machine learning for energy landscapes
play

Machine learning for energy landscapes Tristan Bereau Van t Hoff - PowerPoint PPT Presentation

Feb 20, 2023 •347 likes •1.21k views

Machine learning for energy landscapes Tristan Bereau Van t Hoff Institute for Molecular Sciences & Informatics Institute University of Amsterdam Introduction to kernel-based ML Today Incorporation of physical symmetries,

  1. Machine learning for energy landscapes Tristan Bereau Van ’t Hoff Institute for Molecular Sciences & Informatics Institute University of Amsterdam

  2. • Introduction to kernel-based ML Today • Incorporation of physical symmetries, conservation laws Machine learning for energy landscapes Tristan Bereau Van ’t Hoff Institute for Molecular Sciences & Informatics Institute University of Amsterdam

  3. • Introduction to kernel-based ML Today • Incorporation of physical symmetries, conservation laws Machine learning for energy landscapes Tristan Bereau Van ’t Hoff Institute for Molecular Sciences & Informatics Institute University of Amsterdam Supervised ML in chemistry Thursday and materials science Kevin Jablonka Mohamad Moosavi

  4. Molecular dynamics 2

  5. Molecular dynamics F = m a 2

  6. Molecular dynamics F = m a Specify interparticle forces: “force field” 2

  7. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” 2

  8. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” Wikipedia 2

  9. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” Wikipedia fs ps ns s ms s µ 2

  10. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” Wikipedia integration time step fs ps ns s ms s µ 2

  11. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” Wikipedia integration Timescales of interest time step fs ps ns s ms s µ 2

  12. Molecular dynamics Numerically integrate particle positions F = m a Specify interparticle forces: “force field” Wikipedia integration Timescales of interest time step Emergent complexity fs ps ns s ms s µ 2

  13. Data as the 4 th pillar of science A. Agrawal and A. Choudhary APL Mater. 4 053208 (2016); https://www.bigmax.mpg.de 3

  14. Data science Hardware Machine learning Databases 4

  15. Data science Hardware DeepMind Machine learning Databases Google 4

  16. Links to machine learning Interpolation of a high-dimensional function Potential energy surface Can we build a more accurate PES? Can we easily build an accurate PES? Can we make the numerical integration faster and/or more efficient? … 5

  17. Teaser: let’s fit data points Good Not good 6

  18. Teaser: let’s fit data points Good Not good 6

  19. Teaser: let’s fit data points Good Not good I want to be here 6

  20. Teaser: let’s fit data points Good Not good I want to be here ruthlessly stolen from A. von Lilienfeld 6

  21. Multivariate function approximation Sparse data infer smooth function 7

  22. Multivariate function approximation “inverse problems” Sparse data infer smooth function 7

  23. Multivariate function approximation “inverse problems” Sparse data infer smooth function sparse data ground truth Ê property interpolation property Ê Ê Ê Ê Ê molecular structure descriptor Rupp, International Journal of Quantum Chemistry 115 (2015) 7

  24. Multivariate function approximation “inverse problems” Sparse data infer smooth function Regression : y sparse data prediction of f : ℝ d → ℝ ground truth Ê property interpolation property based on noisy data points Ê Ê D = ( X , y ) = { x n , y n } N Ê Ê n =1 Ê y n = f ( x n ) + ε x molecular structure descriptor What is f ? Rupp, International Journal of Quantum Chemistry 115 (2015) 7

  25. Kernel methods are vintage - needs a representation { - linear algebra Kernel - can be efficient with small data - learns the representation { - complex mathematical Deep learning structure - data hungry 8

  26. � Learning from experience � � � � Inductive (based on examples) � (genera (snake seems like overfitting, � fitting to well, cf. Lecture 2) source: xkcd [7] Visualization of high-dimensional space 9

  27. <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> Extrapolation in machine learning 35 U LJ ( r ) 30 Training points Prediction 25 20 U LJ ( r ) [ ≤ ] 15 ⇣ � ⌘ 6 � ⌘ 12 ⇣ � 10 U LJ ( r ) = 4 ✏ − r r 5 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0 1 . 2 1 . 4 1 . 6 1 . 8 2 . 0 r [ æ ] 10

