Max Welling keynote talk on Machine Learning and Chemistry - ChemAI conference 1st edition

1. "
Prof. Max Welling
University of Amsterdam
Is Computational
Chemistry Ripe for a
Paradigm Shift with
Machine Learning?

2. AI4Chemistry4AI
Max Welling

3. Overview
• The Free Energy
• Part I: Chemistry4AI
• Part II: AI4Chemistry
• Conclusions

4. Introduction

5. Free Energy = Energy - Entropy
5
Ability to perform physical work Level of organization, information of a system
Industrial Revolution: ±1820 Information Revolution: ±1940

6. F = E - H
6
Daphne Schippers:
2015 & 2017 world champion 200 meters
Ben Feringa:
Nobel Prize Chemistry 2016

7. “It From Bit”
7

8. E.T. Jaynes
8
Entropy is our degree of ignorance about the microscopic degrees of freedom of a system

9. Chemistry4AI

10. Free Energies in Physics/Chemistry and ML
(Variational Free Energy)
(Boltzmann Distribution)
Physics/chemistry
(Evidence Lower Bound: ELBO)
ML
− log 𝑃 𝑋
≤ 𝐸𝑄 𝑍 𝑋 [− log 𝑃(𝑋, 𝑍)] − 𝑆[𝑄(𝑍|𝑋)]
F≤ 𝐸𝑄 𝑍 𝐻(𝑍) − 𝑇𝑆[𝑄 𝑍 ]
𝑃 𝑍 =
1
N
exp(−
𝐻 𝑍
𝑇
)

11. ML as Nonequilibrium Thermodynamics
Expectation Maximization:
E-step: Update Q to minimize Bound
M-step: Update P(X,Z) to minimize Bound
Nonequilibrium Thermodynamics:
Heat: Relax Q to minimize F
Work: Change H at fixed Q
− log 𝑃 𝑋
≤ 𝐸𝑄 𝑍 𝑋 [− log 𝑃(𝑋, 𝑍)] − 𝑆[𝑄(𝑍|𝑋)]
F≤ 𝐸𝑄 𝑍 𝐻(𝑍) − 𝑇𝑆[𝑄 𝑍 ]

12. Generative AI as Nonequilibrium Statistical Mechanics
2015

13. Example of a VAE: Diffusion Based Models
2021

14. Generative AI: Images
14

15. Generative AI: Art
15

16. Generative AI: Videos
16
“A shot following a hiker through jungle
brush.”
“An aerial shot of a mountain
landscape.”

17. Generative AI: Molecules!

18. Equivariant GNN Diffusion Models

19. AI4Chemistry

20. Molecules
Fluid Mechanics
Geophysics
Astrophysics
Time
Length
fs
sec
year
gigayear
pm nm km light-year
AI4Science: A Multi-Scale Scientific World
Quantum Classical
Fusion plasmas
Particle physics
mm

21. AI can Fold Proteins
21

22. Sören von Bülow, Mateusz
Sikora, Gerhard Hummer.
MPI of Biophysics
A New Paradigm for Materials Design
data
COMPUTATIONAL
COMPLEXITY
TIME
Era 3: In-silico design
Era 2: Data-driven modelling
Era 1: Trial-and-error

23. Amortization through Simulation
23
slow
fast
Simulate
Emulate
Simulate Train NN Surrogate Emulate

24. How can AI help the Sciences?
24
Schrodinger eqn. Langevin eqn. Navier Stokes eqn.
Materials Science:
Generating materials
Catalysis:
Accelerate reactions.
Drug Discovery:
Generating Molecules

25. Quantum
DFT
MCMC
Boltzmann
Generator
Classical
DFT
Equilibrium
Methods
Where can AI help?: Equilibrium Methods
Continuous representation
(PDEs)
Discrete representation
(samples/particles)
“bridge across scales”

26. Schrödinger
Equation
Molecular
Dynamics
DPD/
Conditional
Sampling
Hydro-
dynamics
Non-equilibrium
Methods
Where can AI help?: Non-Equilibrium Methods
Continuous representation
(PDEs)
Discrete representation
(samples/particles)
“bridge across scales”

27. Generating Molecules in Protein Pockets
27
Arne Schneuing Ilia Igashov
(+B. Correira, M. Bronstein et al.)
L denotes ligand nodes, P denotes pocket nodes

28. DiffLinker: Molecular Linker Design
28
Input fragments Reference molecule Generated molecule

29. Transition Path sampling
PIPS : Path Integral Path Sampling
Given initial state 𝑟0 and target state 𝑟𝑇
find the series of intermediate states
{𝑟1, 𝑟2, … , 𝑟𝑇−1} that describe the transition
path of minimal energy.
Sampling transition paths between molecular conformations
Source: https://www.e-cam2020.eu/rare-events-story/
Project Sisyphus
Controlled dynamics

30. Alanine Dipeptide
With Lars Holdijk, Yuanqi Du, Ferry Hooft,
Priyank Jaini, Bernd Ensing

31. Polyproline Helix & Chignolin
 Transition from left-handed to right-handed helix
(trans vs. cis)
 Small artificial protein used for studying folding process

32. 3D system of Lennard-Jones particles
NN
1D slice
Illustration from Jacobus Dijkman:
(with B. Ensing & J.W. van Meent)
Learning the Free Energy Functional for Classical DFT:

33. AI4Science
Computational
Chemistry
Molecular
Biology
Quantum
Computing
Machine
Learning
Sustainability
Health
Application
Modeling
Technology
Science
Energy
Condensed
Matter
Physics
Computational
Science
A Golden Age of Materials
33
T
i
m
e
Stone age
Bronze age
Iron age
Plastic age
“Material-on-demand age”?

34. Conclusions
ML
chemist
ML
Chemistry
Deep connection between AI
and Chemistry.
ML is the new hammer for
computational chemistry.
Applications in health & sustainability:
new drugs & materials.

35. Conclusions
ML
chemist
ML
Chemistry
Deep connection between AI
and Chemistry:
ML is the new hammer for
computational chemsitry:
Applications in health & sustainability:
new drugs & new materials