Simulation vs Understanding A Tension, in Quantum Chemistry and Beyond. PART B The March of Simulation, for Better or Worse.

In the second part of this essay, we leave philosophy, simply describing Roald's being trashed by simulation. This leads us to a general sketch of artificial intelligence (AI), Searle's Chinese room, and Strevens' account of what a go-playing program knows. Back to our terrain -- we ask "Quantum Chemistry, † ca. 2020?" Then move to examples of Big Data, machine learning and neural networks in action, first in chemistry and then affecting social matters. trivial to scary. We argue that moral decisions are hardly to be left to a computer. And that causes are so much deeper than correlations.

[1]  N. Bohr.  The Quantum Postulate and the Recent Development of Atomic Theory , 1928, Nature.

[2]  Niels Bohr,et al.  Atomic Theory and the Description of Nature , 1934 .

[3]  P. Guyer.  Kant and the Claims of Taste , 1979 .

[4]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[5]  F L Holmes,et al.  Scientific Writing and Scientific Discovery , 1987, Isis.

[6]  John R. Searle,et al.  Minds, brains, and programs , 1980, Behavioral and Brain Sciences.

[7]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[8]  Roald Hoffmann,et al.  Representation in Chemistry , 1989 .

[9]  R. Hoffmann,et al.  Darstellungen in der Chemie — die Sprache der Chemiker , 1991 .

[10]  S. Goedecker.  Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems. , 2004, The Journal of chemical physics.

[11]  Richard Dronskowski,et al.  Computational Chemistry of Solid State Materials , 2005 .

[12]  Shawn T. Brown,et al.  Advances in methods and algorithms in a modern quantum chemistry program package. , 2006, Physical chemistry chemical physics : PCCP.

[13]  Erratum: Structures and Potential Superconductivity inSiH4at High Pressure: En Route to “Metallic Hydrogen” [Phys. Rev. Lett.96, 017006 (2006)] , 2006 .

[14]  John Cowan.  Third thoughts , 2006, Br. J. Educ. Technol..

[15]  Chris J Pickard,et al.  High-pressure phases of silane. , 2006, Physical review letters.

[16]  Roald Hoffmann,et al.  Structures and Potential Superconductivity in SiH~4 at High Pressure: En Route to "Metallic Hydrogen" , 2006 .

[17]  Roald Hoffmann,et al.  The chemical imagination at work in very tight places. , 2007, Angewandte Chemie.

[18]  Sason Shaik,et al.  A Chemist's Guide to Valence Bond Theory , 2007 .

[19]  R. Hoffmann,et al.  Chemie unter hchsten Drcken: eine Herausforderung fr die chemische Intuition , 2007 .

[20]  J. C. Schön,et al.  Structure prediction based on ab initio simulated annealing , 2008 .

[21]  Laura Gagliardi,et al.  The restricted active space followed by second-order perturbation theory method: theory and application to the study of CuO2 and Cu2O2 systems. , 2008, The Journal of chemical physics.

[22]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[23]  Ken E. Whelan,et al.  The Automation of Science , 2009, Science.

[24]  P. Siegbahn.  Structures and energetics for O2 formation in photosystem II. , 2009, Accounts of chemical research.

[25]  R. Arkin.  The Case for Ethical Autonomy in Unmanned Systems , 2010 .

[26]  Stefan Goedecker,et al.  Crystal structure prediction using the minima hopping method. , 2010, The Journal of chemical physics.

[27]  David C. Lonie,et al.  XtalOpt: An open-source evolutionary algorithm for crystal structure prediction , 2011, Comput. Phys. Commun..

[28]  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 .

[29]  Alán Aspuru-Guzik,et al.  From computational discovery to experimental characterization of a high hole mobility organic crystal , 2011, Nature communications.

[30]  Alán Aspuru-Guzik,et al.  Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics , 2011 .

[31]  Chris J Pickard,et al.  Ab initio random structure searching , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.

