Automated Chemistry Workstation Research Articles

Performance of Search Algorithms in the Examination of Chemical Reaction Spaces with an Automated Chemistry Workstation

One of the challenges in applying automated chemistry workstations to problems of reaction optimization entails choosing an appropriate optimization algorithm. In the study described herein, 10 different algorithms have been examined for efficacy in searching reaction spaces using scenarios that explore effects of workstation parallelism and search space size. The algorithms differ in scheduling (serial vs. parallel), adaptive features (open loop vs. closed loop), and methods for stepping through the search space. Several two-tiered algorithms enable a breadth-first survey followed by an indepth optimization. For a workstation with modest parallel capacity, a parallel but nonadaptive algorithm is most effective in small or coarse-grained search spaces, whereas parallel adaptive algorithms are superior for examining large or fine-grained search spaces. The parallel adaptive algorithms become increasingly effective as the size of the search space increases. A serial algorithm is most attractive with a serial workstation, or when chemical resources are limited regardless of workstation or search space. The breadth-first survey of the twotiered algorithms significantly improves the efficiency of the subsequent in-depth optimization. The results obtained provide guidance in choosing optimization algorithms, designing more sophisticated algorithms, and developing workstations with parallel and/or adaptive features that use such algorithms.

A two-tiered strategy for simplex and multidirectional optimization of reactions with an automated chemistry workstation

A two-tiered strategy for optimizing reaction conditions has been developed for use with an automated chemistry workstation capable of parallel adaptive experimentation. In tier one, a broad survey of conditions is performed in parallel. In tier two, the promising region identified in the first tier is used as the starting point for in-depth searches. The search strategies employed in tier two include the composite-modified Simplex (CMS), multidirectional search (MDS), and parallel Simplex search (PSS) methods. Accordingly, this two-tiered strategy has been integrated into the CMS, MDS, and PSS experiment-planning modules. Each of these methods requires an initial user-defined simplex to explore reaction conditions. The breadth-first survey avoids the lengthy experimentation that occurs when the initial simplex is located far from the optimal region, and also diminishes the possibility of trapping in local maxima. Thus, the two-tiered strategy (breadth-first, depth-second) enables the optimal region to be reached rapidly.

A parallel simplex search method for use with an automated chemistry workstation

The Simplex method, an inherently serial means of optimization, can be used to search for improved conditions for chemical reactions. We have developed an automated chemistry workstation equipped for parallel adaptive experimentation. To exploit the capabilities of the workstation and thereby optimize reaction conditions in a more expedient manner, we have developed an experiment-planning module for performing Simplex searches in parallel. The parallel Simplex search (PSS) module enables multiple composite modified Simplex (CMS) searches to be performed in a concurrent manner. Features have been incorporated for two applications. (1) Multiple concurrent simplex searches in one search space, enabling optimization of conditions for one reaction. The use of Simplex searches starting from several points in a search space reduces the possibility of falsely concluding that a local maximum is the global maximum. (2) Concurrent investigation of multiple search spaces with one Simplex search per space, enabling optimization of conditions for each member of a set of catalysts or set of reactants for a given reaction. This latter feature is useful for combinatorial chemistry applications. All individual Simplex searches in a PSS study must employ the same experimental template. The PSS module includes options to define conditions for simplex moves as well as specifying initial points in the search space. Provisions are included for stop criteria, termination of an individual Simplex search, and global termination of multiple Simplex searches. The PSS method provides a parallel yet adaptive means for identifying improved conditions for chemical reactions.

An approach for parallel and adaptive screening of discrete compounds followed by reaction optimization using an automated chemistry workstation

The challenge of finding appropriate reagents, catalysts, or cocatalysts for chemical reactions is typically met with a strategy of surveying broadly to identify a good starting point (i.e., a hit) from which an in-depth optimization can be performed. We have developed an approach for experiment planning that enables this two-tiered strategy to be implemented on an automated chemistry workstation. One experiment-planning module (Parascreen) for screening discrete substances to identify hits is linked to a second module (Multidirectional search, MDS) for optimization of each hit compound. The screening module has been constructed through modification of an experiment-planning module for performing grid searches. Each candidate is examined over a range of concentrations. Two levels of decision-making are performed. (1) Local evaluation: Yield-versus-time data from a given reaction are examined in a pattern-matching procedure to assess whether monitoring should be continued or terminated. (2) Global evaluation: When a user-defined threshold (e.g., yield) is reached, the candidate is flagged as a hit; any experiments at higher concentration of the same candidate are deleted. Each of the hit compounds is subjected to identification of refined conditions by means of an MDS optimization. Each module alone enables parallel adaptive experimentation. Several issues for automated use of two different modules in succession have been addressed. This two-tiered approach of breadth-first screening followed by in-depth optimization of hits enables autonomous experimentation using an automated chemistry workstation.

