Science on the double: How an AI-powered 'digital twin' accelerates chemistry and materials discoveries

Understanding what complex chemical measurements reveal about materials and reactions can take weeks or months of analysis. But now, an AI-powered platform developed by researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) could reduce this interpretation cycle to minutes, enabling much faster insight into chemical processes relevant to energy storage, catalysis, and manufacturing.

The new platform, called "Digital Twin for Chemical Science" (DTCS), allows researchers to observe chemical reactions, adjust experimental parameters, and validate hypotheses simultaneously during a single experiment. Traditional approaches require researchers to first develop a hypothesis, and then design an experiment to collect data and develop theoretical models to analyze that data before they can finally conduct follow-up experiments to validate the model.

"A common challenge that many researchers face during complex experiments is that although we have sophisticated tools that collect data, interpreting that data is another beast," said Jin Qian, a computational chemist and staff scientist in Berkeley Lab's Chemical Sciences Division who designed the DTCS platform.

"Traditionally, we collect as much data as possible, then run simulations to analyze it offline. This back-and-forth process often takes months before theory and experiment reach consensus. DTCS could help overcome this bottleneck."

The advance is a significant step toward autonomous chemical characterization, where AI-guided experiments could accelerate the timeline for discovering and characterizing new materials and chemical processes for useful applications.

"The Digital Twin for Chemical Science platform represents a new capability for Berkeley Lab's Advanced Light Source (ALS) and DOE's scientific user facilities," said Ethan Crumlin, a staff scientist at the ALS and program lead specializing in interface chemistry and characterization. "The idea of partnering with a computational, machine-learning construct will be the future for how science is done."

Crumlin and Qian are co-lead authors of a study and research briefing on DTCS published in the journal Nature Computational Science.

Digital twins for the win

Chemistry is entering a new digital era, from automated synthesis labs to voice-activated quantum calculations, Qian explained. And yet chemical characterization which guides everything from material design to performance optimization has been left behind. The DTCS platform is changing this by enabling chemical insight with digital twins.

Broadly defined, digital twins are virtual replicas that use real-time data from physical systems to model a complex system's performance and predict future behavior.

While digital twins have been used for decades in aerospace, health care, and manufacturing, DTCS is one of the first digital twins designed specifically for chemical research, and one of the first digital twins to augment the characterization of chemical reactions at interfaces. DTCS is one of several digital twin technologies that the Department of Energy is developing to accelerate innovation across various sectors, including nuclear energy, smart grids, and the chemical sciences.

DTCS could bring new insights into interface science and catalysis chemical processes critical to batteries, fuel cells, and chemical manufacturing. By pairing DTCS with state-of-the-art spectroscopy instruments, researchers can now understand step-by-step reaction mechanisms in real time.

Building on decades of innovation

For the study, the Berkeley Lab team created a digital replica of ambient-pressure X-ray photoelectron spectroscopy (APXPS) techniques at the ALS, Berkeley Lab's synchrotron X-ray user facility, available to scientists around the world. Synchrotrons are specialized particle accelerators that produce ultrabright X-ray light for scientific research.

To develop the DTCS code, Qian used computing resources at the National Energy Research Scientific Computing Center (NERSC), the mission computing facility for the U.S. Department of Energy Office of Science at Berkeley Lab. "NERSC, especially NERSC's JupyterHub, has been instrumental in hosting the DTCS platform to rapidly connect supercomputer-generated theoretical data and facility-specific experimental data," she said.

Over the past two decades, the ALS has advanced the field of surface science by innovating APXPS instruments that have been adopted by synchrotron facilities worldwide and commercialized for energy applications. APXPS is one of the best ways to study interfacial chemistry because it shows how chemical species evolve during reactions. It identifies molecular compounds by their unique chemical "fingerprints" or spectra as they form on the solid surface of an operating device such as a battery. APXPS advances at the ALS have enabled powerful techniques for characterizing a wide array of interfaces including solid/gas, solid/liquid, solid/solid, and liquid/vapor interfaces under real-world operating conditions.

However, with conventional APXPS, researchers cannot practically use experimental spectra in real time to gain insights into how different chemical species are physically interacting at the atomic level on a surface. DTCS offers a powerful yet approachable alternative: By comparing experimental spectra and theoretical modeling, the DTCS platform gains insights about the dynamics of the reaction overall, the concentration of each species, the chemical potentials driving the reaction, and even the real-world likelihood of different molecules being in proximity to one another, representing an enormous leap in the power of interpreting APXPS spectra in real time.

