UK science policy in transition

The way the UK government funds science is currently in the midst of a major transition, with the creation of a much more direct link between the priorities of the government of the day and the kind of research that it funds.  A few months ago I wrote about the likely prospect of a breakdown of a long period of consensus in UK science policy – UK Science in a post-liberal world.  I’m not sure whether the current changes are best thought of as the first manifestation of this breakdown of consensus, or as an attempt to make those changes in the system that are necessary to preserve it.  Here I make a first attempt to set these changes in context.  

Some history

UK governments have recognised the need for the State to fund scientific research since the late 19th century, and some of the principles underpinning that were articulated early in the 20th century. One innovation of that period was the Research Council – conceived as a body standing slightly apart from government, largely managed by expert scientists.  The first of these was the Medical Research Council, established in 1920 as a body incorporated by a Royal Charter.  Subsequently, other research councils, covering other fields of science – and social science and the humanities – were established on the same principles, and various reorganisations have taken place, but the basic model remained in place until 2017.

It is important, however, to understand that for most of this period the research supported by Research Councils amounted to only a small fraction of total government R&D.  Most of this took place with the direct support of government departments, such as those responsible for agriculture, for defence and military procurement, and for atomic energy, often in government research laboratories.  Going into the 1980’s, when the UK was one of the most R&D intensive countries in the world, less than 15% of government funded R&D was supported by the research councils.

Continue reading “UK science policy in transition”

The Year in Soft Machines

The Soft Machines blog has been going for more than twenty years, I’m astonished to say. It’s good to see a substantial increase in the number of readers in 2025’s later months – no doubt helped by the fact that, with a bit more time on my hands, I’ve been writing a bit more regularly. For the benefit of new readers and old, here’s a review of some of the year’s posts, set in the context of some of this blog’s recurring themes.

The UK’s productivity and economic growth problem

The UK’s continuing economic stagnation remains a continual preoccupation, unfortunately. A recent post presents the most recent data for GDP per capita, showing that the country is around 30% worse off than if the pre-2008 trend had continued. Such a dramatic change in economic fortunes must have a cause – or causes. Stating what should be obvious, but doesn’t seem to be, to many commentators, I insist that the causes must precede the big break in 2008, and that there may be long lags between cause and effect. But one can always make things worse with subsequent bad decisions.

The UK’s continuing economic growth crisis

Fundamentally, our economic problems are problems of productivity growth – or lack of it. I’ve been writing about this for about a decade, with a post from earlier in the year summarising some of the arguments:

Ten Years of Banging on about Productivity

Why does this matter? From the government’s perspective, projections of future productivity growth make a big difference to how much public spending can grow or how much taxes have to rise to keep the government within its fiscal rules. The role of the Office of Budgetary Responsibility in making forecasts is key here, but its record in predicting future productivity growth is frankly risible, as I discussed in the context of the Spring Statement:

Why productivity growth is important – Spring Statement 2025 Edition

Productivity and GDP per capita are technical concepts, so it might be thought that these issues aren’t relevant to people’s everyday lives. Nothing could be further from the truth – the slowdown in productivity is directly reflected in peoples’ earnings, shown dramatically in this plot from:

The End of Wage Growth in the UK

Average real weekly UK wages. Green: Composite Average Weekly Earnings series, corrected for inflation using consumer prices index. Thomas, R and Dimsdale, N (2017) “A Millennium of UK Data”, Bank of England OBRA dataset. Brown: ONS, Real Average Weekly Earnings, total pay, using CPI (seasonally adjusted). 18/2/2025 release.

Everything that’s wrong with politics and economics in the UK can be traced back to stagnating productivity.

Towards economic growth, energy and progress

Is this economic stagnation inevitable? I don’t think so – I believe it to be the result of policy choices the country has made, and different choices are possible. I welcome a growing movement of commentators and think-tanks exploring concrete policy ideas to break the stagnation, though I don’t always agree with their priorities. At the end of last year, I wrote what I hope comes across as a sympathetic critique of one strand of thought –

Taking Anglofuturism Seriously

One theme that is at the centre of much of this kind of writing prioritises cheap, abundant energy, with a new roll-out of nuclear power put centre-stage. I’m in sympathy with this, though I don’t think the analysis of the recent failure of the UK to build new nuclear power stations goes far enough. In 2014, the government planned to build 18 GW of new nuclear power; as I write, none has been delivered, and only 3.2 GW is under construction. Much emphasis is placed on the need to remove regulatory barriers; this in my view is necessary, but not sufficient: more thought needs to be given to how to rebuild national capabilities, as I argue here:

Ownership, Control, National capability: learning lessons from the UK’s nuclear new build debacle

Another recent feature of the UK economy is a rapid decline in the share of the economy accounted for by manufacturing – a decline shared by other developed economies, but which has been particularly large in the UK. Manufacturing now accounts for 8% of UK economy; should we try & increase this? I think so, but it’s important to distinguish some good arguments for this from bad ones (and recognise some uncertainties). Manufacturing matters for its potential for productivity growth – what’s important is the value it creates, not the jobs. Manufacturing capability is also important for national security, but realism is needed about UK’s position as <3% of world high tech economy – we need to aim for security, not autarky.