An experiment planner for performing successive focused grid searches with an automated chemistry workstation

Automated chemistry workstations equipped for parallel and adaptive experimentation can provide a significant impact in chemistry research, particularly for exploring search spaces as part of optimization studies. A traditional method of investigating a search space involves generation of a response surface upon examination of a regular grid of points (e.g., as in a full factorial design). Such experimental approaches are compatible with parallel experimentation but are not adaptive in directing the search toward favorable regions of the search space. We have developed an algorithm wherein a succession of grid searches is performed in a search space. The location of the optimal response obtained in one search cycle constitutes the location about which a subsequent more fine-grained search is performed. In this manner, a sequential iterative optimization can be achieved: one cycle is comprised of a set of parallel reactions followed by data evaluation, and multiple cycles occur until one of several user-defined termination criteria is satisfied. In successive cycles, the number of levels on each dimension can be decremented and the range of each dimension can be decreased by a defined “shrinkage” factor. The resulting successive focused grid search (SFGS) affords a breadth-first then in-depth study. We have developed an experimental planner that enables the SFGS algorithm to be implemented on an automated chemistry workstation. Options are available for adjusting the scope of experimentation to conserve material resources (e.g., solvent, reagents, reactants) or to curtail the duration of experimentation. Collectively, the SFGS module enables parallel adaptive experimentation and affords a comprehensive response surface that is fine-grained in the region of optimal response.

A planning module for performing grid search, factorial design, and related combinatorial studies on an automated chemistry workstation

The investigation of combinations of factors in a defined search space has broad application in chemistry experimentation. The factors can involve continuous variables such as concentration and temperature, or discrete variables such as chemical reactants or reagents. The number of data points rapidly becomes quite large in such factorial experimentation, which can be adequately handled through the use of an automated chemistry workstation. The automated chemistry workstation utilizes experiment templates for implementation of factorial designs. Experimental templates represent generic plans that are used repetitively with slight variation of designated parameters for the chemical system under investigation. A linkage is established between the dimensions of the search space and the variables in the experimental template. Various patterns of points can be selected, enabling the composition of full factorial or partial factorial designs. The search space points are inscribed in the experimental template, giving rise to one experimental plan per point in the search space. The experimental plans are assessed for resource availability and those for which resources are sufficient are then scheduled for parallel implementation on a microscale automated chemistry workstation. An adaptive feature allows unproductive experiments to be terminated early, de-allocating resources for use in other experiments. Experimental plans can be readily composed and implemented for fundamental studies of chemical reactions, generating response surfaces, performing parallel screening procedures, and creating indexed combinatorial chemical libraries.

Implementation of the multidirectional search algorithm on an automated chemistry workstation. A parallel yet adaptive approach for reaction optimization

We have developed an experiment-planning module for applying a powerful new pattern search algorithm toward the problems of reaction investigation and optimization. The experiment planner works in conjunction with a closed-loop automated chemistry workstation equipped for parallel experimentation. The new algorithm, developed by Torczon for parallel computation of mathematical functions, achieves a focused yet parallel approach to finding regions of improved response. Like a full factorial design, the search space involves a regular grid of points. Like the Simplex algorithm, the multidirectional search (MDS) algorithm uses the movement of a simplex through a search space. However, with each movement all points except the single best are discarded, whereas the Simplex algorithm discards only the one worst point. Thus, in an n-dimensional space, the MDS algorithm projects n mandatory points at every cycle (beyond the initial). In addition, a larger number of exploratory points are identified by look-ahead projection of possible future simplices. Such exploratory points lie on multiple independent lines of search. The responses for the mandatory and exploratory points are acquired via parallel experimentation, with the latter points examined to the extent that the workstation has available capacity during the same schedule. The data from such exploratory points can be used in later cycles of experimentation, accelerating convergence on the region of optimal response. In the case of unlimited parallel experimentation capacity, all possible points in the space are projected, as in a full factorial design. The MDS algorithm thus adapts to the available parallel capacity of the workstation. The MDS planning module includes options for specifying initial points, stop criteria, and early-termination processes. Provisions are included for parallel scheduling of batches of experiments, convergence of the search, and movement at the boundaries of the search space. An MDS investigation can thus be implemented with global decision-making concerning movements through a search space, and local decision-making concerning termination of individual experiments. The MDS algorithm enables directed evolutionary searches in a parallel mode and is ideally suited for rapid optimization of chemical reactions using a microscale automated chemistry workstation.

