Is Artificial Intelligence Replacing Scientists? / Scientific Discoveries Now Take Minutes Instead of Decades

Tehran - BORNA - For decades, some of the most complex mathematical equations in fundamental sciences like physics, chemistry, and climatology have posed formidable barriers to scientific progress. Now, artificial intelligence is revolutionizing this landscape by dramatically reducing the time required to solve these equations from years to just minutes.

Researchers who once waited for computational power or mathematical tricks to unlock these problems over the course of a decade can now resolve the same issues in a single afternoon. This paradigm shift is detailed in a recent 500-page review published in the journal Foundations and Trends in Machine Learning, showing how machine learning algorithms are redefining scientific workflows from drug design to climate modeling.

The Equation Bottleneck in Science

The natural world can be described through three primary regimes: quantum, atomic, and continuum. Each of these is governed by complex differential equations. According to co-author Shuai Wang from Texas A&M University, the fundamental goal of natural sciences is to understand the universe across varying physical and temporal scales, which maps directly to these three regimes.

Take the Schrödinger equation, the foundation of quantum mechanics. It offers an analytical solution for systems with only two electrons. But once millions of particles are involved, the equation becomes practically unsolvable.

As particle numbers grow, variables increase exponentially in a phenomenon known as the “curse of dimensionality,” which can overwhelm even supercomputers. Traditional numerical methods offer only approximate solutions and still require weeks of processing time. They are also limited in accuracy, hindering progress in fields like battery design or the discovery of new catalysts.

Teaching AI Advanced Mathematics

Machine learning models, trained on vast datasets, can recognize patterns that often escape human notice. Once trained, these models can predict wavefunctions or fluid pressures for new systems within seconds with accuracy comparable to traditional solvers.

Unlike black-box models, modern AI architectures are designed to respect physical symmetries. This means that rotations or reflections in the system do not affect the output, thereby enhancing both speed and reliability, as the models inherently comply with conservation laws and fundamental physical principles.

Ji and over 60 researchers introduce techniques like equivariant graph neural networks (EGNNs), which represent atoms as nodes and chemical bonds as edges. The review also explores the use of large language models for the automatic generation of simulation code—currently in use at Texas A&M’s RAISE lab.

A notable success came when a graph neural network reproduced outputs from density functional theory (DFT)—a process typically requiring a month—within just 10 minutes on a laptop. This massive reduction in computational costs now enables students to explore chemical spaces during a single lab session without needing to reserve high-performance computing clusters.

Practical Breakthroughs in Scientific Research

One of the most transformative applications of AI in science occurred with the release of the initial AlphaFold model in 2021. It predicted the 3D structure of nearly 200 million proteins within a year—an achievement that once took biologists months using crystallography.

Materials scientists are also leveraging graph neural networks to screen millions of potential battery electrolyte compounds, narrowing down only the most promising candidates for expensive laboratory testing. This has led to the development of lithium-ion batteries that withstand over 3,000 charge cycles in labs.
Climatologists have integrated neural networks into atmospheric circulation models, resulting in a 40% reduction in the daily energy consumption of climate simulations—without sacrificing storm path accuracy. This energy efficiency enables governments to simulate a wider range of climate scenarios without inflating their computing budgets.

The Limitations of AI in Science

"We use AI to accelerate scientific understanding and engineer better systems," says Ji. However, this vision depends on precise evaluation metrics, uncertainty quantification, and open-access data for reproducibility.

Data scarcity remains a key challenge in frontier science from turbulence in fusion plasmas to magnetic phase transitions in rare elements. Researchers often simulate synthetic datasets to fill these gaps, but doing so may introduce hidden biases the very thing AI aims to eliminate.

Ethical concerns are equally pressing. The same AI that speeds up drug discovery can also be used to design toxins or dangerous pathogens. Leading research groups now recommend screening all AI-generated molecules against bio-threat databases before synthesis.

Collaborative Scientific Solutions

Ji’s team emphasizes that no single lab can address the full scope of quantum, atomic, and continuum problems. Their comprehensive review, co-authored by over 60 scientists from 15 universities, stands as evidence.

The RAISE project unites more than 85 faculty members, connecting computer scientists, chemists, geologists, and civil engineers through platforms like Slack. Weekly data-sharing meetings help avoid redundant work and foster collaborative research initiatives.

Industry also plays a vital role. Pharmaceutical companies contribute reaction data to improved AI models and gain early access in return. Startups are making these models accessible via cloud-based APIs, allowing small labs to run high-level quantum chemistry simulations without purchasing expensive servers.

Scientific Discovery at Machine Speed

If the current trend continues, everyday researchers may soon have AI solvers on their desktops—much like Excel—enabling daily use.

At that point, the central question will no longer be, "Can we trust AI?" but rather, "How should we use the free time it gives us?"

Young scientists could spend mornings developing deeper hypotheses instead of debugging Fortran code, and veteran engineers might rapidly refine prototypes without waiting in server queues.

History shows that whenever a technology dramatically reduces costs, it triggers waves of experimentation and inquiry that were previously deemed too risky or expensive.

Policymakers also stand to gain. Faster simulations enable public agencies to test infrastructure designs under a broader array of climate or seismic scenarios. This could lead to regulations that protect communities based on realistic, not average, conditions.

End Article