Machine learning for molecular and materials science
暂无分享,去创建一个
[1] P. Dirac. Quantum Mechanics of Many-Electron Systems , 1929 .
[2] C. Hansch,et al. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .
[3] P. Hohenberg,et al. Inhomogeneous Electron Gas , 1964 .
[4] W. Kohn,et al. Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .
[5] E J Corey,et al. Computer-assisted design of complex organic syntheses. , 1969, Science.
[6] David A. Pensak,et al. LHASA—Logic and Heuristics Applied to Synthetic Analysis , 1977 .
[7] Paul C. van Oorschot,et al. Introduction and Fundamentals , 2010 .
[8] S. Segawa,et al. End of the beginning , 1990, Nature.
[9] D. Bonchev. Chemical Graph Theory: Introduction and Fundamentals , 1991 .
[10] Christoph Kuhn,et al. Inverse Strategies for Molecular Design , 1996 .
[11] A. Steane. Quantum Computing , 1997, quant-ph/9708022.
[12] N. N. Kiselyova,et al. Computational materials design using artificial intelligence methods , 1998 .
[13] John A Pople. Quantum Chemical Models (Nobel Lecture). , 1999, Angewandte Chemie.
[14] Alex Zunger,et al. The inverse band-structure problem of finding an atomic configuration with given electronic properties , 1999, Nature.
[15] D. Hand,et al. Idiot's Bayes—Not So Stupid After All? , 2001 .
[16] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[17] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2004 .
[18] Trevor Darrell,et al. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) , 2006 .
[19] Lior Rokach,et al. Data Mining And Knowledge Discovery Handbook , 2005 .
[20] M. Head‐Gordon,et al. Simulated Quantum Computation of Molecular Energies , 2005, Science.
[21] Tudor I. Oprea,et al. Target, chemical and bioactivity databases – integration is key , 2006 .
[22] P. Mahadevan,et al. An overview , 2007, Journal of Biosciences.
[23] Simon J L Billinge,et al. The Problem with Determining Atomic Structure at the Nanoscale , 2007, Science.
[24] Victor Hugo C. de Albuquerque,et al. A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network , 2008 .
[25] Hod Lipson,et al. Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.
[26] Matthias Scheffler,et al. Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions , 2009, J. Comput. Phys..
[27] Vili Podgorelec,et al. Decision trees , 2018, Encyclopedia of Database Systems.
[28] A. Harrow,et al. Quantum algorithm for linear systems of equations. , 2008, Physical review letters.
[29] Alexander Tropsha,et al. Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.
[30] Alexander Tropsha,et al. Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research , 2010, J. Chem. Inf. Model..
[31] Anubhav Jain,et al. Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory , 2010 .
[32] R. Kondor,et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.
[33] Lior Rokach,et al. Classification Trees , 2010, Data Mining and Knowledge Discovery Handbook.
[34] John R. Proudfoot,et al. Faculty Opinions recommendation of Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. , 2010 .
[35] P. Popelier,et al. Potential energy surfaces fitted by artificial neural networks. , 2010, The journal of physical chemistry. A.
[36] Todd A. Brun,et al. Quantum Computing , 2011, Computer Science, The Hardware, Software and Heart of It.
[37] Alán Aspuru-Guzik,et al. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid , 2011 .
[38] Norbert Jankowski,et al. Meta-Learning in Computational Intelligence , 2013, Meta-Learning in Computational Intelligence.
[39] Héléna A. Gaspar,et al. Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure‐Activity Modeling and Dataset Comparison , 2012, Molecular informatics.
[40] Klaus-Robert Müller,et al. Finding Density Functionals with Machine Learning , 2011, Physical review letters.
[41] Thomas Bligaard,et al. Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation , 2012 .
[42] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[43] Marco Buongiorno Nardelli,et al. The high-throughput highway to computational materials design. , 2013, Nature materials.
[44] K. Schwab. The Fourth Industrial Revolution , 2013 .
[45] Aron Walsh,et al. Computational Approaches to Energy Materials , 2013 .
[46] Muratahan Aykol,et al. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) , 2013 .
[47] Sanguthevar Rajasekaran,et al. Accelerating materials property predictions using machine learning , 2013, Scientific Reports.
[48] Kristin A. Persson,et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .
[49] Donald B. Boyd. Quantum Chemistry Program Exchange, Facilitator of Theoretical and Computational Chemistry in Pre-Internet History , 2013 .
[50] Chris-Kriton Skylaris,et al. Hybrid MPI-OpenMP Parallelism in the ONETEP Linear-Scaling Electronic Structure Code: Application to the Delamination of Cellulose Nanofibrils. , 2014, Journal of chemical theory and computation.
[51] Kristof T. Schütt,et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.
[52] Alex Graves,et al. Neural Turing Machines , 2014, ArXiv.
