Scientific Machine Learning Webinar Series

This webinar series and panel events are organized by Keith Phuthi, Varun Shankar and Venkat Viswanathan with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.

Webinar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions. Invited session chairs will guide the discussion along with offering their perspective on the field. The Q&A session is typically very interactive with a small group of enthusiastic audience.

Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time

How To Join

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Webinar ID: 992 4479 8052
Passcode: 919401

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Upcoming/Ongoing Events

Fall 2022:

• September 22: Batatia Ilyes (ENS Paris Saclay)
  MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
  Session Chair: Mario Geiger (Massachusetts Institute of Technology)
• September 29: Alby Musaelian and Simon Batzner (Harvard University)
  Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
  Session Chair:
• October 6: Yongji Wang (Princeton University)
  Solving 3-D Euler via PINNs
  Session Chair: Lu Lu (University of Pennsylvania)
• October 20: Jan Weinreich (University of Vienna)
  Ab-initio machine learning of phase space averages
  Session Chair: Stephan Heinen (University of Vienna)
• October 27: Darshil Doshi (Brown University)
  Critical Initialization of Deep Neural Networks using Jacobians
  Session Chair:

Past Webinars

Generative Models:

• July 14: Youzhi Luo (Texas A&M)
  An Autoregressive Flow Model for 3D Molecular Geometry Generation
  Session Chair: Jian Tang (Mila-Quebec AI Institute)
• July 28: Yi Liu (Texas A&M)
  Complete and Efficient Graph AI Techniques for Molecular Sciences
  Session Chair: Wengong Jin (Broad Institute)
• August 4: Yuelin Wang and Kai Yi (Shanghai Jiao Tong University)
  ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition
  Session Chair: Jia Zhao (Utah State University)
• August 18: Niklas Gebauer (TU Berlin)
  Inverse design of 3d molecular structures with conditional generative neural networks
  Session Chair: Gregor Simm (Microsoft Research)

Differentiable Physics:

• February 24: Giuseppe Romano (MIT)
  Differentiable Phonon Simulations to Optimize Thermal Transport in Nanostructures
  Session Chair: Ján Drgoňa (PNNL)
• March 3: Patrick Kidger (University of Oxford)
  On Neural Differential Equations
  Session Chair: David Duvenaud (University of Toronto)
• March 10: Joe Greener (MRC Laboratory of Molecular Biology)
  Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
  Session Chair: Sam Schoenholz (Google Brain)
• March 17: Rafael Gomez-Bombarelli (MIT)
  Differentiable Uncertainty
  Session Chair: Olexandr Isayev (CMU)
• March 24: Desmond Zhong (Siemens Technology)
  Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
  Session Chair: Rachel Kurchin (CMU)
• March 31: Taylor Howell & Simon Le Cleac'h (Stanford)
  DOJO - differentiable rigid-body-dynamics
  Session Chair: Vikas Sindhwani (Google Brain)
• April 21: Akshay Agrawal
  Networks with Differentiable Contact Models
  Session Chair: Zac Manchester (CMU)

Symmetries, Physical Systems and Machine Learning:

• October 14: Rui Wang (UCSD) and Robin Walters (Northeastern)
  Incorporating Symmetry for Improved Generalization in Dynamics Prediction
  Session Chair: Soledad Villar (Johns Hopkins University)
• October 28: Johannes Brandstetter (Johannes Kepler University Linz)
  Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
  Session Chair: Stephan Günnemann (Technical University of Munich)
• November 4: Yu Wang (MIT)
  Geometric Operators for Shape Analysis
  Session Chair: Rana Hanocka (University of Chicago)
• November 18: Pim de Haan (Qualcomm)
  Natural Message Passing
• December 2: Carlos Esteves (Google)
  Towards Efficient Spherical NNs
  Session Chair: Manzil Zaheer (Google Research)
• January 6: Ziming Liu (MIT)
  Machine Learning Symmetries for Conservation Laws
  Session Chair: Sam Vinko (University of Oxford)
• January 13: Zhuoran Qiao (Caltech)
  Geometric Learning for Quantum-Chemistry-Informed Representations
  Session Chair: Peetak Mitra (PARC)
• January 20: Ferran Alet (MIT)
  Learning to Encode and Discover Physics-Based Inductive Biases
  Session Chair: Bharath Ramsundar (Deep Forest Sciences)

Machine Learning Potentials and Force Fields for Materials Chemistry:

• August 19: Pratyush Tiwary, University of Maryland
  From Atoms to Mechanisms with State Predictive Information Bottleneck and Denoising Diffusion Proabilistic Models
  Session Chair: Matthias Rupp, University of Konstanz
• August 26: Sam Schoenholz , Google Brain
  JAX-MD: A Framework for Differentiable Molecular Dynamics
  Session Chair: Michael Brenner, Harvard University
• September 2: Linfeng Zhang, Princeton University
  Learning-Assisted Molecular Modeling: From Methodology Development ot Engineering Effort
  Session Chair: Tess Smidt, Massachusetts Institute of Technology
• September 9: Simon Batzner, Harvard University
  Neural Equivariant Interatomic Potentials
  Session Chair: Matti Hellström, Software for Chemistry and Materials
• September 16: Alice Allen and Dávid Péter Kóvacs, University of Cambridge
  Linear Body-Ordered Molecular Force Fields
  Session Chair: Albert Bartók-Pártay, University of Warwick
• September 23: Andrea Grisafi, École Polytechnique Fédérale de Lausanne
  Symmetry-Adapted and Long-Range Representations in Atomic-scale ML
  Session Chair: Kieron Burke, University of California Irvine
• September 30: Muhammad Firmansyah Kasim and Sam Vinko, University of Oxford
  Differentiable Quantum Chemistry
  Session Chair: Ekin Dogus Cubuk, Google Brain

