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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231109T110000
DTEND;TZID=America/Detroit:20231109T130000
DTSTAMP:20231115T151403Z
CREATED:20231102T175811Z
LAST-MODIFIED:20231115T151403Z
UID:10000661-1699527600-1699534800@micde.umich.edu
SUMMARY:SciML Webinar: Lenz Fiedler - Efficient calculations of electronic structures with machine-learning models
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Lenz Fiedler (Helmholtz-Zentrum Dresden-Rossendorf)\nSession Chair: Michael Herbst (EPFL) \nAbstract: Quantum mechanical calculations of the electronic structure of matter enable accessing interesting thermodynamical properties without the need for prior experimental measurements. Therefore\, electronic structure calculations are of great interest in fields such as materials discovery or drug design. At the forefront of such simulations lies density functional theory (DFT)\, due to its excellent balance between computational accuracy and efficiency. Yet\, as pressing environmental and social issues shift the research focus to increasingly complicated systems and conditions\, even the most efficient of DFT implementations are approaching their limitations in terms of computational feasibility. A possible route to enable more complex calculations lies with machine learning (ML)\, i.e.\, algorithms that are capable of capturing complicated relationships based on large amounts of data.\nIn this talk\, Lenz Fiedler will talk about current contributions of Center for Advanced Systems Understanding\, Helmholtz-Zentrum Dresden-Rossendorf (CASUS) w.r.t. building ML models that replace conventional DFT calculations. More precisely\, Lenz will talk about the current state of the Materials Learning Algorithms library (MALA)\, which allows easy training and inference for ML-DFT models that are developed by CASUS in cooperation with Sandia National Laboraties and Oak Ridge National Laboratory. In contrast to comparable frameworks\, MALA allows full access to the electronic structure of compounds\, including volumetric data as well as scalar quantities of interest\, such as energies. It will be shown how MALA models can operate efficiently across phase boundaries\, length scales and temperature ranges.
URL:https://micde.umich.edu/event/sciml-webinar-lenz-fiedler-efficient-calculations-of-electronic-structures-with-machine-learning-models/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231026T110000
DTEND;TZID=America/Detroit:20231026T130000
DTSTAMP:20231107T231334Z
CREATED:20231017T170318Z
LAST-MODIFIED:20231107T231334Z
UID:10000658-1698318000-1698325200@micde.umich.edu
SUMMARY:SciML Webinar: Bowen Deng - CHGNet: pretrained universal interatomic potential to study electron coupled ionic systems.
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Bowen Deng (UC Berkeley)\nSession Chair: Sakidja Ridwan (Missouri State University) \nAbstract: Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modeling. Although classical force fields often fail to describe the\ncoupling between electronic states and ionic rearrangements\, the more accurate ab-initio molecular dynamics suffers from computational complexity that prevents long-time and large-\nscale simulations\, which are essential to study technologically relevant phenomena. Our work presents the Crystal Hamiltonian Graph Neural Network (CHGNet)\, a graph-neural-\nnetwork-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies\, forces\, stresses\, and magnetic moments\nfrom the Materials Project Trajectory Dataset\, which consists of over 10 years of density functional theory calculations of ∼ 1.5 million inorganic structures. The explicit inclusion of\nmagnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons\, enhancing its capability to describe both atomic and electronic degrees of freedom.\nWe demonstrate several applications of CHGNet in solid-state materials and energy storage applications.
