Molecular Machine Learning - Intersection of Chemistry and AI

In the modern landscape of chemical manufacturing, the most valued resource that we possess is time. Creating an environment that is not only efficient in producing results but also painstakingly effective at meeting its deadlines is a privilege that few companies can boast of, even among the biggest multi-billion manufacturing giants.

Whereas our predecessors may have enjoyed the relative freedom of an unexplored field of chemistry to commit thousands of hours to chemical experiments and the meticulous study of individual data, we are now in an era where information is quantified in the scales of terabytes, and where companies unable to leverage vast speeds of experimental data analysis are soon left behind by the competition.

In fact, the very concept of Industry 4.0 involves the optimization of manufacturing through the intelligent networking of machines and processes for industry with the help of information and communication technology, harnessing advanced technologies in robotics and the Internet of Things (IoT) to exponentially multiply the productivity and efficiency of chemical labs and production plants.

Machine learning, big data analysis, process simulation, and cloud computing are all concepts being introduced to the next generation of industry, and chemical manufacturing is no exception.

As a computer scientist and green chemical manufacturer, it causes me great pleasure to witness how the blend between these advanced fields is rife with opportunities… but let’s get a deeper look into how these many aspects of Industry 4.0 are helping molecular design progress into a new era.

Data analysis – have traditional methods hit a wall?

One of the most worrying situations to be in as a company – or even an entire industrial field in general – is to come across a problem too large to be solved, no matter how much time or money you attempt to use to unravel it. These obstacles are often of a technological nature, in which only the widespread advancement of software or hardware can bring an answer to current questions.

In recent decades, chemistry has hit such a snag with molecular design and data analysis. In most cases, experimental research efforts based on trial and error have become too inefficient, limited, and both time- and cost-intensive to manage the amounts of data produced during the study of molecules; in others, chemical structure properties and the nature of new materials is simply too complex to be studied in this outdated way, forcing the introduction of breakthrough technological solutions.

To attempt to combat this, data scientists are often introduced into the equation to make things more digestible, but this is often not a definitive solution, as they are not familiar with the intricate nature of molecular chemistry. It is for this reason that there is a growing need for education on AI and machine learning in the manufacturing space.

Machine learning is an immensely effective way at not only mining and separating useful data from the immense majority of lab results, but also at accelerating and streamlining the process of chemical development. I recently wrote an article on high throughput screening techniques and their use in chemical labs, and it has been incredible to witness the advantages that machine learning can achieve when combined with these methods of automation:

• The equations involved in modern-day materials science and quantum mechanics have become overly complex for a single team of analysts to solve in a realistic timeframe. Solvation energies, the Schrödinger equation, the prediction of catalytic processes (products and energy requirements), and density-functional theory modelling are easily unlocked by an automated system based on machine learning applications.
• Where an analyst may encounter trouble in managing “successful” and “failed” results without bias, a machine learning AI system will harness the data acquired in both circumstances for analysis and future use.
• Updates and fixes to machine learning tools are typically available within days of a limitation being found; a team of analysts may require long periods to decipher more complex mathematical operations.
• New hypotheses are generated quickly into AI databases by machine learning tools; a system will rapidly access past data through cloud computing and the Internet of Things, leading to instant solutions to repeated problems.
• Furthermore, when an automated system has been “taught” enough about the molecule or reaction, it will provide advice on the best models and manners to perform complex calculations.

Through machine learning, we are now able to analyze uncountable amounts of “big” data, transforming it into usable knowledge at unprecedented speeds; furthermore, as we continuously work on teaching computers how to interpret text as a human would, we are pushing towards the complete digitization of chemical laboratories.

However, the question remains: do all of these tools have a future at a larger, industrial level?

Tying industrial processes with machine learning and big data analysis

Thankfully, there have been recent, positive developments in the fields of artificial intelligence and machine learning in terms of the chemical engineering discipline; these are related to the vast requirements of data analysis within manufacturing plants – even the simpler processes must be constantly supervised for undesirable deviations, and complex processes (crystal formation, catalysis) are too sensitive and costly for careless human mistakes.

Nevertheless, this is not to say that there has not been a fair share of obstacles in creating a solid link between industrial processes and automation; the infrastructure for the use of robotics, machine learning, and big data analysis has been resisted across the industry, especially among companies with smaller budgets for these R&D investments. In other cases, the restrictions posed by a lack of accessible software and experts has represented a stumbling block for companies looking to get involved.

Even so, the technologies are available and just waiting to be applied to plants across the world – these are just a few of the incredible features they can offer towards the optimization of a smart factory:

1. Sensors are already a crucial part of the manufacturing process, helping operators control the conditions within reactor vessels and indicating when safety measures may be required. In the new factory, however, sensors become more essential in helping AI take action, using the Internet of Things (IoT) to send data between agile systems and apply adjustments across the process accordingly within instants. Furthermore, machine learning allows for these changes to be memorized and for preventive measures to be implemented – similarly to how DCS and PLC controllers work in present day manufacturing.
2. With cloud computing, operators and engineers no longer need to be present at the plant to control processes and study the plant’s output; with the connectivity allowed by industry 4.0, data analysis can be accomplished remotely, and staff at one single headquarters can supervise the feedback provided across several manufacturing sites.
3. Process simulation is now seeing immense boosts in accuracy and efficiency; with automation and improved algorithms in machine learning, complex processes involving catalyst design are now feasible in terms of cost and time. Better catalysts can now be created, leading to the development of state-of-the-art advanced intermediates (more about this in another post, here).
4. Finally, concepts that were once futuristic dreams, such as augmented reality (AR) and mixed reality (MR) are already being implemented, allowing scientists and engineers to remotely interact with their peers across enormous geographical distances through headsets, haptic wearables, and virtual environments.

Better manufacturing processes lead to superior products – it’s that simple

With the sophisticated processes now rapidly being integrated into molecular design and industrial manufacturing, we are entering an age of finer, safer products that are built for specialized uses.

At companies such as Environmental Fluids, where we have already been well ahead of the curve in terms of green chemistry and renewable materials, the only logical next step for us is to reach out to companies who are seeking technological advancement within their laboratories and help them enter an era of Industry 4.0.

Harnessing the power of machine learning and big data analysis – together with the superiority of cloud computing, IoT, and process simulation – we are building better processes that will create superior products for forward-thinking customers.

We are putting immense efforts into the research of concepts related to Industry 4.0, and learning how these will help us optimize lab and plant productivity in the immediate future – if you wish your company to be part of this bright future, don’t hesitate in sending us a message!

Contact us now and we will discuss your requirements, and how to meet them with modern data analysis methods and machine learning.