Machine Learning meets Information Theory and Statistical Mechanics:

• July 8: Alex Alemi, Google Research
  Machine Learning and Thermodynamics
  Session Chair: Max Welling, University of Amsterdam
• July 15: Pratik Chaudhri, University of Pennsylvania
  (Towards the) Foundations of Small Data
  Session Chair: Karthik Duraisamy, University of Michigan
• July 22: Sho Yaida, Facebook
  Model Complexity from Macroscopic Perspective
  Session Chair: Jascha Sohl Dickstein, Google Brain
• July 29: Yasaman Bahri, Google Brain
  Dynamics and Phase Transitions in Wide, Deep Neural Networks
  Session Chair: Surya Ganguli, Stanford
• Aug 5: Elena Agliari, Sapienza University of Rome
  Learning From Storing
  Session Chair: Jascha Shol-Dickstein, Google Brain

Machine Learning in Fluid Dynamics:

• May 20: Filipe de Avila Belbute-Peres, Carnegie Mellon University
  Combining PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
  Session Chair: Karthik Kashinath, Berkeley Lab
• May 27: Joseph Bakarji, University of Washington
  Data-driven Discovery of Differential Equations for Complex Models in Fluids
  Session Chair: Julia Ling, Alphabet
• June 3: Pedro M. Milani, Exponent
  ML Approaches to Learning Turbulent Mixing in Film Cooling Flows
  Session Chair: Gavin Portwood, LLNL
• June 10: Zongyi Li, Caltech
  Neural Operator: Learning Maps Between Function Spaces
  Session Chair: Sanjay Choudhry, NVIDIA
• June 17: Ameya D. Jagtap, Brown University
  A Generalized Space-Time Domain based Extended PINN for PDEs: Method and Implementation
  Session Chair: Rose Yu, UCSD
• June 24: Dmitrii Kochkov, Google
  Machine Learning Accelerated Computational Fluid Dynamics
  Session Chair: Themistoklis Sapsis, MIT
• July 1: Pierre Baque, Neural Concept
  Session Chair: Andrea Panizza, Baker Hughes

VC Panel on Quantum Computing (May 11th, Tuesday at 2 pm Pacific Time):

• Moderator: Sridhar Tayur, Carnegie Mellon University
• Carly Anderson, Prime Movers Lab
• Chris Boshuizen, DCVC
• Russ Wilcox, Pillar VC

Quantum Machine Learning:

The organizers would like to thank Jarrod McLean (Google) and Zlatko K. Minev (IBM) for suggestions of speakers and session chairs.

• April 15: Hsin-Yuan (Robert) Huang , Caltech
  Characterizing Quantum Advantage in Machine Learning
  Session Chair: Kristan Temme, Institute for Quantum Information and Matter
• April 22: Ian Convy , UC Berkeley
  Session Chair: Miles Stoudenmire, Flatiron Institute
• April 29: Andrea Skolik, Volkswagen Data Lab and Leiden University
  Session Chair: Maria Schuld, Xanadu and University of KwaZulu-Natal
• May 6: Alba Cervera Lierta, University of Toronto
  Session Chair: Glen Evenbly, Georgia Institute of Technology
• May 13: Michael Broughton, Google
  Session Chair: Max Radin, Zapata Computing

Deployment of ML in the Industry:

• Mar 11: Melanie Senn and Alina Negoita, Innovation Center California of Volkswagen Group of America
  High-Throughput Screening Framework for Battery Materials Design
  Session Chair: Shailendra Kaushik, General Motors
• Mar 18: Austin Sendek, Aionics, Inc.
  Aionics: Harnessing ML to supercharge battery discovery, design, and deployment in industry
  Session Chair: Venkat Viswanathan, Carnegie Mellon University
• Mar 25: Payel Das, IBM Thomas J Watson Research Center
  Trustworthiness in AI for Accelerating Discovery
  Session Chair: Isidoros Doxas, Northrop Grumman Mission Systems
• Apr 1: Aniruddha Mukhopadhyay, ANSYS, Inc.
  Shifting Landscape in ML-Driven Product Engineering
  Session Chair: Vivek Singh, NVIDIA
• Apr 8: Keith Task, BASF
  Data Science for Chemicals and Materials Development at BASF
  Session Chair: Amra Peles, Pacific Northwest National Laboratory

Molecular ML for Drug Discovery:

