Computational Design of Drugs and Materials Advanced lecture series, Uni Bas, Spring 2015

Transcription

1 Computational Design of Drugs and Materials Advanced lecture series, Uni Bas, Spring 2015 Lecture No 7 O. Anatole von Lilienfeld Literature: *Tutorial review paper, Int J Quant Chem (2013); *Book-chapter available online at

2 ToC 1. Intro & Inverse Question 2. Properties: Quantum & Statistical Mechanics/Accuracy vs Cost 3. Chemical Space & Alchemy 4. Machine Learning & Descriptors 5. Optimization (Stochastic, Evolutionary, Gradient, Discrete) 6. High-Performance Computing 7. Drug & Materials design Examples

3 Nature Reviews (2010)

4 Nature Reviews (2010)

5 Nature Reviews (2010)

6 Shift the paradigm Franceschetti and Zunger, Nature (1999)

7 Shift the paradigm Franceschetti and Zunger, Nature (1999) No analytic solution Ill-defined high dimensional expensive 1) Transferable: First principles (QM, UFF) 2) Smart: Variational (dp/dx), Genetic,... 3) Fast: Correlational (Machine Learning) 4) Muscle: Supercomputing & Data

8 Shift the paradigm Franceschetti and Zunger, Nature (1999) No analytic solution Ill-defined high dimensional expensive Iterative minimization 1) Transferable: First principles (QM, UFF) 2) Smart: Variational (dp/dx), Genetic,... 3) Fast: Correlational (Machine Learning) 4) Muscle: Supercomputing & Data

9 Shift the paradigm Franceschetti and Zunger, Nature (1999) No analytic solution Ill-defined high dimensional expensive Iterative minimization 1) Transferable: First principles (QM, UFF) 2) Smart: Variational (dp/dx), Genetic,... 3) Fast: Correlational (Machine Learning) 4) Muscle: Supercomputing & Data

10 Shift the paradigm Franceschetti and Zunger, Nature (1999) No analytic solution Ill-defined high dimensional expensive Iterative minimization 1) Transferable: First principles (QM, UFF) 2) Smart: Variational (dp/dx), Genetic,... 3) Fast: Correlational (Machine Learning) 4) Muscle: Supercomputing & Data

11 Examples 1. Catalysts (Norskov and others) 2. Drugs

12 Materials: Catalysts Various groups work on this problem. E.g. in 2010, Jens Nørskov arrived at Stanford, formerly running the Center for Atomic-scale Materials Design at the Technical University of Denmark. Nørskov et al want to understand catalytic processes such that they can develop a theoretical basis for the design of catalysts "basically going from quantum mechanics to designing a new material" Better catalysts are in need for converting biomass directly into "syngas." One can convert syngas into pretty much any hydrocarbon. Or a catalyst that would capture the energy of the sun and store it in a form usable as fuel: The key to artificial photosynthesis is an energy-efficient way to "reduce" carbon dioxide to combine the carbon with hydrogen to make fuels. Catalyst efficiency is the sticking point, as the catalyst s performance depends on the desired hydrocarbon, possibly resulting in unwanted byproducts. But processes might someday be implemented at very large scales, so even small changes in efficiency are very important. But the design stage is only the beginning. After theory comes "characterization," or determining whether the designed material actually does what the theorists think it will do.

13 Heterogeneous Catalysis

14 Heterogeneous Catalysis

15 Heterogeneous Catalysis

16 Heterogeneous Catalysis

17 Heterogeneous Catalysis Pareto front for price vs catalytic performance for methanation. Volcano plot for Ni loading. Errors are estimated standard deviations of 10%.

18 Heterogeneous Catalysis

19 Heterogeneous Catalysis Acetylene hydrogenation Minimal ethane outlet concentration is desirable

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21 Heterogeneous Catalysis Challenges ahead Identification of catalyst just first step. Also to be considered: Long-term stability lack of side-products poison resistance susceptibility to promotors cost of production Link between theory and reactor design just at beginning: There are several catalyst beds with cooling stages and volcano plots vary strongly with operating conditions which vary throughout the reactor. Go beyond heterogeneous catalyst design that consist of transition metal (alloy) surfaces. nano-particles? Defects? Correlated oxides? Charge-transfer (redox) catalysts? Homogeneous (liquid) catalysts?

22 Drugs

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24 Drugs Ligand binding domain (LBD) of Estrogen receptor (ER)

25 Drugs Ligand binding domain (LBD) of Estrogen receptor (ER)

26 Drugs Oostenbrink, J Comp Chem (2009) One-step perturbation vs thermodynamic integration

27 Conclusions 1. Computational design already in use 2. Profit from computational ``revolution 3. In general problem can be cast as minimizing property penalty in space of chemical configurations and compositions 4. Some simple relationships can be inverted (Hammett, ) 5. Other properties and classes still require substantial research

28 Other Materials: Batteries Ternary mixtures

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