Good reasons and bad reasons for supporting manufacturing (and some uncertainties) 

On artificial intelligence

Inevitably, I have written about artificial intelligence. I don’t think anyone knows how this story is going to play out, least of all me, so back in May I sketched out three scenarios for the economic impact of AI:

1. Intelligence explosion – the Silicon Valley vision of AI entering a state of recursive self-improvement, leading to artificial general intelligence, and a winner takes all economy, in which the controllers of the new technologies enjoy unprecedented political and economic power.

2. Excel in prose – in which AI is understood as a powerful normal technology, whose applications lead to significant productivity gains across a number of sectors, but with a delay as business processes have to be adapted to make the most of the new technology.

3. Crash and burn – in which the revenues generated by applications of AI are disappointing, and can’t justify the huge capital investments have been made in AI infrastructure. The subsequent bursting of a financial bubble leads to systemic damage to the world financial system and the real economy.

Writing in May, I described “Crash and burn” as a contrarian scenario, but in the last few months it seems to have become mainstream; one can’t open up the Financial Times app without coming across an AI Bubble article.

The economic impact of AI: three scenarios  

One aspect of the AI story that I think has been neglected is the state of the material base that underlies the technology – the integrated circuits that are used to train and run the AI models. For many decades, we came to rely on an exponential increase in computer power, arising from the miniaturisation of the circuit components expressed in Moore’s Law.

Moore’s Law is still evoked by commentators as a symbol of accelerating technological change, but in fact the rate of increase in raw computer power has slowed substantially over the last two decades. Available computer power for applications such as large language models is still increasing, but this increased power is coming, less from miniaturisation, more from software, specialised architectures optimised for particular tasks, and advanced packaging of chips.

  Minimum transistor footprint (product of metal pitch and contacted gate pitch) for successive semiconductor process nodes. Data: (1994 – 2014 inclusive) – Stanford Nanoelectronics Lab, post 2017 and projections, successive editions of the IEEE International Roadmap for Devices and Systems

In the classical heyday of Moore’s Law, from the mid 1980’s to the mid 2000’s, computer power grew at a rate of 50% a year compounded, doubling every two years. In this extraordinary period, there was more than a thousandfold cumulative increase over a couple of decades.

Now, in contrast, it is not the supply of computer power that is increasing exponentially; we have an exponential increase in demand, while the increase in supply has more of a linear character.

Moore’s Law, past and future 

In “AI and the manufacturing firm of the future”, I ask how AI will change ht world of manufacturing. Sam Altman, CEO of OpenAI, has written about a manufacturing singularity, with AGI powered humanoid robots building factories to make more robots. I ask, as politely as I can, whether this vision reflects his lack of understanding of the material base of our industrial world, is a somewhat overheated metaphor, or is just bullshit (in Harry Frankfurt’s sense – i.e. an utterance whose intended effect is uncoupled to any truth value).

An alternative scenario is of AI driving process & system optimisation in increasingly automated factories. If Altman’s vision is driving strategies in the USA, I think the latter scenario is the one being aggressively pursued in China. We’ll see which is closer to reality.

AI and the manufacturing firm of the future 

UK science and university policy

Until my retirement at the end of September this year it was very much part of my day job to think about science and university policy in the UK. UK Universities have been under huge financial pressure in recent years, so some might be tempted to step back from their role in their communities. In this piece I argued that this would be a big mistake, and instead they should take even more seriously their role supporting regional economies.

The civic university in hard times 

The next piece offers a much more personal view of the role of universities in their regions – it’s a retrospective on my time as Vice-President for Regional Innovation and Civic Engagement at the University of Manchester, reviewing the progress we have made working with partners in the city-region to realise the University’s potential to support Greater Manchester’s economy.

On leaving the University of Manchester

Finally, my most popular post of the year was this rather provocative piece: UK Science in a post-liberal world. Here, I argue that a multi-decade period of consensus in UK science policy is likely soon to come to an end, and that the UK’s research system must respond to a new focus on re-building, re-energising, re-arming and re-industrialising for a changed & hostile world.

UK Science in a post-liberal world 

Family matters

To turn to personal matters, my mother, Sheila Jones, died on October 31st this year, a little more than two years after the death of my father, Robbie Jones. I found it helpful to write these two pieces to celebrate their lives, and to reflect on where I have come from.

Sheila Howell Jones (1934 – 2025) ,  Robert Cecil Jones (1932 – 2023) 

On leaving the University of Manchester

This year marked the end of my full-time career as an academic – I retired from the University of Manchester at the end of September 2025. I was a lecturer at Cambridge University from 1989 to 1998, when I moved to the University of Sheffield. I was a professor of physics at Sheffield, and also, between 2009 and 2016, Pro-Vice-Chancellor for Research and Innovation. I moved to the University of Manchester in 2020, where latterly I have had the role of Vice-President for Regional Innovation and Civic Engagement. I was touched and honoured by the kind words spoken about me at an event to mark my retirement in September.  UoM President Duncan Ivison, Manchester City Council Chief Executive Tom Stannard, and the Chair of UoM’s Board of Governors Phillipa Hurd all spoke, and GM Mayor Andy Burnham sent a video message.  In response, I said something along these lines:

Thanks for all your kind words.  I’m conscious that I’ve only been at Manchester for 5 years, in contrast to many of you who have devoted a much longer time to the institution.

