HIGH THROUGHPUT SCREENING

Abstract

An apparatus for controlling synthesis of a material in particular a polymer is proposed, the apparatus comprising at least: an obtaining unit configured to receive a digital representa-tion of a candidate material, a model unit configured to provide a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties, a pareto unit configured to provide a provisional pareto front associated with the at least two characteristic material properties for the subset of materials, a property determination unit configured to determine the at least two character-istic material properties of the candidate material based on the data driven model and the digital representation, a validation unit configured to compare the determined at least two characteristic material properties with the provisional pareto front, a providing unit config-ured to, based on the Ccomparison providing a control file, suitable for controlling the syn-thesis of the candidate material.

Claims

1. A computer implemented method for controlling synthesis of a material, in particular a polymer is proposed, the method comprising: providing a digital representation associated with a synthesis specification of a candidate material providing a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties, determining the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation, comparing the determined at least two characteristic material properties with the provisional pareto front, based on the comparison providing a control file, suitable for controlling the synthesis of the candidate material.

2. The method according to claim 1, wherein the synthesis specification of the candidate material comprises a list of ingredients and machine-readable instructions for synthesizing material.

3. The method according to claim 1, wherein the digital representation is provided by a client device and the control file is received by a client device.

4. The method according to claim 1, wherein the provisional pareto front comprises a measure for uncertainty.

5. The method according to claim 1, wherein the at least two determined characteristic properties comprise a measure for uncertainty.

6. The method according to claim 1, wherein the comparing comprises determining if the determined characteristic material properties overlap with the provisional pareto front.

7. The method of claim 6, wherein the control file is provided if the comparing step determines an overlap.

8. The method of claim 1, wherein the method further comprises controlling the experiment, in particular by controlling flow rates of ingredients and reaction temperatures.

9. (canceled)

10. An apparatus for controlling synthesis of a material in particular a polymer is proposed, the apparatus comprising at least: an obtaining unit configured to receive a digital representation of a candidate material, a model unit configured to provide a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties, a pareto unit configured to provide a provisional pareto front associated with the at least two characteristic material properties for the subset of materials, a property determination unit configured to determine the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation, a validation unit configured to compare the determined at least two characteristic material properties with the provisional pareto front, a providing unit configured to, based on the comparison providing a control file, suitable for controlling the synthesis of the candidate material.

11. The apparatus of claim 10, communicatively coupled to a control unit for controlling the experiment.

12. A computer program product for controlling synthesis of a material, the computer program product comprising instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the method of controlling according to claim 1.

13. A computer implemented method for determining a provisional pareto front associated with characteristic material properties of materials in particular for screening comprising providing via a communication interface a set of materials, wherein each of the materials of the set of materials is described by their digital representation providing via the communication interface for of each of the materials of a subset of the set of materials at least two of their respective characteristic properties; providing a data driven model trained based on the digital representation of each of the materials of the subset of materials and at least two of their respective characteristic properties; predicting with the processing device the at least two characteristic material properties of remaining materials from the set of materials based on the data driven model; providing via the communication interface for each of the set of materials their at least two characteristic material properties; determining with the processing device from the set of materials a predicted pareto optimum for the at least two of the characteristic material properties; providing via the communication interface the determined provisional pareto front.

14. A computing apparatus including a processor and a memory storing instructions that, when executed by the processor, configure the apparatus to perform the method of claim 13.

15. A computer program product including instructions that, when processed by a computer, configure the computer to perform the method of claim 13.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0121] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first in30 troduced.

[0122] FIG. 1 illustrates a routine 100 for determining a provisional pareto front of characteristic material properties of materials in particular for screening in accordance with one embodiment.

[0123] FIG. 2 illustrates a routine 200 for determining a provisional pareto front of characteristic material properties of materials in particular for screening in accordance with one embodiment.

[0124] FIG. 3 illustrates an aspect of the subject matter in accordance with one embodiment.