  28. <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> <latexit sha1_base64="G3h4pfP2Yj/buXVPhz0Vw6uTCrc=">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</latexit> Extrapolation in machine learning 35 35 U LJ ( r ) U LJ ( r ) 30 30 Training points Training points Prediction Prediction 25 25 20 20 U LJ ( r ) [ ≤ ] U LJ ( r ) [ ≤ ] 15 15 ⇣ � ⌘ 6 � ⌘ 12 ⇣ � 10 10 U LJ ( r ) = 4 ✏ − r r 5 5 0 0 0 . 2 0 . 2 0 . 4 0 . 4 0 . 6 0 . 6 0 . 8 0 . 8 1 . 0 1 . 0 1 . 2 1 . 2 1 . 4 1 . 4 1 . 6 1 . 6 1 . 8 1 . 8 2 . 0 2 . 0 r [ æ ] r [ æ ] 10

  29. Bayesian inference Prediction { Prior beliefs Sampled data 11

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Complexity of Machine Learning and Landscapes Jim Halverson Northeastern University ICTP - Machine

Complexity of Machine Learning and Landscapes Jim Halverson Northeastern University ICTP - Machine

Complexity of Machine Learning and Landscapes Jim Halverson Northeastern University ICTP - Machine Learning Landscape, December 2018 Based on 1809.08279 with Fabian Ruehle see also: 2006 work of [Douglas, Denef], 2010 work of [Cvetic,

1.44k views • 111 slides
An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides
Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by which computers can be trained through observation, rather than being explicitly programmed. Basically, a program is given input and adjusts its

556 views • 17 slides
Issues and Opportunities ARPA-E Machine Learning-Enhanced Energy-Product Development Workshop

Issues and Opportunities ARPA-E Machine Learning-Enhanced Energy-Product Development Workshop

ORNL Publication ID: 112595 Machine Learning: Issues and Opportunities ARPA-E Machine Learning-Enhanced Energy-Product Development Workshop June 21-22, 2018 Falls Church, VA David E. Womble Director of Artificial Intelligence Programs Oak

327 views • 22 slides
Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides
Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides
Using Machine Learning with Wide Area Networks (WANs) Dr. Mariam Kiran Energy Sciences Network

Using Machine Learning with Wide Area Networks (WANs) Dr. Mariam Kiran Energy Sciences Network

Using Machine Learning with Wide Area Networks (WANs) Dr. Mariam Kiran Energy Sciences Network (ESnet) ANTG Research Group Lawrence Berkeley National Lab Summer Student 2017 Talk 1 Agenda Machine learning (ML) is everywhere: Is it

442 views • 43 slides
Machine learning Boltzmann Machines Dima Kochkov 1 1 Department of Physics University of Illinois

Machine learning Boltzmann Machines Dima Kochkov 1 1 Department of Physics University of Illinois

Machine learning Machine learning basics Energy based models Algorithmic improvements Examples Summary Machine learning Boltzmann Machines Dima Kochkov 1 1 Department of Physics University of Illinois at Urbana-Champaign Algorithm interest

474 views • 32 slides
PRACTICES TO MAINTAIN HEALTHY LANDSCAPES Self-Guided Educational Module Lesson 1 of 4 Learning

PRACTICES TO MAINTAIN HEALTHY LANDSCAPES Self-Guided Educational Module Lesson 1 of 4 Learning

PRACTICES TO MAINTAIN HEALTHY LANDSCAPES Self-Guided Educational Module Lesson 1 of 4 Learning Objectives 2 1. Describe the zone management approach 2. The principles behind sustainable landscapes Cultural controls Physical controls

862 views • 44 slides
CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

1/22/19 CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine Learning? A Simple Task: Recognize Obama How do you program a computer to Recognize faces? Recommend movies? Decide which web

581 views • 5 slides
Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.04k views • 28 slides
Socioeconomic valuation of cultural landscapes landscapes Assoc. Prof. Indr Graulevi i

Socioeconomic valuation of cultural landscapes landscapes Assoc. Prof. Indr Graulevi i

WORKSHOP: Methodology of valorisation: territories, identities and local heritage Socioeconomic valuation of cultural landscapes landscapes

810 views • 78 slides
Adversarial Machine Learning (AML) Somesh Jha University of Wisconsin, Madison Thanks to

Adversarial Machine Learning (AML) Somesh Jha University of Wisconsin, Madison Thanks to

Adversarial Machine Learning (AML) Somesh Jha University of Wisconsin, Madison Thanks to Nicolas Papernot, Ian Goodfellow, and Jerry Zhu for some slides . Machine learning brings social disruption at scale Healthcare Energy Source: Peng and