My career has taken me from Cornell, through Cambridge, to Sheffield (with quite a lot of time in Swindon, first on secondment to run the cross-council nanotechnology programme, then as EPSRC Council Member), and, as Duncan said, it’s taken a number of twists and turns – I often describe myself as a deviant physicist.  There’s been science – both blue skies and highly collaborative with industry, public engagement, science policy, and contributions to local economic development and attempts to influence national policy.

I think my time at Manchester has been a culmination of that career, where I’ve been able to bring together all those different strands in the service of a great university in a great city.

Continue reading “On leaving the University of Manchester”

UK Science in a post-liberal world

I took part in a panel discussion at the Royal Society last Thursday 20th November, with the topic ‘The future role of the state in a changing R&D landscape’.  Here is a slightly expanded version of my opening remarks. My intention was to be provocative; other more optimistic views are available (and were presented by the other panel members).

The UK has seen a long period of consensus about the role of the state in the R&D landscape – and that consensus has actually been very positive for academic science.  I think it’s not improbable, even quite likely, that this consensus will come to an end in the next few years.  This will have huge consequences for the R&D landscape, potentially very negative, and I think it’s really important that we start to think this through.

The consensus

There’s been a great deal of continuity in UK science policy over the last twenty years.  The 2004 10 Year Science and Innovation Investment Framework set out the foundations of an approach that has persisted through governments of different flavours, overseen by a series of influential science ministers – from Lord Sainsbury, through Lords Willetts, to Lord Vallance.  

This is fundamentally a supply side science policy, with a focus on correcting market failure.  Government supports basic science, ensures a pipeline of skilled people (including through skilled migration), and supports commercialisation of university research.  There’s been some gradual change – a bumpy path to a more explicit industrial strategy since Mandelson’s return to government in 2008, to the current fully developed version.  But the basic assumptions remain the same.

The consequences have been significant real terms increases in government R&D budgets, and a system dominated by research in universities.

Auguries of a breakdown

Why might we think this consensus is at risk?  If we look internationally, we see the USA, often seen as the natural partners of the UK in science as in other areas, we see direct attacks on the autonomy of funding agencies, and on the position of leading research universities.

Continue reading “UK Science in a post-liberal world”

The civic university in hard times

Universities in the UK at the moment are broke and unloved. In these circumstances, the temptation is going to be to withdraw to “core business” – teaching students, and for research intensives, doing the kind of research that pushes the institution up the international league tables, to attract the overseas students whose fees prop the whole system up. In a period of retrenchment, it might be tempting for managements to see supporting the role of universities in their communities as a dispensable luxury. I think this would be a profound mistake.

This isn’t to understate the difficulty UK universities find themselves in. Around three quarters of them are expected to be in deficit next year, and about a hundred are now actively restructuring or making staff redundant. This follows a 40% real terms erosion in fees for home students, and a business model, reliant on growing overseas student numbers, which has become both politically unpopular and exposed to geopolitical risk. The latest proposal – of a levy on international student fees – is both another financial blow, and a symbol of the way universities find themselves on the wrong side of culture war discourse. What’s quite clear is that, whatever recognition there might be in government of the university sector’s troubles, the sector is simply not a high priority for a government facing difficult issues on all sides. Continue reading “The civic university in hard times”

The world of business R&D (and the UK’s place in that world)

Most research and development (R&D) in the world is done not in universities or research institutes, but by businesses – big businesses can do more R&D than medium size countries. A useful snapshot of this world is provided by the 2024 EU Industrial R&D Investment Scoreboard, which came out in December. The scoreboard lists the top 2000 companies in the world by their annual R&D expenditure, classifying them by sector and nationality of headquarters. In total, this amounts to total R&D spend of €1257.7 billion (converted at market rates), which the authors believe accounts for 85% to 90% of worldwide R&D funded by the business enterprise sector.

Unsurprisingly, the top companies are US tech firms – Alphabet, Meta, Apple and Microsoft – which between them spend €127 billion. Number 5 is the German auto firm Volkswagen, Asia provides numbers 6 and 7 in the shape of China’s Huawei and Korea’s Samsung.

Taking the world as a whole, the top sectors are Software, accounting for 19% of the total, Pharma at 18%, Automobiles at 15%. Tech hardware accounts for 16% and Electronic & Electrical hardware another 7%. These last two categories do have some overlap – the former includes Apple, Huawei, Intel, Qualcomm, Nvidia, Cisco and TSMC, while the latter includes Samsung, Siemens and Hon Hai (aka Foxconn).

