zhu T, McDowell DL. Temperature and composition dependent screw dislocation mobility in austenitic stainless steels from large-scale molecular dynamics. npj Comput Mater. 2020;6:179. Miller BK, Geiger M, Smidt TE, Noé F. Relevance of rotationally equivariant convolutions for predicting molecular properties. arXiv preprint arXiv:2008.08461. 2020. Yao S, Halpern Y, Thain N, Wang X, Lee K, Prost F, et al. Measuring Recommender System Effects with Simulated Users. arXiv preprint arXiv:2101.04526. 2021. Bellerive A, Kaminski J, Lewis PM, Colas P, Diener R, Peter Kluit P, et al. MPGDs for TPCs at future lepton colliders. arXiv preprint arXiv:2203.06267. 2022. Malik B, Kashyap AR, Kan M-Y, Poria S. UDApter --Efficient Domain Adaptation Using Adapters. arXiv preprint arXiv:2302.03194. 2023. Bechetti DH, Semple JK, Zhang W, Fisher CR. Temperature-Dependent Material Property Databases for Marine Steels—Part 1: DH36. Integr Mater Manuf Innov. 2020;9:257-86. Janet JP, Kulik HJ. Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships. J Phys Chem A. 2020;124:1850-61. Pernot P, Cailliez F. A critical review of statistical calibration methods for estimating thermochemical properties of materials. J Phys Chem A. 2021;125:6245-58.