821 views • 71 slides
Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine Learning Introduction to Machine Learning 1 / 12 Outline 1 Nearest Neighbor Prediction 2 Complexity Considerations 3 The Voronoi Diagram 4 Overfitting

564 views • 12 slides
1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

What is Learning? Herbert Simon: Learning is any process by which a system improves performance from CS 391L: Machine Learning experience. Introduction What is the task? Classification Problem solving / planning /

603 views • 6 slides
MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

JACOPO DI IORIO, MATTEO FONTANA, DANIELE OCCHIUTO, CLAUDIA VOLPETTI MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING STATISTICAL LEARNING Separate evolutions, but with shared methodologies MERGE

565 views • 55 slides
Writing Academic Texts General Remarks Using the Media Informatics templates for theses &amp;

Writing Academic Texts General Remarks Using the Media Informatics templates for theses &amp;

Writing Academic Texts General Remarks Using the Media Informatics templates for theses &amp; reports Process Templates Writing Jrg Cassens Institute for Mathematics and Applied Informatics Academic Literacy Winter term 2019/2020 WT

615 views • 59 slides
Data Reduction Jieping Ye Arizona State University Joint work with Jie Wang and Jun Liu 1

Data Reduction Jieping Ye Arizona State University Joint work with Jie Wang and Jun Liu 1

Center for Evolutionary Medicine and Informatics Sparse Screening for Exact Data Reduction Jieping Ye Arizona State University Joint work with Jie Wang and Jun Liu 1 Center for Evolutionary Medicine and Informatics wide data feature

671 views • 41 slides
Partitioning Spatially Located Load with Rectangles: Algorithms and Simulations Erik Saule ,

Partitioning Spatially Located Load with Rectangles: Algorithms and Simulations Erik Saule ,

Partitioning Spatially Located Load with Rectangles: Algorithms and Simulations Erik Saule , Erdeniz Ozgun Bas, Umit V. Catalyurek Department of Biomedical Informatics, The Ohio State University { esaule,erdeniz,umit } @bmi.osu.edu Frejus 2010

948 views • 63 slides
OpportuniHes to Harness NanoinformaHcs for Impact in Environmental

OpportuniHes to Harness NanoinformaHcs for Impact in Environmental

OpportuniHes to Harness NanoinformaHcs for Impact in Environmental RemediaHon Mark D. Hoover , PhD, CHP, CIH 304-285-6374 mhoover1@cdc.gov NaHonal InsHtute for OccupaHonal Safety and

307 views • 17 slides
Biodiversity and Ecosystem Informatics Panel Yannis Ioannidis Univ. of Athens, Hellas Personal

Biodiversity and Ecosystem Informatics Panel Yannis Ioannidis Univ. of Athens, Hellas Personal

Biodiversity and Ecosystem Informatics Panel Yannis Ioannidis Univ. of Athens, Hellas Personal Background Not involved with any BDEI project No immediate knowledge of BDEI projects Involved with scientific database projects in

289 views • 10 slides
Carol V. Alexandru , Sebastiano Panichella, Sebastian Proksch, Harald C. Gall Software Evolution

Carol V. Alexandru , Sebastiano Panichella, Sebastian Proksch, Harald C. Gall Software Evolution

59th CREST Open Workshop Centre for Research on Evolution, Search and Testing University College London, London, United Kingdom Carol V. Alexandru , Sebastiano Panichella, Sebastian Proksch, Harald C. Gall Software Evolution and Architecture Lab

1.1k views • 91 slides
Unusual Tensor Decompositions for Informatics Applications Brett W. Bader Sandia National

Unusual Tensor Decompositions for Informatics Applications Brett W. Bader Sandia National

Unusual Tensor Decompositions for Informatics Applications Brett W. Bader Sandia National Laboratories NSF Tensor Workshop February 20, 2009 Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the

442 views • 28 slides
AGENDA FOR TODAY 10. 0.00a 00am We m Welcome me and nd int ntro roduction t n to

AGENDA FOR TODAY 10. 0.00a 00am We m Welcome me and nd int ntro roduction t n to

AGENDA FOR TODAY 10. 0.00a 00am We m Welcome me and nd int ntro roduction t n to event nt 10.10am am Vintag age V e Vibes es o over erview ew 10.20 20am am Th The Big L g Ligh ght o overv rview 10.30am

457 views • 11 slides

More recommend