How does the UK do? The share of world business R&D done by UK domiciled firms is 2.8%, and there are just two UK companies in the top 100 – the pharmaceutical companies AstraZeneca and GSK.

Of course, where a company is domiciled and where it does its R&D aren’t necessarily the same. Roughly half of UK business R&D is done by overseas owned companies – for example, the significant R&D carried out in the UK by the auto company Jaguar LandRover is ascribed in these statistics to its Indian parent, Tata Motors. This is a very high fraction of R&D done by overseas firms, by comparison with other countries of a similar size. The positive interpretation of this is that it is a testament to the attractiveness of the UK as a place to do R&D. But control matters, and this exposes the UK to the risk that this R&D may be more footloose than R&D done my domestically owned firms.

We can get a sense of the sectors that the UK focuses on by comparing the UK shares with the global fraction.

Pharmaceuticals is a clear leader for the UK – it accounts for 49% of the UK owned business R&D, which amounts to 7.5% of the world total. There is an interesting aspect to this, however – it is completely dominated by the two giants, AstraZeneca and GSK. This is in contrast to the USA, where there is a significant tier of relatively recently founded companies that have emerged from the biotech revolution – such as Gilead, Amgen, Moderna, Regeneron and Vertex, all with € multibillion R&D spend. UK pharma scale-ups – like Bicycle Therapeutics and Immunocore – are still an order of magnitude smaller.

The other area of specialism for the UK is Banking – this accounts for 17% of the UK’s R&D; this represents 41% of the world R&D in this sector. Of course, there may be issues of what is classified as R&D in different companies.

Where UK firms are largely absent is in Software, Tech hardware and Electronic & Electrical hardware. Between them, these sectors dominate global business R&D, accounting for 42% of all business R&D. But the UK accounts for just 0.6% of world Software R&D, 0.45% in Electronic & Electrical hardware, and a tiny 0.046% of world R&D in Tech hardware. Once again, this doesn’t take into account of R&D carried out in the UK by overseas firms – for example, DeepMind’s work will be ascribed to its US owner, Alphabet. But it does suggest that the UK has largely missed out on innovation in the fastest moving areas of new technology in its domestically owned firms.

Finally, one might ask how effective markets are at allocating resources to the areas where the need for innovation is greatest. Given the urgency of climate change, and the need for innovation to drive down the costs of low carbon energy, it’s depressing to see that business R&D in the Alternative Energy sector accounts for just 0.23% of the world total, with Oil and Gas still accounting for 1.05%.

Research and Innovation in a Labour government

Above all, growth. The new government knows that none of its ambitions will be achievable without a recovery from the last decade and a half’s economic stagnation. Everything will be judged by the contribution it can make to that goal, and research and innovation will be no exception.

The immediate shadow that lies over UK public sector research and innovation is the university funding crisis. The UK’s public R&D system is dependent on universities to an extent that’s unusual by international standards, and university research depends on a substantial cross-subsidy, largely from overseas student fees, which amounted to £4.9 bn in 2020. The crisis in HE is on Sue Gray’s list of unexploded bombs for the new government to deal with.

But it’s vital for HE to be perceived, not just as a problem to be fixed, but as central to the need to get the economy growing again. This is the first of the new Government’s missions, as described in the Manifesto: “Kickstart economic growth to secure the highest sustained growth in the G7 – with good jobs and productivity growth in every part of the country making everyone, not just a few, better off.”

To understand how the government intends to go about this, we need to go back to the Mais Lecture, given this March by the new Chancellor of the Exchequer. As I discussed in an earlier post, the questions Reeves poses in her Mais Lecture are the following: “how Britain can pay its way in the world; of our productive capacity; of how to drive innovation and diffusion throughout our economy; of the regional distribution of work and opportunity; of how to mobilise investment, develop skills and tackle inefficiencies to modernise a sclerotic economy; and of energy security”.

Reeves calls her approach to answering these questions “securonomics”; this owes much to what the US economist Dani Rodrik calls “productivism”. At the centre of this will be an industrial strategy, with both a sector focus and a regional focus.

The sector focus is familiar, supporting areas of UK comparative advantage: “our approach will back what makes Britain great: our excellent research institutions, professional services, advanced manufacturing, and creative industries”.

The regional aspect aims to develop clusters and seeking to unlock the potential agglomeration benefits in our underperforming big cities, and connects to a wider agenda of further English regional devolution, building on the Mayoral Combined Authority model.

There is “a new statutory requirement for Local Growth Plans that cover towns and cities across the country. Local leaders will work with major employers, universities, colleges, and industry bodies to produce long-term plans that identify growth sectors and put in place the programmes and infrastructure they need to thrive. These will align with our national industrial strategy.”

Universities need to at the heart of this. The pressure will be on them, not just to produce more spin-outs and work with industry, but also to support the diffusion of innovation across their regional economies. There are no promises of extra money for science – instead, as in other areas, the implicit suggestion seems to be that policy stability itself will yield better value:

“Labour will scrap short funding cycles for key R&D institutions in favour of ten-year budgets that allow meaningful partnerships with industry to keep the UK at the forefront of global innovation. We will work with universities to support spinouts; and work with industry to ensure start-ups have the access to finance they need to grow. We will also simplify the procurement process to support innovation and reduce micromanagement with a mission-driven approach.”