Decision-tree programs for an adaptive automated chemistry workstation. Application to catalyst screening experiments

Automated chemistry workstations with the capability of altering the course of experimentation based on acquired data are essential for performing strategic searches. Such adaptive experimentation requires the ability to compose experimental plans for initiating, monitoring, evaluating, and making decisions about experiments. We have developed a decision-tree (DT) experiment-planning module that includes a complete command set for adaptive operation of the workstation. Functions for dialog prompts are included, enabling generic programs to be tailored for specific applications by various users. The DT experiment-planning module is one component of an experiment planner for an automated microscale chemistry workstation. The DT functions are also used to compose early-termination programs and describe objective functions for use in the companion factorial design, composite-modified simplex, and multidirectional search experiment-planning modules. The features of the DT experiment-planning module are illustrated with a program that screens a list of candidate compounds for catalytic activity. A set of dialog boxes at the beginning of the program prompts the user for information. Based on this information, the candidates are examined automatically (in a serial process) in exhaustive or hill-climbing searching strategies. A yield vs. time pattern-matching procedure is performed dynamically in order to assess whether monitoring should be continued. Decisions also are made concerning examination of the candidate at higher or lower concentrations. Higher concentrations are achieved by addition of aliquots of the candidate to the ongoing reaction, while lower concentrations are achieved by spawning a new reaction. When a user-defined yield threshold is reached or the entire user-specified concentration range has been spanned, catalysts and their effective concentration ranges are identified. This experiment-planning module enables composition of flexible programs for adaptive experimentation that can be used as stand-alone programs or as decision-making components of other searching programs.

A unified framework for the development of automated manufacturing systems supervisory software for the pharmaceutical industry

The myth of software development being a 'black art' has long gone. Today, many software engineering methodologies and Computer Aided Software Engineering (CASE) tools are available to streamline the software development process. This paper introduces a unified framework to develop supervisory software for automated manufacturing systems. The framework co-ordinates the efforts put forward by these methods and tools to reduce the cost of software re-configuration, and to enhance the software's quality. The framework addresses the complete software development life-cycle by using an object-oriented approach, to capture and analyse the behavioural, structural and informational aspects of a system. Traceability, completeness and modularity are the principal emphases of the framework. The framework also provides well-defined milestones and guidelines for practising software engineers who undertake software projects for the development of manufacturing systems. The applicability of the framework is illustrated by considering the development of the supervisory control software for an automated chemistry workstation that is commonly employed by the pharmaceutical industry.

Investigation of Cocatalysis Conditions Using an Automated Microscale Multireactor Workstation: Synthesis of meso-Tetramesitylporphyrin

Prior manual work has shown that the condensation of mesitaldehyde and pyrrole leading to tetramesitylporphyrin depends sensitively on concentration and requires cocatalysis involving BF3·O(Et)2 and ethanol or other protic species. We have applied an automated microscale chemistry workstation capable of parallel or adaptive experimentation to systematically investigate these cocatalysis conditions. Examination of experimental grids spanning a 1000-fold range of the concentrations of BF3·O(Et)2 and ethanol identified the best cocatalysis conditions for various mesitaldehyde and pyrrole concentrations. As the reaction concentration increased from 10 to 200 mM, the optimal yields were achieved with a parallel increase in BF3·O(Et)2 concentration (from 3.3 to 56 mM), but the amount of ethanol remained relatively constant. Catalysis conditions were identified that afforded ∼30% yields for reactants in the range of 10−73 mM, thereby enabling the reaction to be performed at increased concentration without loss in yield. Application of a catalyst searching protocol identified ethylene glycol, 2-methoxyethanol, and methanol as effective cocatalysts from a set of 12 candidates. Collectively, these results show the utility of an automated chemistry workstation in acquiring a comprehensive set of data concerning the scope of cocatalysis in the reaction of mesitaldehyde and pyrrole.

Toward high performance parallel experimentation machines. Use of a scheduler as a quantitative computer-aided design tool for evaluating workstation performance

Parallel experimentation machines offer the opportunity to acquire large volumes of scientific data in short periods of time. However, envisioning the performance of the integrated assembly of hardware components (robot, sample handling modules, analytical instruments) is a stubborn and pervasive design problem in the field of laboratory automation. An automated chemistry workstation has been built that consists of discrete hardware modules and is equipped with an experiment manager software package; the latter includes a scheduler for performing parallel experiments. In this paper, the scheduler is used as a computer-aided design (CAD) tool to evaluate the effects of speed enhancements in various hardware modules, including robot translation rate, sample manipulator syringe speed, solvent dispensing with a high-speed pump, and replacing traditional batch methods of sample analysis with flow injection analysis. The proposed hardware alterations are feasible with existing technology. The CAD approach has three elements. First, each alteration is evaluated quantitatively using timing formulae containing variable speed parameters. Second, the overall system performance improvement due to each individual alteration is simulated by scheduling a typical workload of experiments. Third, performance is equated with the number of experiments that can be performed in parallel. The relative importance of the hardware alterations depends on the typical workload of experiments. A workstation that incorporates all alterations exhibits a performance improvement of five to fifteenfold compared with our existing workstation (and over 150-fold compared with serial operation). Such a workstation would be capable of initiating and monitoring > 300 reactions (16 h each) in parallel, a throughput corresponding to ≈ 1700 experiments per week. Application of the scheduler as a CAD tool thus provides a means to predict performance quantitatively. This approach should prove generally useful in laboratory automation and is crucial for guiding the systematic design of high performance parallel experimentation machines.

