AI Transforms Physics From Proteins To Quantum Realms

The AI Boom and Fundamental Questions

Artificial intelligence or AI is experiencing a massive surge in popularity and application. We see AI algorithms at work across numerous scientific fields, helping to predict protein structures, discover new materials with specific characteristics, and analyze medical data for diagnoses. Everyday AI tools like ChatGPT, Claude, NotebookLM, DALL E, Gemini, and Midjourney are also becoming common, used for creating images and videos from text, writing assistance, and web searches.

This widespread adoption naturally leads to a crucial question: Can these advanced AI tools also aid in exploring the fundamental properties of nature? Or is there a significant gap between human scientific capabilities and what AI can currently achieve that we must address first?

The Challenge of Black Box AI in Science

A gap does indeed exist. A primary concern with many current AI applications in scientific research is their "black box" nature. These models are trained on data and produce outputs, but the internal workings and the precise relationship between inputs and outputs often remain unclear.

This lack of transparency is a significant issue for the scientific community. For instance, last year DeepMind experienced pressure from life science researchers to make its AlphaFold model, which predicts protein structures, more inspectable.

This "black box" problem is equally concerning in the physical sciences, where understanding the process and the steps taken to reach a solution is often as vital as the solution itself. Despite these challenges, scientists have not been deterred. They began incorporating AI tools into the study of complex systems as early as the mid 1980s, with high energy physics following suit in 1990.

AI Revolutionizing Astro and High Energy Physics

In the fields of astronomy and astrophysics, scientists focus on the structure and dynamics of celestial bodies. Two critical tasks for researchers here are big data analytics and image enhancement. AI based algorithms are proving invaluable for the former by identifying patterns, anomalies, and correlations within vast datasets.

AI has truly transformed astrophysical observations. It automates tasks such as capturing images and tracking distant stars and galaxies. Furthermore, AI algorithms can compensate for Earths rotation and atmospheric disturbances, leading to improved observations in shorter timeframes. These algorithms also enable the automation of telescopes designed to detect fleeting, short lived events in the sky, recording crucial information in real time.

Experimental high energy physicists regularly work with enormous datasets. The Large Hadron Collider LHC experiment in Europe, for example, generates over 30 petabytes of data annually. Just one detector at the LHC, the Compact Muon Solenoid, captures 40 million 3D images of particle collisions every second. Analyzing such immense data volumes quickly enough to track significant subatomic events is a monumental challenge for human physicists.

To tackle this, LHC researchers implemented an AI model capable of accurately identifying particles of interest amidst very noisy data. This type of AI model was instrumental in the discovery of the Higgs boson particle more than a decade ago.

AI Intertwined with Statistical Physics

Statistical mechanics explores the collective behavior of particle groups rather than individual particles. This branch of physics helps us understand macroscopic properties such as temperature and pressure.

A classic example is the statistical model for magnetism developed by Ernst Ising in the 1920s. His model focused on how atomic spins interact collectively with their neighbors. It describes systems having higher and lower energy states, with materials tending towards the lowest energy state. The Boltzmann distribution is another cornerstone of statistical mechanics, used to predict phenomena like the precise conditions for ice to melt into water. Using this distribution, Ernst Ising and Wilhelm Lenz predicted the temperature at which a material transitions from magnetic to non magnetic in the 1920s.

Interestingly, the principles of statistical mechanics also inspired the development of neural networks by last year's physics Nobel laureates, John Hopefield and Geoffrey Hinton. A neural network NN is a model where interconnected nodes receive data and perform computations, processing information in a way analogous to animal brains. Consider an image composed of visible and hidden pixels. To understand the complete image, physicists must consider all possible arrangements of the hidden pixels. The concept of most likely states from statistical mechanics is useful here. Hopefield and Hinton's theory for NNs treated pixel interactions like neuron interactions, similar to Lenz and Ising's approach. A Hopfield network, for instance, calculates an image's energy by finding the least energy arrangement of hidden pixels, mirroring principles from statistical physics.

AI tools have, in turn, contributed significantly to statistical physics, particularly in the study of Bose Einstein condensates BECs. A BEC is an exotic state of matter formed by certain subatomic or atomic particles at extremely low temperatures, first created in labs in the early 1990s. In 2016, scientists at Australian National University employed AI to help establish the precise conditions for BEC formation. The AI succeeded impressively, even helping to maintain stable conditions and prolong the BEC's existence.

Paul Wigley, a coauthor of the study, remarked, "I didn’t expect the machine could learn to do the experiment itself, from scratch, in under an hour. A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out."

AI Pushing the Boundaries of Quantum Technology

AI is also making inroads into the quantum realm. In a 2022 research paper, scientists from Australia, Canada, and Germany detailed an AI assisted method that simplifies the entanglement of two subatomic particles. Quantum computing and related technologies are major areas of current research and practical interest, with significant government investment worldwide, including India, aiming to develop these futuristic capabilities. A substantial part of their revolutionary potential lies in achieving and manipulating quantum entanglement.

For instance, a key process in quantum computers is entanglement swapping. This allows two particles that have never directly interacted to become entangled through the use of intermediate entangled particles. The 2022 paper introduced a tool named PyTheus, described as "a highly efficient, open source digital discovery framework... which can employ a wide range of experimental devices from modern quantum labs." PyTheus is designed to improve the achievement of entanglement in quantum optic experiments.

Using PyTheus, scientists have achieved breakthroughs with significant implications for quantum networks, which are used for secure message transmission. These advancements are helping to make such quantum technologies more practical and feasible. While further research and development are necessary, tools like PyTheus clearly demonstrate AI's potential to make quantum exploration more efficient.

The Future of Physics Human AI Collaboration

Looking ahead, it appears that artificial intelligence AI and machine learning ML will soon be integral to every subfield of physics, assisting scientists in tackling their most challenging problems. The ultimate aim is to simplify the process of formulating better research questions, accelerate hypothesis testing, and gain deeper insights from experimental results. The next revolutionary discovery in physics could very well emerge from the powerful synergy between human ingenuity and the computational capabilities of machines.