[53] Michiaki Arita,et al. Stable and Efficient Linear Scaling First-Principles Molecular Dynamics for 10000+ Atoms. , 2014, Journal of chemical theory and computation.
[54] Jerome G. P. Wicker,et al. Will it crystallise? Predicting crystallinity of molecular materials , 2015 .
[55] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[56] John J. Irwin,et al. ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..
[57] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[58] J. Vybíral,et al. Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.
[59] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[60] Marco Buongiorno Nardelli,et al. The AFLOW standard for high-throughput materials science calculations , 2015, 1506.00303.
[61] Sergei V. Kalinin,et al. Big-deep-smart data in imaging for guiding materials design. , 2015, Nature materials.
[62] Tejs Vegge,et al. Identifying systematic DFT errors in catalytic reactions , 2015 .
[63] Aron Walsh,et al. Inorganic materials: The quest for new functionality. , 2015, Nature chemistry.
[64] Muratahan Aykol,et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies , 2015 .
[65] W. Alkema,et al. Application of text mining in the biomedical domain. , 2015, Methods.
[66] A. Choudhary,et al. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science , 2016 .
[67] Olexandr Isayev,et al. Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode , 2016 .
[68] Felix A Faber,et al. Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. , 2015, Physical review letters.
[69] Taylor D. Sparks,et al. High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds , 2016 .
[70] Wojciech Czarnecki,et al. Learning to SMILE(S) , 2016, ArXiv.
[71] Piotr Dittwald,et al. Computer-Assisted Synthetic Planning: The End of the Beginning. , 2016, Angewandte Chemie.
[72] Juno Nam,et al. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions , 2016, ArXiv.
[73] Paul Raccuglia,et al. Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.
[74] B. Meredig,et al. Materials science with large-scale data and informatics: Unlocking new opportunities , 2016 .
[75] Ryan P. Adams,et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. , 2016, Nature materials.
[76] Aron Walsh,et al. Computational Screening of All Stoichiometric Inorganic Materials , 2016, Chem.
[77] Hans-J. Briegel,et al. Quantum-enhanced machine learning , 2016, Physical review letters.
[78] Roger G. Melko,et al. Machine learning phases of matter , 2016, Nature Physics.
[79] M. Head‐Gordon,et al. ωB97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation. , 2016, The Journal of chemical physics.
[80] Stefano de Gironcoli,et al. Reproducibility in density functional theory calculations of solids , 2016, Science.
[81] Patrick McCabe,et al. Generation of crystal structures using known crystal structures as analogues , 2016, Acta crystallographica Section B, Structural science, crystal engineering and materials.
[82] Steven L. Brunton,et al. Data-driven discovery of partial differential equations , 2016, Science Advances.
[83] Igor V Tetko,et al. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development , 2017, Molecular informatics.
[84] Alán Aspuru-Guzik,et al. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.
[85] Andrew I. Cooper,et al. Functional materials discovery using energy–structure–function maps , 2017, Nature.
[86] Leroy Cronin,et al. An autonomous organic reaction search engine for chemical reactivity , 2017, Nature Communications.
[87] Andreas Trabesinger. Quantum leaps, bit by bit , 2017, Nature.
[88] Li Li,et al. Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.
[89] M. Troyer,et al. Elucidating reaction mechanisms on quantum computers , 2016, Proceedings of the National Academy of Sciences.
[90] Vijay S. Pande,et al. Low Data Drug Discovery with One-Shot Learning , 2016, ACS central science.
[91] Cormac Toher,et al. Universal fragment descriptors for predicting properties of inorganic crystals , 2016, Nature Communications.
[92] Marcin Andrychowicz,et al. One-Shot Imitation Learning , 2017, NIPS.
[93] Alok Choudhary,et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations , 2017 .
[94] Marwin H. S. Segler,et al. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. , 2017, Chemistry.
[95] J. Behler. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. , 2017, Angewandte Chemie.
[96] Maxim Ziatdinov,et al. Learning surface molecular structures via machine vision , 2017, npj Computational Materials.
[97] Bowen Liu,et al. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models , 2017, ACS central science.
[98] Jacob biamonte,et al. Quantum machine learning , 2016, Nature.
[99] George E. Dahl,et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.
[100] Jerome G. P. Wicker,et al. A publicly available crystallisation data set and its application in machine learning , 2017 .
[101] A. McCallum,et al. Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning , 2017 .
[102] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[103] François-Xavier Coudert,et al. Reproducible Research in Computational Chemistry of Materials , 2017 .
[104] Atsuto Seko,et al. Descriptors for Machine Learning of Materials Data , 2017, 1709.01666.
[105] Piotr Dittwald,et al. Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory , 2018 .
[106] Natalio Mingo,et al. Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods. , 2017, The journal of physical chemistry. B.
[107] Mike Preuss,et al. Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.
[108] Yaohua Liu,et al. Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science , 2019, CVC.
[109] F. Racioppi,et al. Unlocking new opportunities , 2022 .