Beyond the government’s growth imperative, its priorities are defined by its other four missions; in clean energy, tackling crime, widening opportunities for people, and rebuilding the healthcare system. Research and Innovation, and the HE sector more widely, need to play a central role in at least three of these missions.

A commitment to cheap, zero carbon electricity by 2030 is a very stretching target, despite some advantages: “our long coast-line, high winds, shallow waters, universities, and skilled offshore workforce combined with our extensive technological and engineering capabilities.” Here the “strategy” part of industrial strategy is going to be vital – getting the balance right between the technologies that the UK will develop itself and those it imports from international balance will be vital. The call is to double onshore wind, triple solar, and quadruple offshore wind. There is a commitment to new nuclear build, including small modular reactors, and recognition of the importance of upgrading the grid and improving home insulation. R&D will need to be focused to support renewables, new nuclear and grid upgrades.

In health, commitments to address health inequalities imply higher priority on prevention, with high hopes placed on data and AI: “the revolution taking place in data and life sciences has the potential to transform our nation’s healthcare. The Covid-19 pandemic showed how a strong mission-driven industrial strategy, involving government partnering with industry and academia, could turn the tide on a pandemic. This is the approach we will take in government.” This statement gains more significance following the appointment of Sir Patrick Vallance as Science Minister, as I’ll discuss below.

There’s long been a tension between the high hopes that a succession of UK governments have placed on a strong life sciences sector, and a perception that the NHS is an organisation that’s not particularly innovative. So it’s unsurprising to read that “as part of Labour’s life sciences plan, we will develop an NHS innovation and adoption strategy in England. This will include a plan for procurement, giving a clearer route to get products into the NHS coupled with reformed incentive structures to drive innovation and faster regulatory approval for new technology and medicines.” I am sure this is correct in principle, and many such opportunities exist, but it will be difficult to take this forward until the immediate funding crisis faced by most parts of the NHS is overcome.

The new government’s fourth mission is to “break down barriers to opportunity”. A big part of this is to reform post-16 education (in England, one should add, as education is a devolved responsibility in Wales, Scotland and Northern Ireland). Universities will need to get used to there being more focus on the neglected FE sector, from which specialised “Technical Excellence Colleges” will be created, and should ready themselves for a more collaborative relationship with their neighbouring FE colleges: “to better integrate further and higher education, and ensure high-quality teaching, Labour’s post-16 skills strategy will set out the role for different providers, and how students can move between institutions, as well as strengthening regulation.”

There’s one important priority that wasn’t in the original list of five missions, but can’t now be ignored: the threatening geopolitical situation inevitably means a renewed focus on defence. The new government is explicit about the role of the defence industrial base in this:

“Strengthening Britain’s security requires a long-term partnership with our domestic defence industry. Labour will bring forward a defence industrial strategy aligning our security and economic priorities. We will ensure a strong defence sector and resilient supply chains, including steel, across the whole of the UK. We will establish long-term partnerships between business and government, promote innovation, and improve resilience.”

As the MoD budget grows, defence R&D will grow in importance. It’s perhaps not widely enough appreciated how much, following the end of the Cold War, the major focus of the UK’s research effort switched from defence to health and life sciences, so this will represent a partial turn-around of a decades-long trend.

How is the new government actually going to achieve these ambitious goals? Much stock is being placed on “mission led government”, in which Whitehall departments effortlessly collaborate to deliver goals which cross the boundaries between departments. In its first day, the new government made one unexpected announcement, which I think offers a clue as to how serious it is about this. That was the appointment of Sir Patrick Vallance as Science Minister.

Vallance, of course, has an outstanding background to be a Science Minister, as a highly successful researcher who then led R&D at one of the UK’s few world-class innovation led multinationals, GlaxoSmithKline. But, in the context of the new government’s ambitions, I think his most significant achievement, as Government Chief Scientific Advisor in the covid pandemic, was to set-up the Vaccine Task Force. If that’s going to be a model for how “mission led government” might work, we might see some exciting and rapid developments.

Research and innovation has a huge part to play in addressing the pressing challenges that face the new government, which necessarily cross Whitehall fiefdoms. The ambition in setting up the Department of Science, Innovation and Technology was to have a department coordinating science and innovation across the whole of government; it’s difficult to imagine anyone better qualified to realise this ambition than Vallance.

Quotations from the 2024 Labour Manifesto.

How much can artificial intelligence and machine learning accelerate polymer science?

I’ve been at the annual High Polymer Research Group meeting at Pott Shrigley this week; this year it had the very timely theme “Polymers in the age of data”. Some great talks have really brought home to me both the promise of machine learning and laboratory automation in polymer science, as well as some of the practical barriers. Given the general interest in accelerated materials discovery using artificial intelligence, it’s interesting to focus on this specific class of materials to get a sense of the promise – and the pitfalls – of these techniques.