[0125] FIG. 4 illustrates an implementation of the method of controlling synthesis of a material

[0126] FIG. 5 illustrates an example of an apparatus for controlling synthesis of a material

DETAILED DESCRIPTION

[0127] In block 102, routine 100 provides via a communication interface a set of materials, wherein each of the materials of the set of materials is described by their digital representation. In this example the set of materials is a design space. In this example the design space considered of polymers was evaluated. For the design space, four monomer types and chain lengths between sixteen and forty-eight in increments of two were considered. It was further considered that the reverse sequence equals the forward sequence. The total number of polymers in the design space may then be determined. This results in more than 14 million possible sequences. Enumeration is impossible for so many polymers. For example, assuming an average memory requirement of 62 kB per simplified molecular-input lineentry system (SMILES) the memory footprint would correspond to 0.8 TB. In this example the design space has been limited by design of experiment (DOE). Therefore, the set of materials is a reduction from the full design space. Reducing the design space using DOE may be an optional step.

[0128] In block 104, routine 100 provides via the communication interface for of each of the materials of a subset of the set of materials at least two of their respective characteristic properties.

[0129] In this example, the characteristic material properties are the adsorption free energy, the dimer free energy barrier and the radius of gyration. For other materials different characteristic material properties may be used. The characteristic material properties in this example, where determined by simulations in a step previous before the step providing (104). In other examples the characteristic material properties may be determined by experiment. In further examples, the characteristic material properties may be determined by simulation and experiments.

[0130] In block 106, routine 100 provides a data driven model trained based on the digital representation of each of the materials of the subset of materials and at least two of their respective characteristic properties. In block 108, routine 100 predicts with the processing device the at least two characteristic material properties of remaining materials from the set of materials based on the data driven model. In block 110, routine 100 provides via the communication interface for each of the set of materials their at least two characteristic material properties. In block 112, routine 100 determines with the processing device from the set of materials a predicted pareto optimum for the at least two of the characteristic material properties. In block 114, routine 100 provides via the communication interface the determined provisional pareto front.

[0131] In block 202, routine 200 provides via a communication interface a set of materials, wherein each of the materials of the set of materials is described by their digital representation. In block 204, routine 200 provides via the communication interface for of each of the materials of a subset of the set of materials at least two of their respective characteristic properties.

[0132] In block 206, routine 200 provides a data driven model trained based on the digital representation of each of the materials of the subset of materials and at least two of their respective characteristic properties. In block 208, routine 200 predicts with the processing device the characteristic material properties of remaining materials from the set of materials based on the data driven model. In block 210, routine 200 provides via the communication interface for each of the set of materials their characteristic material properties. In block 212, routine 200 determines with the processing device from the set of materials a predicted pareto optimum for the at least two of the characteristic material properties. In block 214, routine 200 provides via the communication interface the determined provisional pareto front. In block 216, routine 200 wherein the at least two characteristic material properties of each of the materials comprises uncertainty estimates. In block 218, routine 200 wherein providing via the communication interface the characteristic material properties for each material of the set of materials may comprise providing the uncertainty estimate for each of the characteristic material properties for each material of the set of materials, wherein providing the uncertainty estimate for each of the characteristic material properties for each material of the set of materials comprises. In block 220, routine 200 provides the uncertainty estimate defined by errors in determining characteristic material properties by simulations and/or measurements, for each of the materials when the uncertainty estimates defined by errors in determining characteristic material properties by simulations and/or measurements are available wherein classifying materials as pareto dominant, comprises. In block 222, routine 200 classifies a material as pareto dominant when for at least one of the at least two of the characteristic properties the material is superior to other materials by at least a margin and at the same time is not inferior in any of the at least two characteristic properties, wherein classifying materials as pareto dominated comprises In classifying materials as pareto dominated when at least one other material is pareto dominant by at least a margin , comprising. In block 224, routine 200 ranking unclassified materials based on the magnitude of their respective uncertainty estimates. In block 226, routine 200 provides via the communication interface a proposed material for sampling or a batch of materials for sampling based on the ranking. In block 228, routine 200 provides determined characteristic material properties of the proposed material or a batch of materials. In block 230, routine 200 retrains the model based on the proposed material and the determined the characteristic material properties of the proposed material or batch of materials. In block 232, routine 200 provides the trained model comprises providing the retrained model. In FIG. 3 a schematic flow of determining the provisional pareto front is shown. FIG. 3 a) each black dot depicts a characteristic material property 302 of a material in the material property space. In this non limiting example the material property space is 2-d, one property dimension is depicted as objective 1, the other property dimension is depicted as objective 2. Each characteristic material property 302 is surrounded by their respective uncertainty estimates 304. In FIG. 3 b pareto dominated materials 306 are depicted with hashed uncertainty estimates 312, pareto dominating materials 308 are classified, and belong to the provisional pareto front 310. Materials that are neither pareto dominant nor pareto dominated are unclassified material 314. In FIG. 3 c the pareto dominated materials 306 have been discarded. Only one unclassified material 314 is present and is proposed for sampiing. After sampling, the uncertainty estimates 304 of the sampled material are reduced in size as the error of the sampling is typically smaller than the error of the prediction model. FIG. 3 d shows the pareto dominant values and the previously unclassified material 314 after retraining of the data driven model. It can be seen that the uncertainty estimates 304 is reduced for various pareto dominant materials. This is, because the added training datum of the freshly sampled unclassified material 314 increases performance of the prediction model. It is clearly visible that unclassified material 314 now belongs to the class of pareto dominated materials and can be discarded.