[32]  Alán Aspuru-Guzik,et al.  The Harvard Clean Energy Project. Large-scale computational screening and design of molecular motifs for organic photovoltaics on the World Community Grid , 2011 .

[33]  J. Malrieu,et al.  A Strategy to Determine Appropriate Active Orbitals and Accurate Magnetic Couplings in Organic Magnetic Systems. , 2012, Journal of chemical theory and computation.

[34]  R. Carlson,et al.  On Hype, Malpractice, and Scientific Misconduct in Organic Synthesis , 2012 .

[35]  M. Rupp,et al.  Machine learning of molecular electronic properties in chemical compound space , 2013, 1305.7074.

[36]  K. Yoshizawa.  Quantum Chemical Studies on Dioxygen Activation and Methane Hydroxylation by Diiron and Dicopper Species as well as Related Metal–Oxo Species , 2013 .

[37]  Alán Aspuru-Guzik,et al.  Chapter 17 – Organic Photovoltaics , 2013 .

[38]  M. Lewis.  Flash Boys: A Wall Street Revolt , 2014 .

[39]  Alán Aspuru-Guzik,et al.  Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project , 2014 .

[40]  Frank Neese,et al.  Electronic structure of the oxygen-evolving complex in photosystem II prior to O-O bond formation , 2014, Science.

[41]  Tim Mueller,et al.  Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning , 2014 .

[42]  K. Müller,et al.  Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space , 2015, The journal of physical chemistry letters.

[43]  E. Zurek,et al.  Predicting crystal structures and properties of matter under extreme conditions via quantum mechanics: the pressure is on. , 2015, Physical chemistry chemical physics : PCCP.

[44]  Anders S. G. Andrae,et al.  On Global Electricity Usage of Communication Technology: Trends to 2030 , 2015 .

[45]  Alán Aspuru-Guzik,et al.  The Harvard organic photovoltaic dataset , 2016, Scientific Data.

[46]  F. Arnold,et al.  Directed evolution of cytochrome c for carbon–silicon bond formation: Bringing silicon to life , 2016, Science.

[47]  Burak Himmetoglu,et al.  Tree based machine learning framework for predicting ground state energies of molecules. , 2016, The Journal of chemical physics.

[48]  Markus Reiher,et al.  Automated Selection of Active Orbital Spaces. , 2016, Journal of chemical theory and computation.

[49]  Rampi Ramprasad,et al.  Machine Learning Force Fields: Construction, Validation, and Outlook , 2016, 1610.02098.

[50]  F. Arnold,et al.  Highly Stereoselective Biocatalytic Synthesis of Key Cyclopropane Intermediate to Ticagrelor. , 2016, ACS catalysis.

[51]  Luciano Floridi.  What a maker’s knowledge could be , 2016, Synthese.

[52]  Alireza Khorshidi,et al.  Amp: A modular approach to machine learning in atomistic simulations , 2016, Comput. Phys. Commun..

[53]  Li Li,et al.  Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.

[54]  Klaus-Robert Müller,et al.  Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.

[55]  Oren Etzioni,et al.  Pros and Cons of Autonomous Weapons Systems , 2017 .

[56]  Noam Bernstein,et al.  Machine learning unifies the modeling of materials and molecules , 2017, Science Advances.

[57]  Alán Aspuru-Guzik,et al.  Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics , 2017 .

[58]  J S Smith,et al.  ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.

[59]  S. Noble.  Algorithms of Oppression: How Search Engines Reinforce Racism , 2018 .

[60]  Sam Lemonick.  Machine learning offers fast, accurate calculations , 2018 .

[61]  Boris Maryasin,et al.  Maschinelles Lernen für die organische Synthese: Ersetzen Roboter Chemiker? , 2018 .

[62]  Alexandre Tkatchenko,et al.  Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning. , 2017, The Journal of chemical physics.

