Materials Informatics: An Algorithmic Design Rule
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[1] R. Jacobs,et al. Calibration after bootstrap for accurate uncertainty quantification in regression models , 2022, npj Computational Materials.
[2] Chi Chen,et al. A universal graph deep learning interatomic potential for the periodic table , 2022, Nature Computational Science.
[3] W. Green,et al. Multi-fidelity prediction of molecular optical peaks with deep learning , 2021, Chemical science.
[4] A. Strachan,et al. Active learning and molecular dynamics simulations to find high melting temperature alloys , 2021, Computational Materials Science.
[5] Alejandro Strachan,et al. Sim2Ls: FAIR simulation workflows and data , 2021, PloS one.
[6] Chiho Kim,et al. Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms , 2021, Journal of Materials Science.
[7] Florence H. Vermeire,et al. Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy , 2021, J. Chem. Inf. Model..
[8] I. Takeuchi,et al. On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning , 2021, Applied Physics Reviews.
[9] Suzanna Sia,et al. Clustering with UMAP: Why and How Connectivity Matters , 2021, ArXiv.
[10] William H. Green,et al. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction , 2021, J. Chem. Inf. Model..
[11] Rampi Ramprasad,et al. Data-assisted polymer retrosynthesis planning , 2021 .
[12] Geoffrey E. Hinton,et al. Deep learning for AI , 2021, Commun. ACM.
[13] AkshatKumar Nigam,et al. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design , 2021, Digital discovery.
[14] William H. Green,et al. Predicting Infrared Spectra with Message Passing Neural Networks , 2021, J. Chem. Inf. Model..
[15] Deepak Kamal,et al. Novel high voltage polymer insulators using computational and data-driven techniques. , 2021, The Journal of chemical physics.
[16] Ankit Srivastava,et al. Efficiently exploiting process-structure-property relationships in material design by multi-information source fusion , 2021, Acta Materialia.
[17] A. Strachan,et al. Neural network reactive force field for C, H, N, and O systems , 2021, npj Computational Materials.
[18] Chi Chen,et al. Learning properties of ordered and disordered materials from multi-fidelity data , 2021, Nature Computational Science.
[19] Alejandro Strachan,et al. Parsimonious neural networks learn interpretable physical laws , 2020, Scientific Reports.
[20] Rampi Ramprasad,et al. Automated knowledge extraction from polymer literature using natural language processing , 2020, iScience.
[21] Michael L. Waskom,et al. Seaborn: Statistical Data Visualization , 2021, J. Open Source Softw..
[22] Lihua Chen,et al. Machine-learning predictions of polymer properties with Polymer Genome , 2020, Journal of Applied Physics.
[23] Arunkumar Chitteth Rajan,et al. Polymer informatics with multi-task learning , 2020, Patterns.
[24] Danial Khatamsaz,et al. Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion , 2020, JOM.
[25] Tim Sainburg,et al. Parametric UMAP Embeddings for Representation and Semisupervised Learning , 2020, Neural Computation.
[26] Jeff Reback,et al. pandas-dev/pandas: Pandas 1.1.2 , 2020 .
[27] V. Chaudhary,et al. Cyberinfrastructure for Sustained Scientific Innovation (NSF) , 2020, Federal Grants & Contracts.
[28] Jonas Verhellen,et al. Illuminating elite patches of chemical space† , 2020, Chemical science.
[29] D. Morgan,et al. Opportunities and Challenges for Machine Learning in Materials Science , 2020, Annual Review of Materials Research.
[30] Jaime Fern'andez del R'io,et al. Array programming with NumPy , 2020, Nature.
[31] Chiho Kim,et al. A Deep Learning Solvent-Selection Paradigm Powered by a Massive Solvent/Nonsolvent Database for Polymers , 2020 .
[32] Hieu A. Doan,et al. Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials , 2020, Chemistry of Materials.
[33] Jordan P. Lightstone,et al. Frequency-dependent dielectric constant prediction of polymers using machine learning , 2020, npj Computational Materials.
[34] Regina Barzilay,et al. Uncertainty Quantification Using Neural Networks for Molecular Property Prediction , 2020, J. Chem. Inf. Model..
[35] Riley J. Hickman,et al. Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge , 2020, 2003.12127.
[36] S. Khrapak. Lindemann melting criterion in two dimensions , 2020, 2002.00651.
[37] Emma J. Chory,et al. A Deep Learning Approach to Antibiotic Discovery , 2020, Cell.