• Feb 11: Wengong Jin, Massachusetts Institute of Technology
  Graph Neural Networks and Generative Models for Drug Discovery
  Session Chair: Alex Wiltschko, Google Research
• Feb 18: Bharath Ramsundar and Seyone Chithrananda
  ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
  Session Chair: Tom Miller, Caltech and Entos, Inc.
• Feb 25: Dominik Lemm
  Energy-Free Machine Learning Predictions of Ab Initio Structures
• March 4: Mario Krenn
  Robust Molecular String Representation for Molecular Machine Learning
  Session Chair: Olexandr Isayev, Carnegie Mellon University

Panel Discussion on Open Challenges in ML:

This session is focused on discussing challenges and technological bottlenecks at the intersection of machine learning and science/engineering. Industry leaders at original equipment manufacturers (OEMs) and venture capitalists (VCs) will provide their perspective and directions for research and development. We anticipate that this session will facilitate effective TT & O (Tech. Transfer and Outreach).

VC Panel (March 23rd):

• Joel Moxley, Breakthrough Energy Ventures
• James Hardiman, DCVC (Data Collective)
• Mark Cupta, Prelude Ventures
• Bilal Zuberi, Lux Capital

Physics-Regularized ML:

• Oct 1: Dilip Krishnamurthy, Carnegie Mellon University PhD
  Physics-Informed Neural Networks
  Session Chair: Bharath Ramsundar, DeepChem
• Oct 8: Xiaowei Jia, Asst. Prof. @ University of Pittsburgh & University of Minnesota PhD
  Physics-Guided Machine Learning for Scientific Discovery
  Session Chair: Jason Koeller, Citrine Informatics

ML Obeying Physical Symmetries:

• Oct 15 (note schedule change): Bingqing Cheng, Trinity College Cambridge & EPFL PhD
  Machine-Learning Potentials: What Works and What Doesn’t
  Session Chair: Prof. Frank Noe, Freie Universität Berlin
• Oct 22: Bharath Ramsundar, Creator of DeepChem & Stanford CS PhD
  Physical Theories and Differentiable Programs
  Session Chair: Prof. Venkat Viswanathan, Carnegie Mellon University
• Oct 29 (note schedule change): Jan Hermann , Freie Universität Berlin & Humboldt University of Berlin Physics PhD
  Deep neural network solution of the electronic Schrödinger equation
  Session Chair: Prof. Giuseppe Carleo, École polytechnique fédérale de Lausanne (EPFL)
• Nov 5 (note schedule change): Tess Smidt and Mario Geiger, Lawrence Berkeley Laboratory & École polytechnique fédérale de Lausanne, respectively
  Neural Networks With Euclidean Symmetry for Physical Sciences and e3nn: A Modular PyTorch Framework for Euclidean Neural Networks
  Session Chair: Prof. Risi Kondor, University of Chicago

ML-Embedded Physical Models:

• Nov 12: Li Li, Google Accelerated Science & UC Irvine PhD
  Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
  Session Chair: Prof. Vikram Gavini, University of Michigan
• Nov 19: Christopher Rackauckas and Alec Bills, Massachusetts Institute of Technology & Pumas-AI, and Carnegie Mellon University, respectively
  Universal Ordinary Differential Equations and Its Application to an Engineering Challenge
  Session Chair: Prof. Srinivas (Sai) Ravela, Massachusetts Institute of Technology
• Dec 3: Alok Warey, General Motors & University of Texas at Austin PhD
  Deep Learning for Vehicle Systems
  Session Chair: Aniruddha Mukhopadhyay, ANSYS, Inc.
• Dec 10: Jesse Bettencourt , University of Toronto PhD
  Neural Ordinary Differential Equations
  Session Chair: Prof. Zico Kolter, Carnegie Mellon University
• Jan 14: Miles Cranmer, Princeton Astrophysics PhD
  Time Symmetries and Neurosymbolic Learning for Dynamical Systems
  Session Chair: Prof. Phiala Shanahan, Massachusetts Institute of Technology
• Jan 21: Shaojie Bai, Carnegie Mellon University PhD
  Deep Equilibrium Models
  Session Chair: Stephan Hoyer, Google Research
• Jan 28: Gurtej Kanwar, Massachusetts Institute of Technology PhD
  Gauge Equivariant Normalizing Flows for Lattice Field Theory
  Session Chair: Lena Funcke, Perimeter Institute for Theoretical Physics
• Feb 4: Evan Feinberg, Genesis Therapeutics, Inc.
  Machine Learning and Molecular Simulation Based Methods for Therapeutics
  Session Chair: Amir Barati Farimani, Carnegie Mellon University

Resources

The seminar series is supported by the ARPA-E DIFFERENTIATE program and the Carnegie Mellon Presidential Fellowship.

Conception

A panel discussion on the topic Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization crystallized important considerations while applying machine learning methods to limited-data engineering applications. Often it's useful to synergistally stack the ML models to the extent possible with the known physics of the problem for effective learning even in low-data regimes.

Questions?

Email the organizers at mphuthi[at]andrew.cmu.edu, varunshankar[at]cmu.edu and venkvis[at]cmu.edu

Video Recordings

Video recordings are available (login details available on request) here