Nobel Laureate Richard Feynman in his Lectures on Physics famously remarked that "...everything that living things do can be understood in terms of the jigglings and wigglings of atoms". This deceptively simple statement highlights the difficulty that structural biologists, medicinal chemists and computational scientists are faced with when attempting to understand human health and disease. We are used to thinking about a static, isolated picture of objects at the atomic scale, but often it is the dynamics (the "jigglings and wigglings") of the system and its environmental interactions that determine the underlying science, such as the role of intrinsically disordered proteins in neurodegenerative diseases or the possible link between quantum entanglement and molecular vibrations in biological photosynthesis. Twentieth century science not only set the challenge of studying life at the level of the structure and dynamics of atoms, but also provided (in theory) the solution, through the laws of quantum mechanics and the famous Schroedinger equation. Quantum mechanics explains the fundamental behaviour of matter at the atomic scale, and smaller. It enables scientists to make predictions about materials that are inaccessible to experiment, such as the structure of solid hydrogen in a star's core. At a more everyday level, quantum mechanics is routinely used by researchers in the microelectronics and renewable energy industries to rapidly scan multitudes of hypothetical materials compositions. In this way, the costly manufacturing process of the new materials need only begin once the desired properties have been predicted. However, quantum mechanics does not directly enable scientists to understand the biomolecular origins of disease, or to design new medicines to combat it. The reason for this comes down to Feynman's statement. It is infeasible to solve (even approximate) equations of quantum mechanics for the length and time scales sufficient to model all of the atomistic movements that need to take place, for example, for a drug molecule to find its target. Instead, computational chemists use a much simplified computational model, known as a force field, to estimate the dynamics of atoms. The force field models the atoms as bonded together in a molecule by springs, and interacting with other atoms through electrostatic and van der Waals forces, which are much stronger than gravity at the atomic scale. The strengths of these interactions are modelled by thousands of adjustable parameters, which have been manually tuned to reproduce experimental data over a period of many decades. We are reaching a stagnation point where accuracy is urgently needed for computer-aided design of new medicines, but parameter tuning delivers only small improvements. My vision for this UKRI Future Leaders Fellowship is to build a multi-disciplinary team that will work together to close the accuracy gap between quantum mechanics, and the approximate force fields used in biology and medicine. By working with international coding efforts, I will build the theory and software infrastructure required to dispense with these adjustable force field parameters, and instead derive them directly for the system under study, such as a protein implicated in disease. This will enable me to build more accurate computational models of the electrostatic and van der Waals interactions that determine the strength of binding of potential drugs to their targets. By crossing disciplinary boundaries to train in data science and machine learning, I will deploy the expertise that has been made famous for its applications in face and speech recognition, to create a spectrum of tools for speeding up the assignment of parameters and improving the accuracy of force field design. Finally, by undertaking secondments in the pharmaceutical industry, I will ensure that the developed methods will be used for the cost efficient design of the next generation of medicines.

The early prospects of Additive Manufacturing (AM) technologies promised to provide greater design freedoms, raise productivity levels, minimise material usage, compress supply chains, and enable the producer to attain greater levels of competitiveness by delivering enhanced product capabilities. Metal based LPBF AM systems have developed steadily over the past 20 years and now represent a multibillion-pound global market in machines, materials, and software. They find niche low volume applications in many industrial sectors and somewhat wider applications in aerospace and biomedical sectors. However LPBF AM processes are still slow compared to traditional manufacturing routes and are quite complex. They require precise focusing and manipulation of high energy laser beams over large powder beds in order to consolidate metal powder into a 3-dimensional solid through laser melting. Melting strategies play a significant role in part quality. Single laser beam melting strategies employed in all commercial systems suffer from melt instabilities, low melting efficiencies, and complex scanning strategies to reach high densities. They require a high level of labour-intensive part-specific build parameter refinement and time-consuming post processing operations. Despite the clear attractiveness of this production route, there remain several challenges in terms of build rates, process stability, part accuracy, repeatability, and part cost. In this project we propose to investigate several technology solutions that address these fundamental problems. To improve build rate we will establish a new class of LPBF AM capability by re-configuring the laser powder interaction process away from the current single laser interaction to large scale laser arrays. This approach offers increased melting efficiencies and true power scalability in the multi-kW domain. Since laser arrays are readily scalable, a 20kW system could deliver build rates of 153 kg in 24 hours. This is some 20 times faster than current systems. Our approach could offer world leading performance figures for LPBF AM systems. The use of laser arrays enables the problematic keyholing regime to be replaced with conduction limited regime leading to dramatic increases in process stability and part densities routinely reaching 99.99%. More stable melting regimes with reduced thermal gradients and reduce residual stress, reduce part distortion, and ultimately increase part accuracy. In process metrology will be applied to detect errors in the build layers and enable corrective steps thereby increasing process repeatability and deliver a right-first-time production process. With the combined innovations cited above we estimate that part costs savings up to 80% could be achieved compared to conventional LPBF AM systems.

The DigitalMetal CDT is born out to meet a national, strategic need for training a new generation of technical leaders able to lead digital transformation of metals industry & its supply chain with the objective of increasing agility, productivity & international competitiveness of the metals industry in the UK. The metals industry is a vital component of the UK's manufacturing economy and makes a significant contribution to key strategic sectors such as construction, aerospace, automotive, energy, defence and medical, directly contributing £20bn to UK GDP, and underpins over £190bn manufacturing GDP. Without a new cadre of leaders in digital technologies, equipped to transform discoveries and breakthroughs in metals and manufacturing (M&M) technologies into products, the UK risks entering another cycle of world-leading innovation but losing the benefits arising from exploitation to more capable and better prepared global competitors. The evolution to Industry 4.0 and Materials 4.0 coupled with unprecedented opportunities of "big data" enable the uptake of artificial intelligence/deep learning (AI/DL) based solutions, making it feasible to implement zero-defects, right first-time manufacturing/zero-waste (ZDM/ZW) concepts and meet the environmental-, sustainable- and societal- challenges. However, to fully take advantage of these opportunities, two critical challenges must be addressed. First, as user-identified problems in the metals industry that spans domains (from discoveries in M&M to their up-scaling and deployment in high volume/value production), urgently needed a new breed of engineers with skills to traverse these domains by going beyond the classical PhD training, i.e., T-model signifying transferable skills and in-depth knowledge in a single domain, to a new Pi-model raining that is underpinned by transferable skills and in-depth knowledge that transverse across domains i.e.,: AI/DL and engineering (M&M) to enable rapid exploitation of discoveries in M&M. Second, while AI/DL domain provides data-driven correlation analysis critical for product performance and defect identification, it is insufficient for root cause analysis (causality). This necessitates training on integrating data-driven with physics-based models of product & production, which is currently lacking in the metals industry. The Midlands region, as the top contributor to UK Gross Value Added through metals and metal products, with world-leading companies, such as Rolls-Royce and Constellium, LEAR and their customers, underpinned through collaborations with the five Midlands universities: Birmingham, Leicester, Loughborough, Nottingham & Warwick, is uniquely positioned to integrate research and industry resources and train a new cadre of engineers & researchers on the Pi-model to address user-needs. Our vision is to train future leaders able to accelerate the exploitation of M&M discoveries using digital technology to enable defect-free, right first-time manufacturing at reduced costs, digitise to decarbonise, and implement fuel switching in metals manufacturing industry.