[38] Samuel Temple Reeve,et al. Implementing a neural network interatomic model with performance portability for emerging exascale architectures , 2020, Comput. Phys. Commun..
[39] Ryan P. Lively,et al. Polymer genome–based prediction of gas permeabilities in polymers , 2020 .
[40] Masayuki Shirane,et al. Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning , 2020, Science and technology of advanced materials.
[41] M. Withnall,et al. Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction , 2020, Journal of Cheminformatics.
[42] Dane Morgan,et al. Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion , 2019, Computational Materials Science.
[43] Ryan Jacobs,et al. The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research , 2019, Computational Materials Science.
[44] Alán Aspuru-Guzik,et al. Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space , 2019, ICLR.
[45] Rampi Ramprasad,et al. Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers , 2019, J. Chem. Inf. Model..
[46] Connor W. Coley,et al. BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules , 2019, ACS central science.
[47] Kristof T. Schütt,et al. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions , 2019, Nature Communications.
[48] Seyede Fatemeh Ghoreishi,et al. Efficient Use of Multiple Information Sources in Material Design , 2019, Acta Materialia.
[49] Zhehui Wang,et al. Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators , 2019, Journal of Materials Science.
[50] Pascal Friederich,et al. Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation , 2019, Mach. Learn. Sci. Technol..
[51] Ethan Fetaya,et al. Evaluating and Calibrating Uncertainty Prediction in Regression Tasks , 2019, Sensors.
[52] Regina Barzilay,et al. Analyzing Learned Molecular Representations for Property Prediction , 2019, J. Chem. Inf. Model..
[53] Markus Meuwly,et al. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.
[54] Anand Chandrasekaran,et al. Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures , 2019, Modelling and Simulation in Materials Science and Engineering.
[55] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[56] Chi Chen,et al. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals , 2018, Chemistry of Materials.
[57] Prasanna Balaprakash,et al. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks , 2018, 2018 IEEE 25th International Conference on High Performance Computing (HiPC).
[58] Patrick Huck,et al. Active learning for accelerated design of layered materials , 2018, npj Computational Materials.
[59] Marwin H. S. Segler,et al. GuacaMol: Benchmarking Models for De Novo Molecular Design , 2018, J. Chem. Inf. Model..
[60] Andrew Gordon Wilson,et al. GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration , 2018, NeurIPS.
[61] Michele Ceriotti,et al. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. , 2018, Journal of chemical theory and computation.
[62] K-R Müller,et al. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. , 2018, Journal of chemical theory and computation.
[63] Ankit Srivastava,et al. Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials , 2018, Journal of Mechanical Design.
[64] Anand Chandrasekaran,et al. Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions , 2018, The Journal of Physical Chemistry C.
[65] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[66] Sebastian Raschka,et al. MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack , 2018, J. Open Source Softw..
[67] Yuma Iwasaki,et al. Boosting material modeling using game tree search , 2018, Physical Review Materials.
[68] Mike Preuss,et al. Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.
[69] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[70] Leland McInnes,et al. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.
[71] Alán Aspuru-Guzik,et al. Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics , 2017 .
[72] Arun Mannodi-Kanakkithodi,et al. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond , 2017, Materials Today.
[73] K-R Müller,et al. SchNet - A deep learning architecture for molecules and materials. , 2017, The Journal of chemical physics.
[74] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[75] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[76] Timo Aila,et al. Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder , 2017, ACM Trans. Graph..
[77] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[78] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[79] Stefan Steinerberger,et al. Clustering with t-SNE, provably , 2017, SIAM J. Math. Data Sci..
[80] Károly Héberger,et al. Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching , 2017 .
[81] S. Datta,et al. Design of novel age-hardenable aluminium alloy using evolutionary computation , 2017 .
[82] W. D. Thomison,et al. A Model Reification Approach to Fusing Information from Multifidelity Information Sources , 2017 .
[83] Julia Ling,et al. High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates , 2017, Integrating Materials and Manufacturing Innovation.
[84] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[85] Supratik Mukhopadhyay,et al. Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag , 2017, J. Chem. Inf. Model..
[86] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[87] Kenta Hongo,et al. Bayesian molecular design with a chemical language model , 2017, Journal of Computer-Aided Molecular Design.
[88] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[89] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[90] Jonathan Masci,et al. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[91] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[92] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[93] 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.
[94] Junichiro Shiomi,et al. Designing Nanostructures for Phonon Transport via Bayesian Optimization , 2016, 1609.04972.
[95] Li Li,et al. Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.
