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Anima Anandkumar | Frederick R. Manby | Thomas F. Miller | Anders S. Christensen | Matthew Welborn | Zhuoran Qiao
[1] Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. , 2020, Journal of chemical theory and computation.
[2] Daniel G A Smith,et al. Psi4 1.4: Open-source software for high-throughput quantum chemistry. , 2020, The Journal of chemical physics.
[3] Alexander D. MacKerell,et al. The BioFragment Database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions. , 2017, The Journal of chemical physics.
[4] Arghya Bhowmik,et al. DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction , 2020, ArXiv.
[5] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[6] J. Delft,et al. A numerical algorithm for the explicit calculation of SU(N) and SL(N,C) Clebsch-Gordan coefficients , 2010, 1009.0437.
[7] Jirí Cerný,et al. Benchmark database of accurate (MP2 and CCSD(T) complete basis set limit) interaction energies of small model complexes, DNA base pairs, and amino acid pairs. , 2006, Physical chemistry chemical physics : PCCP.
[8] Fabian B. Fuchs,et al. SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks , 2020, NeurIPS.
[9] Stefan Grimme,et al. A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1-86). , 2017, Journal of chemical theory and computation.
[10] Jacob D. Durrant,et al. Dimorphite-DL: an open-source program for enumerating the ionization states of drug-like small molecules , 2019, Journal of Cheminformatics.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Stefan Grimme,et al. Implementation of nuclear gradients of range‐separated hybrid density functionals and benchmarking on rotational constants for organic molecules , 2014, J. Comput. Chem..
[13] Xiang Gao,et al. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials , 2020, J. Chem. Inf. Model..
[14] Andrew R. Leach,et al. An open source chemical structure curation pipeline using RDKit , 2020, Journal of Cheminformatics.
[15] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[16] Jan M. L. Martin,et al. The melatonin conformer space: benchmark and assessment of wave function and DFT methods for a paradigmatic biological and pharmacological molecule. , 2013, The journal of physical chemistry. A.
[17] Gurtej Kanwar,et al. Equivariant flow-based sampling for lattice gauge theory , 2020, Physical review letters.
[18] Jeng-Da Chai,et al. Long-Range Corrected Hybrid Density Functionals with Improved Dispersion Corrections. , 2012, Journal of chemical theory and computation.
[19] Weitao Yang,et al. The use of global and local molecular parameters for the analysis of the gas-phase basicity of amines. , 1986, Journal of the American Chemical Society.
[20] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[21] Florian Weigend,et al. Hartree–Fock exchange fitting basis sets for H to Rn † , 2008, J. Comput. Chem..
[22] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[23] Noam Bernstein,et al. Machine learning unifies the modeling of materials and molecules , 2017, Science Advances.
[24] Gurtej Kanwar,et al. Normalizing Flows on Tori and Spheres , 2020, ICML.
[25] Olexandr Isayev,et al. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules , 2017, Scientific Data.
[26] Ta-Pei Cheng,et al. Gauge Theory of elementary particle physics , 1984 .
[27] B. Hall. Quantum Theory for Mathematicians , 2013 .
[28] Alberto Fabrizio,et al. Electron density learning of non-covalent systems , 2019, Chemical science.
[29] Hans W. Horn,et al. Fully optimized contracted Gaussian basis sets for atoms Li to Kr , 1992 .
[30] Multiplicity, invariants, and tensor product decompositions of compact groups , 1996 .
[31] Bing Huang,et al. Quantum machine learning using atom-in-molecule-based fragments selected on the fly , 2017, Nature Chemistry.
[32] Geoffrey Hutchison,et al. Assessing Conformer Energies using Electronic Structure and Machine Learning Methods , 2020 .
[33] Klaus-Robert Müller,et al. Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.
[34] Jan Andzelm,et al. Gaussian Basis Sets for Molecular Calculations , 2012 .
[35] Johannes Klicpera,et al. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules , 2020, ArXiv.
[36] Anders S. Christensen,et al. Operators in quantum machine learning: Response properties in chemical space. , 2018, The Journal of chemical physics.
[37] Alberto Fabrizio,et al. Transferable Machine-Learning Model of the Electron Density , 2018, ACS central science.
[38] Max Welling,et al. Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs , 2020, ICLR.