Application of an automated chemistry workstation to problems in synthetic chemistry

An automated chemistry workstation is applied to problems in the synthetic chemistry of porphyrins. A factorial design study (sixteen experiments, 96 data points) was performed to examine the role of catalyst and reactant concentrations on porphyrin yield. Four experiments could be scheduled to run concurrently; all sixteen experiments were completed in less than 1 day of workstation time. The response surface from this experiment shows the conditions for achieving the highest yield. A simplex optimization was performed over the same reaction space, requiring fewer experiments to arrive at the optimal reaction parameters. A strategic search was performed to screen a list of reagents for catalytic activity. The effective concentration range of each catalyst was surveyed by systematic modification of an ongoing reaction. By terminating reactions when a yield threshold was surpassed or when the entire concentration range had been spanned, compounds with catalytic activity and their effective concentration ranges were identified with minimal experimentation. A new scientific finding was made concerning the catalytic activity of methanesulfonic acid in the porphyrin condensation. Automated chemistry workstations of this type should yield rapid accelerations in scientific research.

Experiment planner for strategic experimentation with an automated chemistry workstation

An automation system capable of modifying its actions based on experimental data will serve as a useful assistant to the scientist. In order to achieve flexibility in experimentation, we have developed a software system for stating experimental protocols that involve decision-making based on experimental data. These decision-making capabilities enable strategic experimentation, where experiments are altered or terminated based on prior or newly acquired data. An experiment planner provides for experimentation in an automated chemistry workstation using the simplex algorithm or a decision-tree programming approach. The composite modified simplex algorithm is used for optimization experiments. Simplex experiments are planned through a series of menus. The simplex algorithm can be altered in sophistication by turning features on or off in software. The simplex algorithm is well-suited for optimizations but provides no general decision-making capabilities. A general decision-tree programming module enables the composition of diverse experimental protocols which capture the logic of experimentation. Experimental protocols written with a text editor are converted by an interpreter to a set of procedural events. Experimental protocols can be composed that execute incisive actions based on data from ongoing experiments. Protocols have been written for automatically matching samples with analytical instruments and performing an automated dye titration. Resource consumption cannot be predicted in advance in closed-loop experimentation, thus a static resource manager is used with each simplex cycle and a dynamic resource manager is used in decision-tree experimentation. The resource managers and strategic decision-making features empower the workstation to function autonomously in pursuit of the research objectives stated by the scientist.

Experiment manager software for an automated chemistry workstation, including a scheduler for parallel experimentation

An automation system with the ability to work relentlessly, precisely, strategically, adn autonomously in pursuit of scientific goals will provide a powerful tool for tackling difficult scientific problems. We now present our work over the past seven years aimed at developing the hardware and software architecture for such an automated chemistry workstation. The workstation is designed for microscale experimentation in relatively clean domains of synthetic chemistry. This paper has two themes. First, the software system is presented in a comprehensive manner. An experiment manager software package has been developed that provides for composition of experimental plans, controls all aspects of automated experimentation, and manages the data. Experiment manager software is comprised of open-loop and closed-loop experiment planners bundled together with supporting features for timing, scheduling, material databases, resource management, automated start-up procedures, running display, data handling, configuration options, and maintenance operations. The experiment planners provide a menu-driven user interface, editing features, and a modular set of procedural events with which diverse experimental protocols can be composed. An overarching objective has been to achieve versatility in experiment planning yet still maintain access to the power that parallelism can confer in experimentation. Accordingly, the second theme in this paper is a description of approaches for performing experiments in parallel. Parallelism originates chiefly through simultaneous processing of samples at semi-autonomous hardware modules, at the user interface, and through the use of a scheduler. The scheduler takes as input a set of experimental protocols, establishes a sequence of the protocols, then interleaves the experimental protocols without altering the relative times of the procedures within each protocol. Experimental throughput can be increased by up to ten-fold by this approach. The straightforward planning and implementation of concurrent experimental protocols should yield major increases in research productivity.