Fetching the paper…
Reading the bibliography…
Data science and informatics tools have been proliferating recently within the computational materials science and catalysis fields.
1905
Earlier work this paper cites.
1910
Earlier work this paper cites.
Kresse, G.; Hafner, J. Ab initio molecular dynamics for liquid metals. Physical Review B 1993
1993
Earlier work this paper cites.
Kresse, G.; Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Physical Review B 1994
1994
Earlier work this paper cites.
Kresse, G.; Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Computational Materials Science 1996
1996
Earlier work this paper cites.
Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B 1996
1996
Earlier work this paper cites.
Hammer, B.; Hansen, L. B.; Nørskov, J. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals. Physical Review B 1999
1999
Earlier work this paper cites.
Gneiting, T.; Raftery, A. E. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 2007
2007
Earlier work this paper cites.
Chu, W.; Zinkevich, M.; Li, L.; Thomas, A.; Tseng, B. Unbiased online active learning in data streams. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2011
2011
Earlier work this paper cites.
Settles, B. In Synthesis Lectures on Artificial Intelligence and Machine Learning ; Brachman, R. J., Cohen, W. W., Dietterich, T. G., Eds.; Morgan & Claypool, 2012; p 100
2012
Earlier work this paper cites.
Garnett, R.; Krishnamurthy, Y.; Xiong, X.; Schneider, J.; Mann, R. Bayesian optimal active search and surveying. Proceedings of the 29th International Conference on Machine Learning, ICML 2012 2012
2012
Earlier work this paper cites.
Thompson, S. K. In Sampling , 3rd ed.; Shewhart, W. A., Wilks, S. S., Eds.; John Wiley and Sons Inc., 2012; Chapter 11, pp 139–156
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Hyndman, R. J.; Athanasopoulos, G. Forecasting: Principles and practice , 1st ed.; otexts.com, 2014
2014
Cited alongside, same era.
Dawid, A. P.; Musio, M. Theory and applications of proper scoring rules. Metron 2014
2014
Cited alongside, same era.
Peterson, A. A. Acceleration of saddle-point searches with machine learning. Journal of Chemical Physics 2016
2016
Cited alongside, same era.
Gal, Y.; Ghahramani, Z. Dropout as a Bayesian Approximation : Representing Model Uncertainty in Deep Learning. 33rd International Conference on Machine Learning. New York, NY, USA, 2016
2016
Cited alongside, same era.
Peterson, A. A.; Christensen, R.; Khorshidi, A. Addressing uncertainty in atomistic machine learning. Phys. Chem. Chem. Phys. Phys. Chem. Chem. Phys 2017
2017
Cited alongside, same era.
Tran, K.; Aini, P.; Back, S.; Ulissi, Z. W. Dynamic Workflows for Routine Materials Discovery in Surface Science. Journal of Chemical Information and Modeling 2018
2018
Later among the works it cites.
Meredig, B.; Antono, E.; Church, C.; Hutchinson, M.; Ling, J.; Paradiso, S.; Blaiszik, B.; Foster, I.; Gibbons, B.; Hattrick-Simpers, J.; Mehta, A.; Ward, L. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Molecular Systems Design and Engineering 2018
2018
Later among the works it cites.
Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical Review Letters 2018
2018
Later among the works it cites.
Bingham, E.; Chen, J. P.; Jankowiak, M.; Obermeyer, F.; Pradhan, N.; Karaletsos, T.; Singh, R.; Szerlip, P.; Horsfall, P.; Goodman, N. D. Pyro: Deep Universal Probabilistic Programming. 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hjorth Larsen, A. et al. The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter 2017
2017
Cited alongside, same era.
Lakshminarayanan, B.; Pritzel, A.; Blundell, C. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. 31st Conference on Neural Information Processing Systems (NIPS). 2017
2017
Cited alongside, same era.
Medford, A. J.; Kunz, M. R.; Ewing, S. M.; Borders, T.; Fushimi, R. Extracting Knowledge from Data through Catalysis Informatics. ACS Catalysis 2018
2018
Cited alongside, same era.
Frazier, P. I. A Tutorial on Bayesian Optimization. 2018
2018
Cited alongside, same era.
Kandasamy, K.; Neiswanger, W.; Zhang, R.; Krishnamurthy, A.; Schneider, J.; Poczos, B. Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. 2018
2018
Cited alongside, same era.
Torres, J. A. G.; Jennings, P. C.; Hansen, M. H.; Boes, J. R.; Bligaard, T. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model. 2018
2018
Cited alongside, same era.
Kuleshov, V.; Fenner, N.; Ermon, S. Accurate Uncertainties for Deep Learning Using Calibrated Regression. 35th International Conference on Machine Learning. Stockholm, Sweden, 2018
2018
Cited alongside, same era.
Gardner, J. R.; Pleiss, G.; Bindel, D.; Weinberger, K. Q.; Wilson, A. G. GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. 32nd Conference on Neural Information Processing Systems (NeurIPS). 2018
2018
Later among the works it cites.
Gu, G. H.; Noh, J.; Kim, I.; Jung, Y. Machine learning for renewable energy materials. Journal of Materials Chemistry A 2019
2019
Closest in time.
Schleder, G. R.; Padilha, A. C. M.; Acosta, C. M.; Costa, M.; Fazzio, A. From DFT to Machine Learning: recent approaches to Materials Science – a review. Journal of Physics: Materials 2019
2019
Closest in time.
Alberi, K. et al. The 2019 materials by design roadmap. J. Phys. D: Appl. Phys 2019
2019
Closest in time.
Jinnouchi, R.; Lahnsteiner, J.; Karsai, F.; Kresse, G.; Bokdam, M. Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference. Physical Review Letters 2019
2019
Closest in time.
Musil, F.; Willatt, M. J.; Langovoy, M. A.; Ceriotti, M. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. Journal of Chemical Theory and Computation 2019
2019
Closest in time.
Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chemical Science 2019
2019
Closest in time.
Back, S.; Yoon, J.; Tian, N.; Zhong, W.; Tran, K.; Ulissi, Z. W. Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts. The Journal of Physical Chemistry Letters 2019
2019
Closest in time.