[63]  QBism , 2018, The World According to Quantum Mechanics.

[64]  K. Pernal,et al.  Correlation Energy from the Adiabatic Connection Formalism for Complete Active Space Wave Functions. , 2018, Journal of chemical theory and computation.

[65]  A.W.G. de Vries.  Bitcoin's Growing Energy Problem , 2018 .

[66]  R. Hoffmann,et al.  Coarctate and Möbius: The Helical Orbitals of Allene and Other Cumulenes , 2018, ACS central science.

[67]  Florian Sittel,et al.  Machine Learning of Biomolecular Reaction Coordinates. , 2018, The journal of physical chemistry letters.

[68]  Thomas F. Miller,et al.  Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. , 2018, Journal of chemical theory and computation.

[69]  O. Anatole von Lilienfeld,et al.  Quantum Machine Learning im chemischen Raum , 2018 .

[70]  Garry Kasparov,et al.  Chess, a Drosophila of reasoning , 2018, Science.

[71]  Hany Farid,et al.  The accuracy, fairness, and limits of predicting recidivism , 2018, Science Advances.

[72]  Alán Aspuru-Guzik,et al.  The Matter Simulation (R)evolution , 2018, ACS central science.

[73]  O. Anatole von Lilienfeld,et al.  Quantum Machine Learning in Chemical Compound Space , 2018 .

[74]  M. Kosinski,et al.  Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images , 2018, Journal of personality and social psychology.

[75]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[76]  Philipp Marquetand,et al.  Machine Learning for Organic Synthesis: Are Robots Replacing Chemists? , 2018, Angewandte Chemie.

[77]  K. Yoshizawa,et al.  Theoretical Overview of Methane Hydroxylation by Copper-Oxygen Species in Enzymatic and Zeolitic Catalysts. , 2018, Accounts of chemical research.

[78]  Meredith Broussard,et al.  Artificial Unintelligence: How Computers Misunderstand the World , 2018 .

[79]  Sam Lemonick.  Machine learning predicts electron energies , 2018 .

[80]  Zachary Wu,et al.  Machine learning-assisted directed protein evolution with combinatorial libraries , 2019, Proceedings of the National Academy of Sciences.

[81]  Alberto Fabrizio,et al.  Electron density learning of non-covalent systems , 2019, Chemical science.

[82]  Joel Z. Leibo,et al.  Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research , 2019, ArXiv.

[83]  Luciano Floridi,et al.  What the Near Future of Artificial Intelligence Could Be , 2019, Philosophy & Technology.

[84]  A. Elgammal.  AI Is Blurring the Definition of Artist , 2019, American Scientist.

[85]  Intelligence May Not Be Computable , 2019, American Scientist.

[86]  F. Neese,et al.  Chemistry and Quantum Mechanics in 2019: Give Us Insight and Numbers , 2019, Journal of the American Chemical Society.

[87]  Jaci Wilkinson.  Artificial Unintelligence , 2019, Journal of Web Librarianship.

[88]  S. Shaik,et al.  Fenton-Derived OH Radicals Enable the MPnS Enzyme to Convert 2-Hydroxyethylphosphonate to Methylphosphonate: Insights from Ab Initio QM/MM MD Simulations. , 2019, Journal of the American Chemical Society.

[89]  Ankur Taly,et al.  Using attribution to decode binding mechanism in neural network models for chemistry , 2018, Proceedings of the National Academy of Sciences.

[90]  Pieter P. Plehiers,et al.  A robotic platform for flow synthesis of organic compounds informed by AI planning , 2019, Science.

[91]  William A. Goddard,et al.  Density functional theory based neural network force fields from energy decompositions , 2019, Physical Review B.

[92]  Anand Chandrasekaran,et al.  Solving the electronic structure problem with machine learning , 2019, npj Computational Materials.

[93]  N. David Mermin,et al.  Can the scientist play a role in the laws of physics? , 2019, Physics Today.