Carl Poelking

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All published works

Action	Title	Year	Authors
+	Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions	2023	Lucian Chan Marcel L. Verdonk Carl Poelking
+	CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM	2023	Rubén Sánchez-García Michael Saur Javier Vargas Carl Poelking Charlotte M. Deane
+ PDF Chat	BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale	2022	Carl Poelking Felix A. Faber Bingqing Cheng
+	Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns	2022	Carl Poelking Gianni Chessari Christopher W. Murray Richard J. Hall Lucy J. Colwell Marcel L. Verdonk
+	3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design	2022	Lucian Chan Rajendra Kumar Marcel L. Verdonk Carl Poelking
+ PDF Chat	Chemical Design Rules for Non‐Fullerene Acceptors in Organic Solar Cells	2021	Anastasia Markina Kun‐Han Lin Wenlan Liu Carl Poelking Yuliar Firdaus Diego Rosas Villalva Jafar I. Khan Sri Harish Kumar Paleti George T. Harrison Julien Gorenflot
+	BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale	2021	Carl Poelking Felix A. Faber Bingqing Cheng
+	Investigating 3D Atomic Environments for Enhanced QSAR	2020	William McCorkindale Carl Poelking Alpha A. Lee
+	Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering	2019	Carl Poelking Yehia Amar Alexei A. Lapkin Lucy J. Colwell
+	Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering	2019	Carl Poelking Yehia Amar Alexei A. Lapkin Lucy J. Colwell
+ PDF Chat	Machine learning unifies the modeling of materials and molecules	2017	Albert P. Bartók Sandip De Carl Poelking Noam Bernstein James R. Kermode Gábor Cśanyi Michele Ceriotti

