Alchemy’s five forerunner projects are the initial research focus and cover a variety of different AI for Chemistry topics and challenges, applicable to both industry and academia, with partners ‘mapping’ onto projects as they develop. Future challenges facing the industrial chemical sector will be identified via our Industry Partners and translated to live research projects, facilitated through Alchemy’s funding calls.
>_ F1: Human-in-the-Loop
Combining human hypothesis and Bayesian Optimisation will allow chemists to build human intelligence and chemical knowledge into closed-loop AI-driven robotic experiments. The synergy between a human in the loop approach and robotics will enable the accelerated discovery of materials and properties.
>_ F2: Large-Scale Crystal Structure Prediction
The prediction of material structures and properties, particularly organic materials, remains an important challenge to chemists. Here the focus is to address the limited availability of data for training ‘inverse design’ methods for organic materials and to provide a step change in the quantity of data available to describe relationships between molecular structure, crystal structure, and functional properties.
>_ F3: Data-Driven Materials Discovery
The literature available describing research undertaken on material classes and functions is vast and at times under utilised. This work aims to build and execute a ‘design-to-device’ pipeline for materials discovery for a chemical application via: AI-driven data sourcing, machine-learning predictions and high throughput experimental validation.
>_ F4: Generative AI for Small Molecules and Materials
Chemical discovery through generative AI can identify novel molecule and materials classes with tailored compositions, structures and functions in areas of societal need. This will be achieved by using scalable generative models that harvest comprehensive chemical knowledge from large-scale multi-modal chemistry data to learn representations with desirable chemistry properties.
>_ F5: Identifying and Optimising Reaction Mechanisms
The ability and speed at which we are able predict new materials is continuously increasing. This identifies the need for new methods to accelerate the rate at which materials can move from ‘prediction’ to reality.
Here, we set out to develop an automated platform for reaction mechanism discovery employing multifidelity ML models that combine computational and spectroscopic data.