In this one-minute clip, Ethan Crumlin, Deputy for Science in the Chemical Sciences Division and a staff scientist at the Advanced Light Source, explains how APXPS, a specialized technique at the Advanced Light Source, identifies a "rainbow" of interfacial chemistry products essential to high-performance batteries and other energy technologies.

Putting DTCS to the test

By optimizing experiments on the fly with real-time simulations of the interface, DTCS works through two connected pathways: The "forward loop" matches simulated spectra with experimental observations, while the "inverse loop" takes experimental data and solves for the underlying chemical mechanisms.

Data collected by an APXPS instrument teaches DTCS's AI algorithms which chemical reaction mechanisms and kinetic parameters led to the current observation. The platform's physics-based simulations provide real-time snapshots of a reaction and predict which experimental parameters within this "chemical reaction network" will be explored next.

To test the platform, the researchers studied a fundamental catalytic system a silver/water interface relevant to batteries, catalysis, and corrosion prevention. The results were striking: DTCS's predictions matched established experiments and theory, and the platform could predict how, when, and where oxygen-containing species would appear on the silver surface within minutes.

"This lets you see how the concentration profiles within the reaction network and spectra will evolve over time, and then you can compare that with what you're observing at the instrument," Qian said. "Instead of waiting weeks or months to analyze results, researchers can validate hypotheses and change experimental plans based on new findings in real time."

Looking ahead to DTCS 2.0

The research team is already developing DTCS 2.0, preparing it for broader community use and training its AI algorithms with new data. They're also building digital twins for other analytical techniques including Raman and infrared spectroscopy, which complement APXPS by providing information about chemical bonds.

The researchers expect to make DTCS available to other scientific institutions and user facilities within the next few years, potentially transforming how chemistry research is conducted worldwide.

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Breakthrough Calcium-Ion Battery Could Challenge Lithium for Clean Energy

A next-generation calcium battery breakthrough could challenge lithium and transform clean energy storage.

A research team at The Hong Kong University of Science and Technology (HKUST) has reported a major advance in calcium-ion battery (CIB) development that could influence how energy is stored in everyday technologies. By integrating quasi-solid-state electrolytes (QSSEs), the scientists created a new type of CIB designed to improve both performance and environmental sustainability.

The innovation could support renewable energy storage, electric vehicles, and other power-hungry applications. The results were published in Advanced Science in a paper titled “High-Performance Quasi-Solid-State Calcium-Ion Batteries from Redox-Active Covalent Organic Framework Electrolytes.”

Growing Demand for Lithium Alternatives

As global investment in renewable energy accelerates, the need for dependable, high-capacity batteries continues to rise. Lithium-ion batteries (LIBs) currently dominate the market, but concerns about limited lithium supplies and constraints in energy density have pushed researchers to search for alternatives. Exploring battery chemistries beyond lithium has become increasingly important for long-term energy security and sustainability.

Calcium-ion batteries offer several advantages. Calcium is widely available, and CIBs operate within an electrochemical window comparable to that of LIBs. Despite this promise, practical challenges have slowed their progress. Efficient movement of calcium ions inside the battery has been difficult to achieve, and maintaining stable performance over repeated charging cycles has proven problematic. These limitations have prevented CIBs from competing directly with commercial lithium-ion systems.

Redox Covalent Organic Framework Electrolytes

To address these technical barriers, the team led by Prof. Yoonseob KIM, Associate Professor in the Department of Chemical and Biological Engineering at HKUST, developed redox covalent organic frameworks that function as QSSEs. These carbonyl-rich QSSEs achieved strong ionic conductivity (0.46 mS cm–1) and Ca2+ transport capability (>0.53) at room temperature.

Through a combination of laboratory experiments and computational simulations, the researchers determined that Ca2+ ions move quickly along aligned carbonyl groups within the ordered pores of the covalent organic frameworks. This structured pathway enables faster ion transport and contributes to improved battery stability.

High Performance Over 1,000 Cycles

Using this approach, the team built a full calcium-ion battery cell that delivered a reversible specific capacity of 155.9 mAh g–1 at 0.15 A g–1. Even at 1 A g–1, the cell retained more than 74.6% of its capacity after 1,000 charge and discharge cycles. These results demonstrate the potential of redox covalent organic frameworks to significantly strengthen CIB technology and move it closer to practical use.