Debra Audis, from the USA’s National Institute of Standards and Technology, started the meeting off with a great talk on how to use machine learning to make predictions of polymer properties given information about molecular structure. She described three difficulties for machine learning – availability of enough reliable data, the problem of extrapolation outside the parameter space of the training set, and the problem of explainability.

A striking feature of Debra’s talk for me was its exploration of the interaction between old-fashioned theory, and new-fangled machine learning (ML). This goes in two directions – on the one hand, Debra demonstrated that incorporating knowledge from theory can greatly speed up the training of a ML model, as well as improving its ability to extrapolate beyond the training set. But given a trained ML model – essentially a black box of weights for your neural network, Debra emphasised the value of symbolic regression to convert the black box to a closed form expression of simple functional forms of the kind a theorist would hope to be able to derive from some physical principles, providing something a scientist might recognise as an explanation of the regularities that the machine learning model encapsulates.

But any machine learning model needs data – lots of data – so where does that data come from? One answer is to look at the records of experiments done in the past – the huge corpus of experimental data contained within the scientific literature. Jacqui Cole from Cambridge has developed software to extract numerical data, chemical reaction schemes, and to analyse images from the scientific data. For specific classes of (non-polymeric) materials she’s been able to create data sets with thousands of entries, using automated natural language processing to extract some of the contextual information that makes the data useful. Jacqui conceded that polymeric materials are particularly challenging for this approach; they have complex properties that are difficult to pin down to a single number, and what to the outsider may seem to be a single material (polyethylene for example) may actually be a category that encompasses molecules with a wider variety of subtle variations arising from different synthesis methods and reaction conditions. And Debra and Jacqui shared some sighs of exasperation at the horribly inconsistent naming conventions used by polymer science researchers.

My suspicion on this (informed a little by the outcomes of a large scale collaboration with a multinational materials company that I’ve been part of over the last five years) is that the limitations of existing data sets mean that the full potential of machine learning will only be unlocked by the production of new, large scale datasets designed specifically for the problem in hand. For most functional materials the parameter space to be explored is vast and multidimensional, so considerable thought needs to be given to how best to sample this parameter space to provide the training data that a good machine learning model needs. In some circumstances theory can help here – Kim Jelfs from Imperial described an approach where the outputs from very sophisticated, compute intensive theoretical models were used to train a ML model that could then interpolate properties at much lower compute cost. But we will always need to connect to the physical world and make some stuff.

This means we will need automated chemical synthesis – the ability to synthesise many different materials with systematic variation of the reactants and reaction conditions, and then rapidly determine the properties of this library of materials. How do you automate a synthetic chemistry lab? Currently, a synthesis laboratory consists of a human measuring out materials, setting up the right reaction conditions, then analysing and purifying the products, finally determining their properties. There’s a fundamental choice here – you can automate the glassware, or automate the researcher. In the UK, Lee Cronin at Glasgow (not at the meeting) has been a pioneer of the former approach, while Andy Cooper at Liverpool has championed the latter. Andy’s approach involves using commercial industrial robots to carry out the tasks a human researcher would do, while using minimally adapted synthesis and analytical equipment. His argument in favour of this approach is essentially an economic one – the world market for general purpose industrial robots is huge, leading to substantial falls in price, while custom built automated chemistry labs represent a smaller market, so one should expect slower progress and higher prices.

Some aspects of automating the equipment are already commercially available. Automatic liquid handling systems are widely available, allowing one, for example to pipette reactants into multiwell plates, so if one’s synthesis isn’t sensitive to air one can use this approach to do combinatorial chemistry. Adam Gormley from Rutgers described this approach for making a library of copolymers by an oxygen-tolerant adaptation of reversible addition?fragmentation chain-transfer polymerisation (RAFT), to produce libraries of copolymers with varying polymer molecular weight and composition. Another approach uses flow chemistry, in which reactions take place not in a fixed piece of glassware, but as the solvents containing the reactants travel down pipes, as described by Tanja Junkers from Monash, and Nick Warren from Leeds. This approach allows in-line reaction monitoring, so it’s possible to build in a feedback loop, adjusting the ingredients and reaction conditions on the fly in response to what is being produced.

It seems to me, as a non-chemist, that there is still a lot of specific work to be done to adapt the automation approach to any particular synthetic method, so we are still some way from a universal synthesis machine. Andy Cooper’s talk title perhaps alluded to this: “The mobile robotic polymer chemist: nice, but does it do RAFT?” This may be a chemist’s joke.

But whatever approach one has realised to be able to produce a library of molecules with different characteristics, and analyse their properties, there remains the question of how to sample what is likely to be a huge parameter space in order to provide the most effective training set for machine learning. We were reminded by the odd heckle from a very distinguished industrial scientist in the audience that there is a very classical body of theory to underpin this kind of experimental strategy – the Design of Experiments methodology. In these approaches, one selects the optimum set of different parameters in order most effectively to span parameter space.

