References
- Niu G, Guo X, Wang L. Review of recent progress in chemical stability of perovskite solar cells. J Mater Chem A. 2015;3(17):8970–8980.
- Kaji H, Suzuki H, Fukushima T, et al. Purely organic electroluminescent material realizing 100% conversion from electricity to light. Nat Commun. 2015;6:8476.
- Ueda A, Yamada S, Isono T, et al. Hydrogen-bond-dynamics-based switching of conductivity and magnetism: a phase transition caused by deuterium and electron transfer in a hydrogen-bonded purely organic conductor crystal. J Am Chem Soc. 2014;136(34):12184–12192.
- Yeung MCL, Yam VWW. Luminescent cation sensors: from host-guest chemistry, supramolecular chemistry to reaction-based mechanisms. Chem Soc Rev. 2015;44(13):4192–4202.
- Horiuchi S, Tokura Y. Organic ferroelectrics. Nat Mater. 2008;7(5):357–366.
- Podlewska S, Czarnecki WM, Kafel R, et al. Creating the new from the old: Combinatorial libraries generation with machine-learning-based compound structure optimization. J Chem Inf Model. 2017;57(2):133–147.
- Ikebata H, Hongo K, Isomura T, et al. Bayesian molecular design with a chemical language model. J Comput Aided Mol Des. 2017;31(4):379–391.
- Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–36.
- Bowman SR, Vilnis L, Vinyals O, et al. Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL); Berlin; 2016. p. 10–21.
- Oord Avd, Kalchbrenner N, Kavukcuoglu K. Pixel recurrent neural networks. In: Proceedings of 33rd International Conference on Machine Learning (ICML); New York City; 2016. p. 1747–1756.
- Gómez-Bombarelli R, Duvenaud D, Hernández-Lobato JM, et al. Automatic chemical design using a data-driven continuous representation of molecules. arXiv preprint arXiv:161002415; 2016.
- Kusner MJ, Paige B, Hernández-Lobato JM. Grammar variational autoencoder. In: Proceedings of 34th International Conference on Machine Learning (ICML); Sydney; 2017. p. 1945–1954.
- Segler MH, Kogej T, Tyrchan C, et al. Generating focussed molecule libraries for drug discovery with recurrent neural networks. arXiv preprint arXiv:170101329; 2017.
- Cho K, van Merrienboer B, Gülçehre Ç, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); Doha; 2014. p. 1724–1734.
- Browne C, Powley E, Whitehouse D, et al. A survey of Monte Carlo tree search methods. IEEE Trans Comput Intell AI Games. 2012;4(1):1–43.
- Silver D, Huang A, Maddison CJ, et al. Mastering the game of go with deep neural networks and tree search. Nature. 2016;529(7587):484–489.
- Dieb TM, Ju S, Yoshizoe K, et al. MDTS: automatic complex materials design using Monte Carlo tree search. Sci Tech Adv Mater. 2017;18(1):498–503.
- Kingma D, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980; 2014.
- Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminf. 2009;1(1):8.
- Irwin JJ, Sterling T, Mysinger MM, et al. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model. 2012;52(7):1757–1768.
- Ueno T, Rhone T, Hou Z, et al. COMBO: an efficient Bayesian optimization library for materials science. Mater Discov. 2016;4:18–21.
- Ong SP, Richards WD, Jain A, et al. Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comp Mater Sci. 2013;68:314–319.