URL:https://micde.umich.edu/event/sciml-webinar-bowen-deng/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231019T110000
DTEND;TZID=America/Detroit:20231019T130000
DTSTAMP:20231025T194805Z
CREATED:20230915T150343Z
LAST-MODIFIED:20231025T194805Z
UID:10000646-1697713200-1697720400@micde.umich.edu
SUMMARY:SciML Webinar Ji Qi: DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust Training of Machine Learning Interatomic Potentials
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Ji Qi (UC San Diego and LLNL)\nSession Chair: Daniel Schwalbe-Koda (UC Los Angeles) \nAbstract: Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond conventional first-principles approaches\, and they have played increasingly important roles in understanding and design of materials. However\, MLIPs are only as accurate and robust as the data they are trained on. In this seminar\, I will present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements\, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive ab initio MD to rapidly create a large configuration space for target materials systems. For demonstration\, we combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. \nIn this seminar\, I will walk through two Jupiter notebooks to showcase DIRECT sampling with the two example cases demonstrated in our manuscript\, so that audience can expect to reproduce our major results with no trouble. Hopefully\, DIRECT sampling will serve as a straightforward\, efficient\, useful plug-in for the robust training of MLIPs across any compositional complexity.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-ji-qi-2/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231012T110000
DTEND;TZID=America/Detroit:20231012T130000
DTSTAMP:20231018T173554Z
CREATED:20230915T150330Z
LAST-MODIFIED:20231018T173554Z
UID:10000644-1697108400-1697115600@micde.umich.edu
SUMMARY:SciML Webinar Justin Beroz: A closed-form mathematical framework for modeling turbulent fluids
DESCRIPTION:Speaker: Justin Beroz (ReynKo Inc.) \n\nSession Chair: Varun Shankar (Physics Inverted Mataerials) \nAbstract: Despite significant advances over the past two centuries\, a complete general mathematical framework for turbulent fluid motion has yet to be put forth\, and remains the longest standing unsolved problem in classical physics. I will present such a framework\, which is based on constructing a spectral decomposition for the fluid’s kinetic energy from first principles. The approach departs from the usual Reynolds decomposition and yields a set of closed and solvable ordinary differential equations in matrix form. Within this prescription\, the linear terms in the Navier-Stokes equations correspond to a symmetric matrix operator\, and the nonlinear convective term enters as an anti-symmetric operator that provides coupling between eigenstates of turbulent fluctuation. Specifically\, I will present a derivation for the turbulent energy spectrum\, including the Kolmogorov energy cascade; elucidate instability mechanisms for the transition to turbulence;  and detail the analytical solution for turbulence in a box. Careful attention will be given to the physical picture and scaling\, in addition to the rigorous mathematical program. The talk will conclude with a forward look into current efforts implementing the model into a numerical simulation within my company\, ReynKo Inc.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-justin-beroz/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231005T110000
DTEND;TZID=America/Detroit:20231005T123000
DTSTAMP:20231018T165056Z
CREATED:20230918T024126Z
LAST-MODIFIED:20231018T165056Z
UID:10000649-1696503600-1696509000@micde.umich.edu
SUMMARY:SciML Webinar: Jianke Yang - Generative Adversarial Symmetry Discovery
DESCRIPTION:Speaker: Jianke Yang (UC San Diego) \n\n\nSession Chair: Bharath Ramsundar (Deep Forest Sciences) \nAbstract:Despite the success of equivariant neural networks in scientific applications\, they require knowing the symmetry group a priori. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data.  We propose a framework\, LieGAN\, to automatically discover equivariances from a dataset using a paradigm akin to generative adversarial training. Specifically\, a generator learns a group of transformations applied to the data\, which preserves the original distribution and fools the discriminator. LieGAN represents symmetry as an interpretable Lie algebra basis and can discover various symmetries such as the rotation group and the restricted Lorentz group in trajectory prediction and top-quark tagging tasks. More generally\, LieGAN can also be extended to discover the nonlinear symmetries in high-dimensional dynamics. The learned symmetry can be readily used in several existing equivariant neural networks to improve prediction accuracy and generalization. It can also improve the symbolic equation discovery and long-term forecasting for various dynamical systems. \n\n\n 
