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RESEARCH PAPER · Journal of Chemical Theory and Computation · 2020

Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch

Guoqing Zhou, Benjamin T. Nebgen, Nicholas Lubbers, Walter Malone, Anders M. N. Niklasson, Sergei Tretiak

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Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorchOriginalEW-Fisher: A Novel Loss Function for Deep Learning-Based Image Co-Segmentation2OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features242Energetics and optimal molecular packing for singlet fission in BN-doped perylenes: electronic adiabatic state basis screening15Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models11Closeout Report: Machine Learned Effective Hamiltonians for Molecular Properties [Slides]0Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective18The Rise of Neural Networks for Materials and Chemical Dynamics92Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models20Quantum-Based Molecular Dynamics Simulations Using Tensor Cores33Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network16Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics62Improving semiempirical quantum mechanical methods with machine learning4Neural networks4TBMaLT, a flexible toolkit for combining tight-binding and machine learning40Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow20Graph-based quantum response theory and shadow Born–Oppenheimer molecular dynamics8Accelerating the density-functional tight-binding method using graphical processing units12Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation16Computational insight into stability-enhanced systems of anthocyanin with protein/peptide30Shadow energy functionals and potentials in Born–Oppenheimer molecular dynamics9Lightweight and effective tensor sensitivity for atomistic neural networks23Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation9Synergy of semiempirical models and machine learning in computational chemistry22Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the Practice10A lightweight convolutional neural network for automated crack inspection20Generative β-hairpin design using a residue-based physicochemical property landscape3dxtb —An efficient and fully differentiable framework for extended tight-binding24Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs6Susceptibility formulation of density matrix perturbation theory0Diffusion Generative Models for Designing Efficient Singlet Fission Dimers3Molecular properties1Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy73Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives41Enhancing PySCF-based quantum chemistry simulations with modern hardware, algorithms, and Python tools10PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units6GPU-Accelerated Graph-Based Semiempirical Quantum Chemistry0Semiempirical Quantum Chemistry in the Age of ab initio Data and Differentiable Programming: I. Differentiable Molecular Orbital Theory1Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry3High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units0

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  1. EW-Fisher: A Novel Loss Function for Deep Learning-Based Image Co-Segmentation

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  2. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

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  3. Energetics and optimal molecular packing for singlet fission in BN-doped perylenes: electronic adiabatic state basis screening

    Physical Chemistry Chemical Physics · 2021-01-01

    Anurag Singh, Alexander Humeniuk, Merle I. S. Röhr

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  4. Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models

    The Journal of Chemical Physics · 2021-02-01

    Anders M. N. Niklasson

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  5. Closeout Report: Machine Learned Effective Hamiltonians for Molecular Properties [Slides]

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    Benjamin T. Nebgen, USDOE National Nuclear Security Administration (NNSA), Justin S. Smith, Sergei Tretiak, Nicholas Lubbers

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  6. Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

    Journal of Chemical Theory and Computation · 2021-04-02

    Joshua Finkelstein, Justin S. Smith, Susan M. Mniszewski, Kipton M. Barros, Christian F. A. Negre, Emanuel H. Rubensson, Anders M. N. Niklasson

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  7. The Rise of Neural Networks for Materials and Chemical Dynamics

    The Journal of Physical Chemistry Letters · 2021-07-01

    Maksim Kulichenko, Justin S. Smith, Benjamin T. Nebgen, Ying Wai Li, Nikita S. Fedik, Alexander I. Boldyrev, Nicholas Lubbers, Kipton M. Barros, Sergei Tretiak

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  8. Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models

    The European Physical Journal B · 2021-08-01

    Anders M. N. Niklasson

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  9. Quantum-Based Molecular Dynamics Simulations Using Tensor Cores

    Journal of Chemical Theory and Computation · 2021-10-01

    Joshua Finkelstein, Justin S. Smith, Susan M. Mniszewski, Kipton M. Barros, Christian F. A. Negre, Emanuel H. Rubensson, Anders M. N. Niklasson

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  10. Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

    Journal of Chemical Theory and Computation · 2022-06-07

    Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski, Christian F. A. Negre, Anders M. N. Niklasson

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  11. Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

    Proceedings of the National Academy of Sciences · 2022-07-01

    Guoqing Zhou, Nicholas Lubbers, Kipton M. Barros, Sergei Tretiak, Benjamin T. Nebgen