[0133] In FIG. 4, an exemplary implementation of a method (700) of controlling synthesis of a specification is shown. At step 700 a digital representation associated with a synthesis specification of a candidate material is provided, in this example, the candidate material is a candidate polymer, and the digital representation associated with the synthesis specification. In other examples, the synthesis specification may be derived from a database. At step 710, a data driven model is provided, the data driven model is trained based on digital representations of previously presented materials and at least two of their respective characteristic properties. at steps 725 the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation are determined. At an optional step 730 They determined at least two characteristic material properties may be provided. At step 750 the provisional pareto front is provided. In the next step 760 the determined at least two characteristic material properties are compared with the provisional pareto front. Based on the comparison step 760 a controlled signal suitable for controlling the synthesis of the material is provided. the comparison step determines whether the candidate material will improve the provisional pareto front or not. If no improvement is to be expected the method step 770 will discard synthesis of the material and the controlled signal may indicate that no synthesis needs to be performed, by sending a stop signal to the synthesis apparatus. An improvement is to be expected when the provisional pareto front and they determined at least two characteristic material properties overlap. In that case at step 780 the control file initiating synthesis of the material will be provided, and synthesis of the candidate material is initiated. The control file may comprise a list of ingredients for synthesizing the material and instructions for synthesis. At step 790 experiments are initiated that measure the at least two characteristic material properties of the candidate material. The results from these measurements may then be provided to update the data driven model at step 740. Updating the data driven model reduces uncertainties for the material characteristics. Updating of the data driven model may be performed by retraining the data driven model with the additional data. In an optional step, the measured characteristics of the material property of the candidate may be compared to the provisional pareto front and the provisional pareto front may be updated. E. g. by classifying the material characteristics of the candidate material as pareto dominant.

[0134] FIG. 5 shows an exemplary apparatus for controlling synthesis of a candidate material. An obtaining unit 510 is configured for receiving a digital representation of a material. In this example the obtaining unit is a client device communicatively coupled to a property determination unit 520. The obtaining unit may be communicatively coupled to the property determination unit by a bus system, wired, wirelessly as well as over via an internet protocol. A model unit 530, is configured to provide a data driven model trained based on based on digital representations of previously presented materials and at least two of their respective characteristic properties. In this example the model unit 530 comprises a database with the stored data driven model. The property determination unit 520 is configured to determine the at least two characteristic material properties of the candidate material based on the digital representation and the data driven model. A pareto unit 532 is configured to provide a provisional pareto front to the validation unit 522. In this example, the pareto unit comprises a database storing the provisional pareto front. The validation unit compares the preliminary praetor front with the determined at least two characteristic properties of the candidate material. Based on the comparison step a control file suitable for performing the synthesis is provided to a control unit 540. Control unit 540 is communicatively coupled to a providing unit 524 and is configured to control the synthesis equipment, 550, 560, 570, 580, 590. The control unit is further configured to receive the control file suitable for controlling synthesis. The synthesis equipment may comprise one or more reservoirs 550 for ingredients needed for the synthesis specification. And one or more valves 560 for regulating ingredient flow into reactor 570. In this example the reactor 570 may be equipped with a mixer 580 and a heating/cooling element 590. A motor 600 of the mixer may be controlled with the control unit 540. The control unit 540 controls the synthesis equipment according to the control file provided by the providing unit 524. Valve 610 may be controlled by the control unit to extract a sample from the reactor. The sample may then be measured in measurement apparatus 620. Measurement apparatus is configured to measure the at least two characteristic material properties of the synthesized candidate material. The measured dato may then be provided to the validation unit to determine whether the measured material characteristics are pareto optimal. In another example not shown, the synthesis and the measurements are performed in silico.