Prof. Kim said, “Our research highlights the transformative potential of calcium-ion batteries as a sustainable alternative to lithium-ion technology. By leveraging the unique properties of redox covalent organic frameworks, we have taken a significant step towards realizing high-performance energy storage solutions that can meet the demands of a greener future.”

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Boron Based Schiff Base: A DNA Breakthrough! #worldresearchawards #Analytical chemistry #research

 

This study reports the design, synthesis, and biological evaluation of a boron-based Schiff base as a selective DNA minor groove binder. Docking and molecular dynamics simulations elucidate binding affinity, stability, and interaction mechanisms, supporting its potential therapeutic applications.

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These Molecular Filters Thousands of Times Thinner Than a Human Hair Could Change How the World Cleans Water

Industrial separations sit quietly at the heart of modern manufacturing, yet they consume enormous amounts of energy and generate significant environmental costs. A new membrane technology developed by an international research team promises a more precise and sustainable alternative.

Scientists from the CSIR-Central Salt and Marine Chemicals Research Institute (CSMCRI), the Indian Institute of Technology Gandhinagar, Nanyang Technological University in Singapore, and the S N Bose National Centre for Basic Sciences have teamed up to build a new kind of filtration membrane designed for unusually sharp molecular sorting.

Reported in the Journal of the American Chemical Society, the approach could cut the energy cost of industrial purification and make large-scale water reuse more achievable.

A huge share of manufacturing depends on “separations.” That single word covers everything from removing unwanted byproducts during drug making to stripping color from textile wastewater to refining ingredients in food processing. Today, many of these steps still lean on distillation and evaporation, which work well but burn vast amounts of energy and add significantly to industrial carbon emissions.

Membrane systems are often viewed as a cleaner alternative because they can separate chemicals without repeatedly heating and cooling large volumes, but common polymer membranes have a persistent weakness: their pores vary in size and can change as the material ages. When the pore landscape shifts, selectivity drops, and that is a deal breaker for precision work.

A New Class of Crystalline Membranes

“To address these limitations, we engineered a new class of ultra-selective, crystalline membranes called “POMbranes”, which contain pores that are about one nanometer wide, thousands of times thinner than a human hair,” said Dr. Shilpi Kushwaha, Senior Scientist at CSMCRI.

That one-nanometer target is not just a small number. At this scale, tiny differences in molecular size and shape start to matter, which is why biology uses channels with near-perfect dimensions to control what passes through. The team drew inspiration from aquaporins, natural protein channels that let water through while blocking many other molecules, and aimed for the same kind of size-based decision-making in a synthetic material.

To do it, they turned to polyoxometalate (POM) clusters. These clusters already include a built in opening with a fixed diameter of exactly 1 nanometer, which means the filtering pathway is defined by the molecule itself rather than by a soft polymer that can slowly deform. According to Ms Priyanka Dobariya, a CSMCRI research scholar and co-first author of the article, “These POMs are tiny, crown-shaped metal clusters that have a permanent, perfect hole in their center that does not change or lose shape, which is the biggest hurdle with traditional plastic filters.”

Self-Assembly and Molecular Control

A membrane is only useful if it forms a continuous sheet without gaps, so the researchers focused on how to arrange enormous numbers of these ring-like clusters into a uniform layer. They attached flexible chemical chains to the clusters, then let the material assemble on the surface of water. Under those conditions, the clusters spread and align into an ultrathin film across large areas, a behavior that makes it easier to imagine scalable manufacturing rather than one-off laboratory samples.

By changing the chain length, the team could tune how tightly the clusters packed together. Tighter packing limits alternative routes around the pores, pushing molecules toward the designed pathway.

“This forced molecules to cross the membrane through the only open path, the one-nanometer holes built into each cluster, allowing the membrane to act like a high-tech sieve,” added Dr. Raghavan Ranganathan, Associate Professor at IITGN’s Department of Materials Engineering.

He and Mr Vinay Thakur, a PhD scholar at IITGN and the co-first author of the article, used molecular-level simulations to show how the structure guides transport and why the pores dominate what gets through.

Exceptional Selectivity and Industrial Performance

In tests, the membrane could tell apart molecules that differ in mass by only about 100 to 200 Daltons, a level of separation that conventional polymer membranes struggle to reach. For context, a Dalton is a unit used to describe molecular mass, so this result points to sorting that can discriminate between closely related compounds rather than just separating large from small.

According to Dr. Ketan Patel, Principal Scientist at CSMCRI, this level of control opens new possibilities for sustainable manufacturing. “Our membranes show almost ten times better separation performance compared to existing technologies, while remaining flexible, stable, and scalable,” he said. “Additionally, these membranes are flexible, stable across different acidity levels (pH ranges), and can be manufactured in large sheets. This combination is essential if the membranes are to be adopted widely in industry.”

That combination matters because real industrial streams are messy. Wastewater and process solvents can swing in acidity, include complex mixtures, and run continuously for long periods. A membrane that keeps its pore structure under those conditions becomes more than a laboratory curiosity.

The work is also closely tied to India’s textile and pharmaceutical industries. Textiles and apparel contribute over 2.3% of GDP and about 13% of industrial production, with a domestic market valued at USD 160 to 225 billion and projected to reach USD 250 to 350 billion by 2030.

Yet dyeing and finishing produce large volumes of polluted wastewater, so better dye removal and water recycling remain urgent. The new membranes could selectively remove dye molecules while allowing water to be reused, lowering freshwater demand and reducing chemical discharge. That is especially relevant as India’s wastewater treatment market is expected to expand rapidly in the coming years.

The new membranes could selectively remove dye molecules while allowing water to be reused, reducing freshwater consumption and chemical discharge. This is particularly significant as India’s wastewater treatment market is expected to grow rapidly in the coming years.

Toward Scalable, Nature-Inspired Manufacturing

For the pharmaceutical sector, where precise separations are essential for drug purity and cost-effective manufacturing, the technology could offer significant benefits. “Processes like drug purification and solvent recovery are both energy-intensive and quality-sensitive,” noted Mr Vinay Thakur. “Highly selective membranes such as these can lower energy use while maintaining the stringent standards required in pharmaceutical production.”

The versatility of the engineered POMbranes makes them an efficient platform technology. Their tunable structure, high selectivity, and stability under harsh chemical conditions ensure their suitability for a wide range of separation challenges, from wastewater treatment to advanced chemical processing.

As industries seek solutions that balance efficiency, durability, and sustainability, molecularly engineered membranes could form the backbone of next-generation manufacturing technologies. By drawing on a core principle from biology precise control at the molecular scale and translating it into a scalable materials system, the research shows how nature-inspired design can address real industrial needs.

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A Shimmering Liquid Metal Could Unlock the Future of Green Hydrogen

A new liquid-metal process powered by light could reshape how hydrogen is produced.

Scientists have found a new way to make clean hydrogen from water using liquid metal and light, and it works with both freshwater and seawater. Instead of relying on electricity to split water, the process uses sunlight to trigger chemistry at the surface of tiny metal droplets, releasing hydrogen gas.

That seawater capability is a big deal. Many existing green hydrogen approaches perform best with highly purified water, which adds cost and complexity and can be difficult to justify in water stressed regions.

By working directly with seawater, the new method points toward hydrogen production that could be located closer to coastlines and industrial ports where demand is high and freshwater is limited.

“We now have a way of extracting sustainable hydrogen, using seawater, which is easily accessible while relying solely on light for green hydrogen production,” said lead author and PhD candidate Luis Campos.

Liquid Metals and Efficiency Gains

Senior researcher Professor Kourosh Kalantar-Zadeh from the School of Chemical and Biomolecular Engineering describes the work as a powerful example of how liquid metals can naturally drive hydrogen production through their chemistry.

Using this method, the research team achieved a peak hydrogen production efficiency of 12.9 percent. While the system is still in its early stages, efforts are underway to further raise efficiency levels to support future commercial use.

“For the first proof-of-concept, we consider the efficiency of this technology to be highly competitive. For instance, silicon-based solar cells started with six percent in the 1950s and did not pass 10 percent till the 1990s.”

“Hydrogen offers a clean energy solution for a sustainable future and could play a pivotal role in Australia’s international advantage in a hydrogen economy,” says project co-lead Dr. Francois Allioux.

Gallium stood out because of its ability to absorb light. This property led researchers to examine how gallium behaves when dispersed in water and exposed to sunlight.

That investigation resulted in a system built around a circular chemical process. Tiny gallium particles are suspended in either freshwater or seawater and activated by sunlight or artificial illumination. During this process, gallium reacts with water to form gallium oxyhydroxide while releasing hydrogen gas.

“After we extract hydrogen, the gallium oxyhydroxide can also be reduced back into gallium and reused for future hydrogen production which we term a circular process,” says Professor Kalantar-Zadeh.

