Title: ETH_Sem6 / DC / W09_AIAugmentedWithSimulations

Question: Which Companies have a large interest in the use of AI for Scientific Purposes?
QType (0=kprim,1=mc,2=sc): 1
Q_1: Microsoft
Q_2: LG
Q_3: Google
Q_4: Meta
Q_5: Swisscom
Answers: 1 0 1 1 0
Extra 1: Google (GNoMe) and Microsoft are using AI for the exploration of new Materials; Meta conducted the Open Catalyst Challange

Q: Why would you use semiempirical methods? (2)
A: - They have a much lower computational cost as they make the Zero differential overlap approximation (https://en.wikipedia.org/wiki/Zero_differential_overlap) resulting in a scaling of the number of coulomb integrals which need to be evaluated with \(N^2\) compared to the full \(N^4\) where \(N\) is the number of orbitals used in the calculation.
- This approximation is compensated by fitting to experimental data which implicitly accounts for the correlation energy. However the results always dependent on the data to which the results where fitted.

Give the order in Jacobs Ladder.

Q: What are the 4 general areas in chemical research in which computational methods can be used to make predictions on properties?
A: - Interactions with surroundings
- Chemical reactivity
- Structure and stability
- Spectroscopic properties

Header: Which solvation model-archetype is depicted below the red mask?
Remarks: This is an implicit solvation model.

Header: Which solvation model-archetype is depicted below the red mask?

Q: What are the 4 possibilities to include computational chemistry methods into ML?
A: - Chemistry-informed models
- Delta learning
- Foundation models and transfer learning
- Multi-fidelity approaches

Q: What is the fundamental idea behind Chemistry-Informed ML?
A: - Instead of just letting a model learn everything from scratch, the chemists knowledge of chemistry (e.g. mechanisms and mathematical relationships) is incorporated into the models can make them more realistc and efficient.

Q: Give 2 examples how mechanistic knowledge can be incorporated in to a ML model.
A: - One can simulate transition states of the desired reaction and the let the model learn relative barriers based on fingerprints and steric descriptors to predict an reaction outcome.
- One can use simulated QM features in addition to atom features (e.g. from SMILES etc.) to augment the model

Q: Give 3 model architectures which can be used to incorporate mathematical relationships into a ML model.
A: - Single-Task Model: Let each property be predicted by a separate model
- Multi-Task Model: Have one Model that predicts all properties at once
- Equation-Based Model: Predict variables with a model and infer result from them

Q: Describe a model architecture which includes knowledge of chemical mathematical relations?
A: - In this case the mathematical relation of interest is included in the learning process. For example a model can learn the Arrhenius coefficients to output the rate constants:

Q: What are the main advantages of chemistry informed ML? (3)
A: - Chemistry informed Models perform better in the low-data regime (e.g. something we often find ourselfs in)
- They extrapolate better
- The physical model regularizes the training

Q: What is the fundamental idea of \(\Delta\)-learning? (4)
A: - If we have a problem at hand where we have no previous knowledge about the system (to carry out chemistry informed ML) but we can perform simulation to get an estimation for numerical results.
- Delta Learning can then be used to predict the difference between simulation and experiment.
- We can carry out simulations of computationally cheap properties and estimate computationally expensive ones based on them

Q: What are the advantages of Delta learning?
A: - Learning only the differences can make training simpler and thus faster
- Good predictions can be achived at lower computational cost

Q: What is a foundation model?
A: - A foundation model is a model that creates representation ("latent space") of input space which is only based on the data and the underlying physical principles for the prediction at hand.
- Is an alternative way to using fingerprints, SMILES and other expert representations for the input space.
https://doi.org/10.1039/D3SC04928K

Q: What is understood by transfer learning?
A: Teaching foundation models something new

Q: What are the "layers on top" and the "finetuning" approaches to funadamental models?
A: - With the layers on top approach the foundation models are frozen and only the weights last few layers are adjusted during training.
- In the finetuning approach pretrained weights are used, but the entire model is updated during training.
https://pubs.rsc.org/en/content/articlelanding/2024/sc/d3sc04928k

Q: What is the main advantage of fundamental models?
A: More efficient data usage: The latent space representation can be learned form cheap and abundant data and the fewer and higher quality experimental data can then be used together with the latent space representation.