But an automated laboratory offers the possibility of adapting the sampling strategy in response to the results as one gets them. Kim Jelfs set out the possible approaches very clearly. You can take the brute force approach, and just calculate everything – but this is usually prohibitively expensive in compute. You can use an evolutionary algorithm, using mutation and crossover steps to find a way through parameter space that optimises the output. Bayesian optimisation is popular, and generative models can be useful for taking a few more random leaps. Whatever the details, there needs to be a balance between optimisation and exploration – between taking a good formulation and making it better, and searching widely across parameter space for a possibly unexpected set of conditions that provides a step-change in the properties one is looking for.

It’s this combination of automated chemical synthesis and analysis, with algorithms for directing a search through parameter space, that some people call a “self-driving lab”. I think the progress we’re seeing now suggests that this isn’t an unrealistic aspiration. My somewhat tentative conclusions from all this:

  • We’re still a long way from an automated lab that can flexibly handle many different types of chemistry, so for a while its going to be a question of designing specific set-ups for particular synthetic problems (though of course there will be a lot of transferrable learning).
  • There is still lot of craft in designing algorithms to search parameter space effectively.
  • Theory still has its uses, both in accelerating the training of machine learning models, and in providing satisfactory explanations of their output.
  • It’s going to take significant effort, computing resource and money to develop these methods further, so it’s going to be important to select use cases where the value of an optimised molecule makes the investment worthwhile. Amongst the applications discussed in the meeting were drug excipients, membranes for gas separation, fuel cells and batteries, optoelectronic polymers.
  • Finally, the physical world matters – there’s value in the existing scientific literature, but it’s not going to be enough just to process words and text; for artificial intelligence to fulfil its promise for accelerating materials discovery you need to make stuff and test its properties.

Implications of Rachel Reeves’s Mais Lecture for Science & Innovation Policy

There will be a general election in the UK this year, and it is not impossible (to say the least) that the Labour opposition will form the next government. What might such a government’s policies imply for science and innovation policy? There are some important clues in a recent, lengthy speech – the 2024 Mais Lecture – given by the Shadow Chancellor of the Exchequer, Rachel Reeves, in which she sets out her economic priors.

In the speech, Reeves sets out in her view, the underlying problems of the UK economy – slow productivity growth leading to wage stagnation, low investment levels, poor skills (especially intermediate and technical) and “vast regional disparities, with all of England’s biggest cities outside London having productivity levels below the national average”. I think this analysis is now approaching being a consensus view – see, for example, this recent publication – The Productivity Agenda – from The Productivity Institute.

Interestingly, Reeves resists the temptation to blame everything on the current government, stressing that this situation reflects long-standing weaknesses, which began in the early 1990’s, which were not sufficiently challenged by the Labour governments of the late 90’s and 00’s, and then were made much worse in the 2010’s by Austerity, Brexit, and post-pandemic policy instability. Singling out Conservative Chancellor of the Exchequer Nigel Lawson as the author of policies that were both wrong in principle and badly executed, she identifies this period as the root of “an unprecedented surge in inequality between places and people which endures today. The decline or disappearance of whole industries, leaving enduring social and economic costs and hollowing out our industrial strength. And – crucially – diminishing returns for growth and productivity.”

To add to our problems, Reeves stresses that the external environment the UK now faces is much more challenging than in previous decades, with geopolitical instability reviving the basic question of national security, uncertainties from new technologies like AI, and the challenges of climate instability and the net zero energy transition. She is blunt in saying “globalisation, as we once knew it, is dead”“a growth model reliant on geopolitical stability is a growth model resting on increasingly shallow foundations.”

What comes next? For Reeves, the new questions are “how Britain can pay its way in the world; of our productive capacity; of how to drive innovation and diffusion throughout our economy; of the regional distribution of work and opportunity; of how to mobilise investment, develop skills and tackle inefficiencies to modernise a sclerotic economy; and of energy security”, and the answers are to be found what economist Dani Rodrik calls “productivism”.

In practise, this means an industrial strategy which, recognising the limits of central government’s information and capacity to act, works in partnership. This needs to have both a sector focus – building on the UK’s existing areas of comparative advantage and its strategic needs – and a regional focus, working with local and regional government to support the development of clusters and the realisation of agglomeration benefits.

In terms of the mechanics of the approach, Reeves anticipates that this central mission of government – restoring economic growth – will be driven from the Treasury, through a a beefed up “Enterprise and Growth” unit. To realise these ambitions, she identifies three areas of focus – recreating macroeconomic stability, investment – particularly in partnership with the private sector, and reform – of the planning system, housing, skills, the labour market and regional governance.

Innovation is a central part of Reeves’s vision for increased investment, partly through the familiar call for more capital to flow to university spin-outs. But there is also a call for more focus on the diffusion of new technologies across the whole economy, including what Reeves has long called the “everyday economy”. In my view, this is correct, but will need new institutions, or the adaptation of existing ones (as I argued, with Eoin O’Sullivan: “What’s missing in the UK’s R&D landscape – institutions to build innovation capacity”). There is a very sensible commitment to a ten year funding cycle for R&D institutions, essential not least because some confidence in the longevity of programmes is essential to give the private sector the confidence to co-invest.