A Simple Reaction with Big Implications

Liquid gallium displays unusual physical characteristics. Although it appears solid at room temperature, warming it to around body temperature causes it to melt into reflective pools of liquid metal.

Mr Campos explained that liquid gallium typically has a chemically “non-sticky” surface, meaning other materials do not readily adhere to it under normal conditions. When the metal is exposed to light while submerged in water, however, reactions occur at its surface.

Under these illuminated conditions, gallium slowly oxidizes and corrodes. This surface reaction leads to the release of clean hydrogen gas and the formation of gallium oxyhydroxide, both of which are central to the hydrogen production process.

“Gallium has not been explored before as a way to produce hydrogen at high rates when in contact with water such a simple observation that was ignored previously,” says Professor Kalantar-Zadeh.

The University of Sydney-led research was published in Nature Communications.

Why scientists are so keen on hydrogen molecules

Many industries and scientists believe hydrogen is the ideal candidate for a sustainable energy source, contributing significantly to reducing greenhouse gas emissions. ‘Green’ hydrogen, as its name suggests, is made using renewable sources.

Hydrogen is one of the most abundant elements on Earth and can be sourced from a large range of compounds as well, such as water (water has two hydrogen molecules). When hydrogen burns, it produces no pollutants, only water, but still can generate high levels of energy or power.

Efforts to produce green hydrogen have focused on ‘water splitting’: splitting atoms in water molecules to release hydrogen using methods including electrolysis, photocatalysis, and plasma (artificial lightning).

But the process required to separate hydrogen and oxygen atoms in water has faced multiple obstacles, including the need to use purified water, incurring high cost or producing low yields of hydrogen.

The method Professor Kalantar-Zadeh’s team introduced with liquid gallium avoids many of those obstacles. The method can use both sea and fresh water, and because the process is circular, gallium in the reaction can be reused.

Professor Kalantar-Zadeh said: “There is a global need to commercialize a highly efficient method for producing green hydrogen. Our process is efficient and easy to scale up.”

The team is now working on increasing the efficiency of the technology, and their next goal is to establish a mid-scale reactor to extract hydrogen.

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AI model uses molecular energy to predict the most stable atom arrangements

Whether a smartphone battery lasts longer or a new drug can be developed to treat incurable diseases depends on how stably the atoms constituting the material are bonded. The core of molecular design lies in finding how to arrange these countless atoms to form the most stable molecule. Until now, this process has been as difficult as finding the lowest valley in a massive mountain range, requiring immense time and costs. Researchers at KAIST have developed a new technology that uses artificial intelligence (AI) to solve this process quickly and accurately.

Professor Woo Youn Kim's research team in the Department of Chemistry has developed the Riemannian denoising model (R-DM), an AI model that understands the physical laws governing molecular stability to predict structures. Their innovation is published in Nature Computational Science.

The most significant feature of this model is that it directly considers the energy of the molecule. While existing AI models simply mimic the shape of molecules, R-DM refines the structure by considering the forces acting within the molecule. The research team represented the molecular structure as a map where higher energy is depicted as hills and lower energy as valleys, designing the AI to move toward and find the valleys with the lowest energy.

R-DM completes the molecule by navigating this energy landscape, avoiding unstable structures to find the most stable state. This applies the mathematical theory of Riemannian geometry, resulting in the AI learning the fundamental law of chemistry: Matter prefers the state with the lowest energy.

Experimental results showed that R-DM achieved up to 20 times higher accuracy than existing AI models, reducing prediction errors to a level nearly indistinguishable from precise quantum mechanical calculations. This represents the world's highest level of performance among AI-based molecular structure prediction technologies.

This technology can be utilized in various fields, including new drug development, next-generation battery materials, and high-performance catalyst design. It is expected to serve as an "AI simulator" that will dramatically speed up research and development by significantly shortening the molecular design process, which previously took a long time. Furthermore, it has great potential in environmental and safety fields, as it can quickly predict chemical reaction paths in situations where experiments are difficult, such as chemical accidents or the spread of hazardous substances.

Professor Kim said, "This is the first case where artificial intelligence has understood the basic principles of chemistry and judged molecular stability on its own. It is a technology that can fundamentally change the way new materials are developed."

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Unlocking the Secrets of Ceria & SrFeO3! #worldresearchawards #Analytical chemistry #researchawards

 

This study investigates the crystal chemistry and interfacial stability of ceria and doped SrFeO₃ systems engineered with reduced critical raw materials, highlighting structure property relationships, defect chemistry, and long-term stability for sustainable energy and catalytic applications. 

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