Common Coauthors

Coauthor	Papers Together
Marcel L. Verdonk	3
Lucy J. Colwell	3
Alexei A. Lapkin	2
Felix A. Faber	2
Bingqing Cheng	2
Yehia Amar	2
Lucian Chan	2
Gábor Cśanyi	1
James R. Kermode	1
Derya Baran	1
Wenlan Liu	1
Iain McCulloch	1
Noam Bernstein	1
Sri Harish Kumar Paleti	1
George T. Harrison	1
Rubén Sánchez-García	1
Diego Rosas Villalva	1
Thomas D. Anthopoulos	1
Anastasia Markina	1
Julien Gorenflot	1
Yuliar Firdaus	1
Alpha A. Lee	1
Albert P. Bartók	1
Stefaan De Wolf	1
Sandip De	1
Kun‐Han Lin	1
Frédéric Laquai	1
Jafar I. Khan	1
Christopher W. Murray	1
Denis Andrienko	1
Weimin Zhang	1
Javier Vargas	1
Rajendra Kumar	1
Gianni Chessari	1
Michael Saur	1
Charlotte M. Deane	1
William McCorkindale	1
Michele Ceriotti	1
Richard J. Hall	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	On representing chemical environments	2013	Albert P. Bartók Risi Kondor Gábor Cśanyi	3
+ PDF Chat	Comparing molecules and solids across structural and alchemical space	2016	Sandip De Albert P. Bartók Gábor Cśanyi Michele Ceriotti	3
+ PDF Chat	ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost	2017	Justin S. Smith Olexandr Isayev Adrián E. Roitberg	2
+	Unified Representation of Molecules and Crystals for Machine Learning	2017	Haoyan Huo Matthias Rupp	2
+ PDF Chat	Machine-learned multi-system surrogate models for materials prediction	2019	Chandramouli Nyshadham Matthias Rupp Brayden Bekker Alexander V. Shapeev Tim Mueller Conrad W. Rosenbrock Gábor Cśanyi David Wingate Gus L. W. Hart	2
+ PDF Chat	Machine learning unifies the modeling of materials and molecules	2017	Albert P. Bartók Sandip De Carl Poelking Noam Bernstein James R. Kermode Gábor Cśanyi Michele Ceriotti	2
+ PDF Chat	Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning	2012	Matthias Rupp Alexandre Tkatchenko Klaus‐Robert Müller O. Anatole von Lilienfeld	1
+ PDF Chat	Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials	2016	Alexander V. Shapeev	1
+	AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery	2015	Izhar Wallach Michael Dzamba Abraham Heifets	1
+ PDF Chat	Quantum-chemical insights from deep tensor neural networks	2017	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela K. Müller Alexandre Tkatchenko	1
+ PDF Chat	Machine learning based interatomic potential for amorphous carbon	2017	Volker L. Deringer Gábor Cśanyi	1
+ PDF Chat	Optimal Design of Experiments by Combining Coarse and Fine Measurements	2017	Alpha A. Lee Michael P. Brenner Lucy J. Colwell	1
+	Neural Message Passing for Quantum Chemistry	2017	Justin Gilmer Samuel S. Schoenholz Patrick Riley Oriol Vinyals George E. Dahl	1
+	Unified Representation for Machine Learning of Molecules and Crystals	2017	Haoyan Huo Matthias Rupp	1
+ PDF Chat	Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error	2017	Felix A. Faber Luke A. D. Hutchison Bing Huang Justin Gilmer Samuel S. Schoenholz George E. Dahl Oriol Vinyals Steven Kearnes Patrick Riley O. Anatole von Lilienfeld	1
+ PDF Chat	Data-Driven Learning of Total and Local Energies in Elemental Boron	2018	Volker L. Deringer Chris J. Pickard Gábor Cśanyi	1
+ PDF Chat	SchNet – A deep learning architecture for molecules and materials	2018	Kristof T. Schütt Huziel E. Sauceda Pieter-Jan Kindermans Alexandre Tkatchenko K. Müller	1
+ PDF Chat	Growth Mechanism and Origin of High <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>s</mml:mi><mml:msup><mml:mi>p</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> Content in Tetrahedral Amorphous Carbon	2018	A. Miguel Volker L. Deringer Jari Koskinen Tomi Laurila Gábor Cśanyi	1
+	PotentialNet for Molecular Property Prediction	2018	Evan N. Feinberg Debnil Sur Zhenqin Wu Brooke E. Husic Huanghao Mai Yang Li Saisai Sun Jianyi Yang Bharath Ramsundar Vijay S. Pande	1
+	Ab initio thermodynamics of liquid and solid water	2019	Bingqing Cheng Edgar A. Engel Jörg Behler Christoph Dellago Michele Ceriotti	1
+ PDF Chat	Atom-density representations for machine learning	2019	Michael J. Willatt Félix Musil Michele Ceriotti	1
+ PDF Chat	Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout	2019	Isidro Cortés‐Ciriano Andreas Bender	1
+	Convolutional Networks on Graphs for Learning Molecular Fingerprints	2015	David Duvenaud Dougal Maclaurin Jorge Aguilera‐Iparraguirre Rafael Gómez‐Bombarelli Timothy Hirzel Alán Aspuru‐Guzik Ryan P. Adams	1
+ PDF Chat	Moment Tensor Potentials	2016	Alexander V. Shapeev	1
+	Analyzing Learned Molecular Representations for Property Prediction	2019	Kevin Yang Kyle Swanson Wengong Jin Connor W. Coley Philipp Eiden Hua Gao Angel Guzmán-Pérez Timothy Hopper Brian P. Kelley Miriam Mathea	1
+	DScribe: Library of descriptors for machine learning in materials science	2019	Lauri Himanen Marc O. J. Jäger Eiaki V. Morooka Filippo Federici Canova Yashasvi S. Ranawat David Gao Patrick Rinke Adam S. Foster	1
+	Hierarchical modeling of molecular energies using a deep neural network	2018	Nicholas Lubbers Justin S. Smith Kipton Barros	1
+ PDF Chat	Alchemical and structural distribution based representation for universal quantum machine learning	2018	Felix A. Faber Anders S. Christensen Bing Huang O. Anatole von Lilienfeld	1
+ PDF Chat	Machine learning of molecular electronic properties in chemical compound space	2013	Grégoire Montavon Matthias Rupp Vivekanand V. Gobre Álvaro Vázquez‐Mayagoitia Katja Hansen Alexandre Tkatchenko Klaus‐Robert Müller O. Anatole von Lilienfeld	1
+ PDF Chat	Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity	2016	Bing Huang O. Anatole von Lilienfeld	1
+ PDF Chat	Predicting the phase diagram of titanium dioxide with random search and pattern recognition	2020	Aleks Reinhardt Chris J. Pickard Bingqing Cheng	1
+ PDF Chat	Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization	2018	Izhar Wallach Abraham Heifets	1
+ PDF Chat	Charge Photogeneration in Non‐Fullerene Organic Solar Cells: Influence of Excess Energy and Electrostatic Interactions	2020	Maria Saladina Pablo Simón Marqués Anastasia Markina Safakath Karuthedath Christopher Wöpke Clemens Göhler Yue Chen Magali Allain Philippe Blanchard Clément Cabanetos	1
+ PDF Chat	Defect Formation Energies without the Band-Gap Problem: Combining Density-Functional Theory and the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>G</mml:mi><mml:mi>W</mml:mi></mml:math>Approach for the Silicon Self-Interstitial	2009	Patrick Rinke Anderson Janotti Matthias Scheffler Chris G. Van de Walle	1
+ PDF Chat	Physics-Inspired Structural Representations for Molecules and Materials	2021	Félix Musil Andrea Grisafi Albert P. Bartók Christoph Ortner Gábor Cśanyi Michele Ceriotti	1
+	Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances	2013	Marco Cuturi	1
+ PDF Chat	Machine Learning Energies of 2 Million Elpasolite<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>A</mml:mi><mml:mi>B</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math>Crystals	2016	Felix A. Faber Alexander Lindmaa O. Anatole von Lilienfeld Rickard Armiento	1
+	Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach	2015	Raghunathan Ramakrishnan Pavlo O. Dral Matthias Rupp O. Anatole von Lilienfeld	1
+ PDF Chat	Finding Density Functionals with Machine Learning	2012	John Snyder Matthias Rupp Katja Hansen Klaus‐Robert Müller Kieron Burke	1
+ PDF Chat	Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons	2010	Albert P. Bartók M. C. Payne Risi Kondor Gábor Cśanyi	1