[39] Frederick R. Manby,et al. entos: A Quantum Molecular Simulation Package , 2019 .
[41] Max Welling,et al. Gauge Equivariant Convolutional Networks and the Icosahedral CNN 1 , 2019 .
[42] Max Welling,et al. Group Equivariant Convolutional Networks , 2016, ICML.
[43] T. H. Dunning. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .
[44] Stefan Grimme,et al. GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions. , 2018, Journal of Chemical Theory and Computation.
[45] C. Bannwarth,et al. B97-3c: A revised low-cost variant of the B97-D density functional method. , 2018, The Journal of chemical physics.
[46] Quoc V. Le,et al. Searching for Activation Functions , 2018, arXiv.
[47] A. I. Molev. Gelfand-Tsetlin bases for classical Lie algebras , 2002 .
[48] Maurice Weiler,et al. A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels , 2020, ICLR.
[49] Stephan Günnemann,et al. Directional Message Passing for Molecular Graphs , 2020, ICLR.
[50] K. Müller,et al. Towards exact molecular dynamics simulations with machine-learned force fields , 2018, Nature Communications.
[51] Yue Gao,et al. Hypergraph Neural Networks , 2018, AAAI.
[52] J. Pople,et al. Self‐Consistent Molecular‐Orbital Methods. IX. An Extended Gaussian‐Type Basis for Molecular‐Orbital Studies of Organic Molecules , 1971 .
[53] Lee-Ping Wang,et al. Geometry optimization made simple with translation and rotation coordinates. , 2016, The Journal of chemical physics.
[54] Anders S. Christensen,et al. Alchemical and structural distribution based representation for universal quantum machine learning. , 2017, The Journal of chemical physics.
[55] F. Weigend,et al. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. , 2005, Physical chemistry chemical physics : PCCP.
[56] Max Welling,et al. E(n) Equivariant Graph Neural Networks , 2021, ICML.
[57] S. Gliske,et al. Algorithms for Computing U(N) Clebsch Gordan Coefficients , 2007 .
[59] Li Li,et al. Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds , 2018, ArXiv.
[60] J. Crabbe,et al. Molecular modelling: Principles and applications , 1997 .
[61] Shuiwang Ji,et al. Spherical Message Passing for 3D Graph Networks , 2021, ArXiv.
[62] Joe Harris,et al. Representation Theory: A First Course , 1991 .
[63] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[64] Risi Kondor,et al. Cormorant: Covariant Molecular Neural Networks , 2019, NeurIPS.
[65] Markus Meuwly,et al. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.
[66] Andreas Hansen,et al. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. , 2017, Physical chemistry chemical physics : PCCP.
[67] Song Bai,et al. Hypergraph Convolution and Hypergraph Attention , 2019, Pattern Recognit..
[68] Rose Yu,et al. Trajectory Prediction using Equivariant Continuous Convolution , 2020, ICLR.
[69] Gurtej Kanwar,et al. Sampling using SU(N) gauge equivariant flows , 2021, Physical Review D.
[70] Max Welling,et al. 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data , 2018, NeurIPS.
[71] Mordechai Kornbluth,et al. SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials , 2021, ArXiv.
[72] M. A. Blanco,et al. Evaluation of the rotation matrices in the basis of real spherical harmonics , 1997 .
[73] David C. Young,et al. Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems , 2001 .
[74] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[75] M. Ratner. Molecular electronic-structure theory , 2000 .
[76] Joan Bruna,et al. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges , 2021, ArXiv.
[77] Michael Gastegger,et al. Equivariant message passing for the prediction of tensorial properties and molecular spectra , 2021, ICML.
[78] Parr,et al. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.
[79] Anima Anandkumar,et al. Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces , 2020, ArXiv.
[80] D. Hartree. The Wave Mechanics of an Atom with a Non-Coulomb Central Field. Part I. Theory and Methods , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.
[81] O. Anatole von Lilienfeld,et al. On the role of gradients for machine learning of molecular energies and forces , 2020, Mach. Learn. Sci. Technol..
[82] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[83] Jos'e Miguel Hern'andez-Lobato,et al. Symmetry-Aware Actor-Critic for 3D Molecular Design , 2021, ICLR.
[84] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[85] Frederick R. Manby,et al. OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features , 2020, The Journal of chemical physics.