[96] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[97] Jorge Cadima,et al. Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[98] Le Song,et al. Discriminative Embeddings of Latent Variable Models for Structured Data , 2016, ICML.
[99] Erik Schultes,et al. The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.
[100] Arun Mannodi-Kanakkithodi,et al. Rational Co‐Design of Polymer Dielectrics for Energy Storage , 2016, Advanced materials.
[101] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[102] Chiho Kim,et al. A polymer dataset for accelerated property prediction and design , 2016, Scientific Data.
[103] Arun Mannodi-Kanakkithodi,et al. Machine Learning Strategy for Accelerated Design of Polymer Dielectrics , 2016, Scientific Reports.
[104] Wei Xiong,et al. Cybermaterials: materials by design and accelerated insertion of materials , 2016 .
[105] Samy Bengio,et al. Order Matters: Sequence to sequence for sets , 2015, ICLR.
[106] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.
[107] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[108] Stefan Steinerberger,et al. A Hidden Signal in the Ulam Sequence , 2015, Exp. Math..
[109] O. A. von Lilienfeld,et al. Electronic spectra from TDDFT and machine learning in chemical space. , 2015, The Journal of chemical physics.
[110] Arun Mannodi-Kanakkithodi,et al. Accelerated materials property predictions and design using motif-based fingerprints , 2015, 1503.07503.
[111] Ryan P. Adams,et al. Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.
[112] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[113] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[114] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[115] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[116] Alok Choudhary,et al. Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .
[117] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[118] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[119] Trevor J. Hastie,et al. Confidence intervals for random forests: the jackknife and the infinitesimal jackknife , 2013, J. Mach. Learn. Res..
[120] Charles H. Ward. Materials Genome Initiative for Global Competitiveness , 2012 .
[121] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[122] Geoffrey E. Hinton,et al. Visualizing non-metric similarities in multiple maps , 2011, Machine Learning.
[123] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[124] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[125] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[126] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[127] Laurens van der Maaten,et al. Learning a Parametric Embedding by Preserving Local Structure , 2009, AISTATS.
[128] Matthias Rarey,et al. On the Art of Compiling and Using 'Drug‐Like' Chemical Fragment Spaces , 2008, ChemMedChem.
[129] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[130] Ah Chung Tsoi,et al. Graph neural networks for ranking Web pages , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).
[131] Tatsuya Akutsu,et al. Extensions of marginalized graph kernels , 2004, ICML.
[132] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[133] James G. Nourse,et al. Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..
[134] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[135] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[136] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[137] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[138] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[139] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[140] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[141] R. L. Winkler. Combining Probability Distributions from Dependent Information Sources , 1981 .
[142] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[143] G. Grimvall,et al. Correlation of Properties of Materials to Debye and Melting Temperatures , 1974 .
[144] M. Ross,et al. Generalized Lindemann Melting Law , 1969 .
[145] L. Libby,et al. New Melting Law at High Pressures. , 1969 .
[146] H. L. Morgan. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. , 1965 .
[147] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[148] D J Rogers,et al. A Computer Program for Classifying Plants. , 1960, Science.
[149] A. M. Turing,et al. Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.
[150] D. P. MacDougall,et al. A Mechanical Analyzer for the Solution of Secular Equations and the Calculation of Molecular Vibration Frequencies , 1937 .
[151] Franz Simon,et al. Bemerkungen zur Schmelzdruckkurve , 1929 .
[152] P. Jaccard. THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .
[153] R. Batra,et al. Polymer design using genetic algorithm and machine learning , 2021, Computational Materials Science.
[154] R. Ramprasad,et al. Copolymer Informatics with Multitask Deep Neural Networks , 2021 .
[155] Deepak Kamal,et al. polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics , 2021, Chemistry of Materials.
[156] Svetha Venkatesh,et al. Batch Bayesian optimization using multi-scale search , 2020, Knowl. Based Syst..
[157] Kyle Swanson,et al. Message passing neural networks for molecular property prediction , 2019 .
[158] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[159] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[160] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[161] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[162] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[163] Ah Chung Tsoi,et al. Computational Capabilities of Graph Neural Networks , 2009, IEEE Transactions on Neural Networks.
[164] Anthony Turner,et al. Materials by Design , 2008 .
[165] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[166] Ian A. Watson,et al. ErG: 2D Pharmacophore Descriptions for Scaffold Hopping , 2006, J. Chem. Inf. Model..
[167] W. C. Mitchell. Statistical Mechanics of Thermally Driven Systems. , 1967 .
[168] Alex Fraser,et al. Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .
[169] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .