Fetching the paper…
Reading the bibliography…
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
Werner Heisenberg, “über den anschaulichen inhalt der quantentheoretischen kinematik und mechanik,”
1927
Earlier work this paper cites.
Alan M Turing, “Computing machinery and intelligence,”
1950
Earlier work this paper cites.
Carl G Hempel, Aspects of scientific explanation (Free Press New York, 1965)
1965
Earlier work this paper cites.
Michael Friedman, “Explanation and scientific understanding,”
1974
Earlier work this paper cites.
Philip Kitcher, “Explanatory unification,”
1981
Earlier work this paper cites.
Erwin Schrödinger, ’Nature and the Greeks’ and ’Science and Humanism’ (Cambridge University Press, 1996)
1996
Earlier work this paper cites.
Edward A Feigenbaum, “Some challenges and grand challenges for computational intelligence,”
2003
Earlier work this paper cites.
Henk W De Regt and Dennis Dieks, “A contextual approach to scientific understanding,”
2005
Earlier work this paper cites.
2006
Earlier work this paper cites.
Chris J Pickard and RJ Needs, “Highly compressed ammonia forms an ionic crystal,”
2008
Earlier work this paper cites.
Jürgen Schmidhuber, “Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes,”
2008
Earlier work this paper cites.
Ross D King, Jem Rowland, Stephen G Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa N Soldatova, et al. , “The automation of science,”
2009
Earlier work this paper cites.
Michael Schmidt and Hod Lipson, “Distilling free-form natural laws from experimental data,”
2009
Earlier work this paper cites.
Laurent Itti and Pierre Baldi, “Bayesian surprise attracts human attention,”
2009
Earlier work this paper cites.
Peder Larsen and Markus Von Ins, “The rate of growth in scientific publication and the decline in coverage provided by science citation index,”
2010
Earlier work this paper cites.
Chris J Pickard and RJ Needs, “Ab initio random structure searching,”
2011
Earlier work this paper cites.
James A Evans and Jacob G Foster, “Metaknowledge,”
2011
Earlier work this paper cites.
Henk W De Regt, “Visualization as a tool for understanding,”
2014
Earlier work this paper cites.
Angela Potochnik, “The diverse aims of science,”
2015
Earlier work this paper cites.
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal, “Long short term memory networks for anomaly detection in time series,”
2015
Earlier work this paper cites.
Andrey Rzhetsky, Jacob G Foster, Ian T Foster, and James A Evans, “Choosing experiments to accelerate collective discovery,”
2015
Earlier work this paper cites.
Aravindh Mahendran and Andrea Vedaldi, “Understanding deep image representations by inverting them,”
2015
Earlier work this paper cites.
Alexander Mordvintsev, Christopher Olah, and Mike Tyka, “Inceptionism: Going deeper into neural networks,”
2015
Earlier work this paper cites.
Esteban A Martinez, Christine A Muschik, Philipp Schindler, Daniel Nigg, Alexander Erhard, Markus Heyl, Philipp Hauke, Marcello Dalmonte, Thomas Monz, Peter Zoller, et al. , “Real-time dynamics of lattice gauge theories with a few-qubit quantum computer,”
2016
Earlier work this paper cites.
Mario Krenn, Mehul Malik, Robert Fickler, Radek Lapkiewicz, and Anton Zeilinger, “Automated search for new quantum experiments,”
2016
Earlier work this paper cites.
Lenka Zdeborová, “New tool in the box,”
2017
Earlier work this paper cites.
Angela Potochnik, Idealization and the Aims of Science (University of Chicago Press, 2017)
2017
Earlier work this paper cites.
Henk W De Regt, Understanding scientific understanding (Oxford University Press, 2017)
2017
Earlier work this paper cites.
Jiehang Zhang, PW Hess, A Kyprianidis, P Becker, A Lee, J Smith, G Pagano, I-D Potirniche, Andrew C Potter, A Vishwanath, et al. , “Observation of a discrete time crystal,”
2017
Earlier work this paper cites.
Christian Gross and Immanuel Bloch, “Quantum simulations with ultracold atoms in optical lattices,”
2017
Earlier work this paper cites.
Aaron Clauset, Daniel B Larremore, and Roberta Sinatra, “Data-driven predictions in the science of science,”
2017
Earlier work this paper cites.
William L Hamilton, Rex Ying, and Jure Leskovec, “Inductive representation learning on large graphs,”
2017
Earlier work this paper cites.
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell, “Curiosity-driven exploration by self-supervised prediction,”
2017
Earlier work this paper cites.
Henk W De Regt and Victor Gijsbers, “How false theories can yield genuine understanding,”
2017
Earlier work this paper cites.
Thomas Fösel, Petru Tighineanu, Talitha Weiss, and Florian Marquardt, “Reinforcement learning with neural networks for quantum feedback,”
2018
Cited alongside, same era.
Alexey A Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, and Hans J Briegel, “Active learning machine learns to create new quantum experiments,”
2018
Cited alongside, same era.
Alán Aspuru-Guzik, Roland Lindh, and Markus Reiher, “The matter simulation (r)evolution,”
2018
Cited alongside, same era.
Michael O’Connor, Helen M Deeks, Edward Dawn, Oussama Metatla, Anne Roudaut, Matthew Sutton, Lisa May Thomas, Becca Rose Glowacki, Rebecca Sage, Philip Tew, et al. , “Sampling molecular conformations and dynamics in a multiuser virtual reality framework,”
2018
Cited alongside, same era.
Daniel Probst and Jean-Louis Reymond, “Exploring drugbank in virtual reality chemical space,”
Jonathan Grizou, Laurie J Points, Abhishek Sharma, and Leroy Cronin, “A curious formulation robot enables the discovery of a novel protocell behavior,”
2020
Later among the works it cites.
Hyungil Moon, Dominic T Lennon, James Kirkpatrick, Nina M van Esbroeck, Leon C Camenzind, Liuqi Yu, Florian Vigneau, Dominik M Zumbühl, G Andrew D Briggs, Michael A Osborne, et al. , “Machine learning enables completely automatic tuning of a quantum device faster than human experts,”
2020
Later among the works it cites.
Mogens Dalgaard, Felix Motzoi, Jens Jakob Sørensen, and Jacob Sherson, “Global optimization of quantum dynamics with alphazero deep exploration,”
2020
Later among the works it cites.
Elsa A Olivetti, Jacqueline M Cole, Edward Kim, Olga Kononova, Gerbrand Ceder, Thomas Yong-Jin Han, and Anna M Hiszpanski, “Data-driven materials research enabled by natural language processing and information extraction,”
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Anne-Catherine Bédard, Andrea Adamo, Kosi C Aroh, M Grace Russell, Aaron A Bedermann, Jeremy Torosian, Brian Yue, Klavs F Jensen, and Timothy F Jamison, “Reconfigurable system for automated optimization of diverse chemical reactions,”
2018
Cited alongside, same era.
Santo Fortunato, Carl T Bergstrom, Katy Börner, James A Evans, Dirk Helbing, Staša Milojević, Alexander M Petersen, Filippo Radicchi, Roberta Sinatra, Brian Uzzi, et al. , “Science of science,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller, “Methods for interpreting and understanding deep neural networks,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Christian Schweizer, Fabian Grusdt, Moritz Berngruber, Luca Barbiero, Eugene Demler, Nathan Goldman, Immanuel Bloch, and Monika Aidelsburger, “Floquet approach to z2 lattice gauge theories with ultracold atoms in optical lattices,”
2019
Cited alongside, same era.
Mario Krenn and Anton Zeilinger, “Predicting research trends with semantic and neural networks with an application in quantum physics,”
2020
Later among the works it cites.
2020
Later among the works it cites.
Ribana Roscher, Bastian Bohn, Marco F Duarte, and Jochen Garcke, “Explainable machine learning for scientific insights and discoveries,”
2020
Later among the works it cites.
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee, “From local explanations to global understanding with explainable ai for trees,”
2020
Later among the works it cites.
2020
Later among the works it cites.
Lav R Varshney, Nazneen Fatema Rajani, and Richard Socher, “Explaining creative artifacts,”
2020
Later among the works it cites.
Jesse Thaler, “Designing an ai physicist,”
2021
Later among the works it cites.
Alireza Seif, Mohammad Hafezi, and Christopher Jarzynski, “Machine learning the thermodynamic arrow of time,”
2021
Later among the works it cites.
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. , “Highly accurate protein structure prediction with alphafold,”
2021
Later among the works it cites.
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, et al. , “Highly accurate protein structure prediction for the human proteome,”
2021
Later among the works it cites.
Xingyuan Xu, Mengxi Tan, Bill Corcoran, Jiayang Wu, Andreas Boes, Thach G Nguyen, Sai T Chu, Brent E Little, Damien G Hicks, Roberto Morandotti, et al. , “11 tops photonic convolutional accelerator for optical neural networks,”
2021
Later among the works it cites.
Davide Castelvecchi, “Using sound to explore events of the universe,”
2021
Later among the works it cites.
Mario Krenn, Jakob Kottmann, Nora Tischler, and Alán Aspuru-Guzik, “Conceptual understanding through efficient automated design of quantum optical experiments,”
2021
Later among the works it cites.
CMS Collaboration, “Probing effective field theory operators in the associated production of top quarks with a z boson in multilepton final states at s=13 tev,”
2021
Later among the works it cites.
Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, and Philip Harris, “Quasi anomalous knowledge: searching for new physics with embedded knowledge,”
2021
Later among the works it cites.
Matthew D Schwartz, “Modern machine learning and particle physics,”
2021
Later among the works it cites.
Gregor Kasieczka, Benjamin Nachman, David Shih, Oz Amram, Anders Andreassen, Kees Benkendorder, Blaz Bortolato, Gustaaf Broojimans, Florencia Canelli, Jack Collins, et al. , “The lhc olympics 2020: a community challenge for anomaly detection in high energy physics,”
2021
Later among the works it cites.
Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, and Rose Yu, “Automatic symmetry discovery with lie algebra convolutional network,”
2021
Later among the works it cites.
AkshatKumar Nigam, Robert Pollice, Matthew FD Hurley, Riley J Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A Voelz, and Alán Aspuru-Guzik, “Assigning confidence to molecular property prediction,”
2021
Later among the works it cites.
A Davies, P Velickovic, L Buesing, S Blackwell, D Zheng, N Tomasev, R Tanburn, P Battaglia, C Blundell, A Juhasz, et al. , “Advancing mathematics by guiding human intuition with ai,”
2021
Later among the works it cites.
Dashun Wang and Albert-László Barabási, The science of science (Cambridge University Press, 2021)
2021
Later among the works it cites.
Cynthia Shen, Mario Krenn, Sagi Eppel, and Alan Aspuru-Guzik, “Deep molecular dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations,”
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Antonio A Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher E Granade, John G Rarity, Raffaele Santagati, and Anthony Laing, “Learning models of quantum systems from experiments,”
2021
Later among the works it cites.
Gal Raayoni, Shahar Gottlieb, Yahel Manor, George Pisha, Yoav Harris, Uri Mendlovic, Doron Haviv, Yaron Hadad, and Ido Kaminer, “Generating conjectures on fundamental constants with the ramanujan machine,”
2021
Later among the works it cites.
Adam Zsolt Wagner, “Constructions in combinatorics via neural networks,”
2021
Later among the works it cites.
Michael R Douglas, “Machine learning as a tool in theoretical science,”
2022
Closest in time.
Geemi P Wellawatte, Aditi Seshadri, and Andrew D White, “Model agnostic generation of counterfactual explanations for molecules,”
2022
Closest in time.