URL:https://micde.umich.edu/event/sciml-webinar-jianke-yang-generative-adversarial-symmetry-discovery/
CATEGORIES:Sciml,SciML Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230928T110000
DTEND;TZID=America/Detroit:20230928T123000
DTSTAMP:20231018T163755Z
CREATED:20230918T023335Z
LAST-MODIFIED:20231018T163755Z
UID:10000648-1695898800-1695904200@micde.umich.edu
SUMMARY:SciML Webinar: Bob Carpenter - Multiscale Generalized Hamiltonian Monte Carlo with Delayed Rejection
DESCRIPTION:Speaker: Bob Carpenter (Flatiron Institute) \n\n\nSession Chair: Sam Livingstone (University College London) \n\n\nAbstract: In this talk\, I will demonstrate how we can combine two ideas\, generalized Hamiltonian Monte Carlo and delayed rejection\, to derive a sampler that is as efficient as Hamiltonian Monte Carlo\, but is able to adapt its step size to deal with multiscale distributions\, much like a standard integrator for ordinary differential equations. A distribution is multiscale if its curvature has different scales in the posterior; a textbook example is Radford Neal’s funnel example derived from hierarchical priors\, which has a very flat mouth (corresponding to high population variance) and very highly curved neck (low population variance). No fixed step size allows exploration of its posterior. Generalized HMC allows us to take a single Hamiltonian step along the gradient at a time (which is equivalent to Metropolis-adjusted Langevin dynamics)\, but only refresh momentum partially (which makes it underdamped). The naive form of this algorithm does not work because momentum must be reversed to maintain detailed balance if the Metropolis step rejects. To maintain directed exploration\, we apply delayed rejection\, which allows a proposal rejected due to divergence of the Hamiltonian (from too large a step size in the first-order approximation of the dynamics) to be retried with a smaller step size (with a Hastings-style adjustment for the retry). We show that the combination of delayed rejection and GHMC allows sampling multiscale distributions which otherwise lead to biased samples in standard Hamiltonian Monte Carlo (including dynamic forms such as the no-U-turn sampler). In conclusion\, I will discuss some preliminary work on applying the the automatic tuning method using complementary parallel chains developed by Matt Hoffman and Pavel Sountsov for their sampler MEADS (which also uses generalized HMC\, but with an alternative approach to maintaining directed exploration based on work of Radford neal\, which will also describe). \n\nSlides: https://statmodeling.stat.columbia.edu/wp-content/uploads/2023/09/carpenter-sciml-webinar-2023.pdf
URL:https://micde.umich.edu/event/sciml-webinar-bob-carpenter-multiscale-generalized-hamiltonian-monte-carlo-with-delayed-rejection/
CATEGORIES:Sciml,SciML Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230921T110000
DTEND;TZID=America/Detroit:20230921T123000
DTSTAMP:20231018T162319Z
CREATED:20230914T172942Z
LAST-MODIFIED:20231018T162319Z
UID:10000643-1695294000-1695299400@micde.umich.edu
SUMMARY:SciML Webinar: David Ruhe - Geometric Clifford Algebra Networks
DESCRIPTION:Speaker: David Ruhe (University of Amsterdam) \n\n\nSession Chair: Erik Bekkers (University of Amsterdam) \n\nAbstract: In this talk\, I will present Clifford Group Equivariant Neural Networks\, an innovative method for building E(n)-equivariant networks based on Clifford (geometric) algebras. First\, I will give an introduction to the Clifford algebra and its geometric applications. Then\, I will introduce the Clifford group and how it always acts through the orthogonal group. As such\, a parameterization that is equivariant to the Clifford group will automatically be equivariant to the orthogonal group of\, e.g.\, rotations and reflections. We show that any polynomial (under the algebra’s geometric product) is such a parameterization.  We propose several layers from these insights and conduct experiments in three-\, four-\, and five-dimensional spaces. One of these experiments even includes equivariance to the nondefinite O(1\,3) Lorentz group from the same code implementation. Finally\, I will provide guidance on how to utilize our codebase for implementing these algorithms.
URL:https://micde.umich.edu/event/sciml-webinar-david-ruhe-geometric-clifford-algebra-networks/
CATEGORIES:SciML Webinar Series
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