This was quite a dense speech, and the commentary around it – including the pre-briefing from Labour – was particularly misleading. I think it would be a mistake to underestimate how much of a break it represents from the conventional economic wisdom of the past three decades, though the details of the policy programme remain to be filled in, and, as many have commented, its implementation in a very tough fiscal environment is going to be challenging. Our current R&D landscape isn’t ideally configured to support these aspirations and the UK’s current challenges (as I argue in my long piece “Science and innovation policy for hard times: an overview of the UK’s Research and Development landscape”); I’d anticipate some reshaping to support the “missions” that are intended to give some structure to the Labour programme. And, as Reeves says unequivocally, of these missions, the goal of restoring productivity and economic growth is foundational.

Science and Innovation in the 2023 Autumn Statement

On the 22nd November, the Government published its Autumn Statement. This piece, published in Research Professional under the title Economic clouds cast gloom over the UK’s ambitions for R&D, offers my somewhat gloomy perspective on the implications of the statement for science and innovation.

This government has always placed a strong rhetorical emphasis on the centrality of science and innovation in its plans for the nation, though with three different Prime Ministers, there’ve been some changes in emphasis.

This continues in the Autumn Statement: a whole section is devoted to “Supporting the UK’s scientists and innovators”, building on the March 2023 publication of a “UK Science and Technology Framework”, which recommitted to increasing total public spending on research to £20 billion in FY 2024/25. But before going into detail on the new science-related announcements in the Autumn Statement, let’s step back to look at the wider economic context in which innovation strategy is being made.

There are two giant clouds in the economic backdrop the Autumn Statement. One is inflation; the other is economic growth – or, to be more precise, the lack of it.

Inflation, in some senses, is good for governments. It allows them to raise taxes without the need for embarrassing announcements, as people’s cost-of-living wage rises take them into higher tax brackets. And by simply failing to raise budgets in line with inflation, public spending cuts can be imposed by default. But if it’s good for governments, it’s bad for politicians, because people notice rising prices, and they don’t like it. And the real effect of stealth public spending cuts do, nonetheless, materialise.

The effect of the inflation we’ve seen since 2021 is a rise in price levels of around 20%; while the inflation rate peak has surely passed, prices will continue to rise. We can already see the effect on the science budget. Back in 2021, the Comprehensive Spending Review announced a significant increase in the overall government research budget, from £15 billion to £20 billion in 24/25. By next year, though, the effect of inflation will have been to erode that increase in real terms, from £5 billion to less than £2 billion in 2021 money. The effect on Core Research is even more dramatic; in effect inflation will have almost totally wiped out the increase promised in 2021.

Our other problem is persistent slow economic growth, as I discussed here. The underlying cause of this is the dramatic decrease in productivity growth since the financial crisis of 2008. The consequence is the prospect of two full decades without any real growth in wages, and, for the government, the need to simultaneously increase the tax burden and squeeze public services in an attempt to stabilise public debt.

The detailed causes of the productivity slowdown are much debated, but the root of it seems to be the UK’s persistent lack of investment, both public and private (see The Productivity Agenda for a broad discussion). Relatively low levels of R&D are part of this. The most significant policy change in the Autumn Statement does recognise this – it is a tax break allowing companies to set the full cost of new plant and machinery against corporation tax. On the government side, though, the plans are essentially for overall flat capital spending – i.e., taking into account inflation, a real terms cut. Government R&D spending falls in this overall envelope, so is likely to be under pressure.

Instead, the government is putting their hopes on the private sector stepping up to fill the gap, with a continuing emphasis on measures such as R&D tax credits to incentivise private sector R&D, and reforms to the pension system – including the “Long-term Investment for Technology and Science (LIFTS)” initiative – to bring more private money into the research system. The ambition for the UK to be a “Science Superpower” remains, but the government would prefer not to have to pay for it.

One significant set of announcements – on the “Advanced Manufacturing Plan” – marks the next phase in the Conservatives’ off-again, on-again relationship with industrial strategy. Commitments to support advanced manufacturing sectors such as aerospace, automobiles and pharmaceuticals, as well as the “Made Smarter” programme for innovation diffusion, are very welcome. The sums themselves perhaps shouldn’t be taken too seriously; the current government can’t bind its successor, whatever its colour, and anyway this money will have to be found within the overall spending envelope produced by the next Comprehensive Spending Review. But it is very welcome that, after the split-up of the Department of Business, Energy and Industrial Strategy, that the successor Department of Business and International Trade still maintains an interest in research and innovation in support of mainstream business sectors, rather than assuming that is all now to be left to its sister Department of Science, Innovation and Technology.

For all the efforts to create a tax-cutting headline, the economic backdrop for this Autumn statement is truly grim. There is no rosy scenario for the research community to benefit from; the question we face instead is how to fulfil the promises we have been making that R&D can indeed lead to productivity growth and economic benefit.