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  12. Improving semiempirical quantum mechanical methods with machine learning

    Elsevier eBooks · 2022-09-23

    Pavlo O. Dral, Tetiana A. Zubatiuk

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  13. Neural networks

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    Pavlo O. Dral, Alexei A. Kananenka, Fuchun Ge, Bao-Xin Xue

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  14. TBMaLT, a flexible toolkit for combining tight-binding and machine learning

    The Journal of Chemical Physics · 2023-01-02

    Adam McSloy, Guozheng Fan, Wenbo Sun, Christian Hölzer, Marvin Friede, Sebastian Ehlert, Nils-Erik Schütte, Stefan Grimme, Thomas Frauenheim, Bálint Aradi

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  15. Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

    The Journal of Chemical Physics · 2023-01-16

    Francesco Bosia, Peikun Zheng, Alain C. Vaucher, Thomas Weymuth, Pavlo O. Dral, Markus Reiher

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  16. Graph-based quantum response theory and shadow Born–Oppenheimer molecular dynamics

    The Journal of Chemical Physics · 2023-01-30

    Christian F. A. Negre, Michael E. Wall, Anders M. N. Niklasson

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  17. Accelerating the density-functional tight-binding method using graphical processing units

    The Journal of Chemical Physics · 2023-02-07

    Van‐Quan Vuong, Caterina Cevallos, Ben Hourahine, Bálint Aradi, Jacek Jakowski, Stephan Irle, Cristopher Camacho

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  18. Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation

    The Journal of Chemical Physics · 2023-02-17

    Rodrigo A. Vargas–Hernández, Kjell Jorner, Robert Pollice, Alán Aspuru‐Guzik

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  19. Computational insight into stability-enhanced systems of anthocyanin with protein/peptide

    Food Chemistry Molecular Sciences · 2023-02-21

    Xing Cheng, Pei Chen, Lei Zhang

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  20. Shadow energy functionals and potentials in Born–Oppenheimer molecular dynamics

    The Journal of Chemical Physics · 2023-04-17

    Anders M. N. Niklasson, Christian F. A. Negre

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  21. Lightweight and effective tensor sensitivity for atomistic neural networks

    The Journal of Chemical Physics · 2023-05-09

    Michael Chigaev, Justin S. Smith, Steven Anaya, Benjamin T. Nebgen, Matthew Tyler Bettencourt, Kipton M. Barros, Nicholas Lubbers

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  22. Semi-Empirical Shadow Molecular Dynamics: A PyTorch Implementation

    Journal of Chemical Theory and Computation · 2023-05-10

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  23. Synergy of semiempirical models and machine learning in computational chemistry

    The Journal of Chemical Physics · 2023-09-15

    Nikita S. Fedik, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Maksim Kulichenko, Ying Wai Li, R.I. Zubatyuk, Richard A. Messerly, Olexandr Isayev, Sergei Tretiak

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  24. Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the Practice

    ACM Transactions on Software Engineering and Methodology · 2023-12-21

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  25. A lightweight convolutional neural network for automated crack inspection

    Construction and Building Materials · 2024-01-31

    Siwei Chang, Bowen Zheng

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  26. Generative β-hairpin design using a residue-based physicochemical property landscape

    Biophysical Journal · 2024-02-01

    Vardhan B. Satalkar, Gemechis D. Degaga, Wei Li, Yui Tik Pang, Andrew C. McShan, James C. Gumbart, Julie C. Mitchell, Matthew P. Torres

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  27. dxtb —An efficient and fully differentiable framework for extended tight-binding

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  28. Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

    Journal of Chemical Theory and Computation · 2024-08-13

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  29. Susceptibility formulation of density matrix perturbation theory

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  31. Molecular properties

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  32. Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy

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  33. Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives

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  34. Enhancing PySCF-based quantum chemistry simulations with modern hardware, algorithms, and Python tools

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  35. PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units

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  36. GPU-Accelerated Graph-Based Semiempirical Quantum Chemistry

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  37. Semiempirical Quantum Chemistry in the Age of ab initio Data and Differentiable Programming: I. Differentiable Molecular Orbital Theory

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  38. Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry

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  39. High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

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