Project acronym 3DX-FLASH
Project Probing MHz processes in 3D with X-ray microscopy
Researcher (PI) Pablo Villanueva Perez
Host Institution (HI) LUNDS UNIVERSITET
Country Sweden
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary I aim to develop an X-ray imaging technique capable of filming processes in 3D, with a temporal resolution several orders of magnitude faster than up-to-date 3D X-ray imaging techniques.
The unique penetration power of X-rays allows us to study systems in their native environment. This property has led to the development of X-ray microtomography (µCT). µCT acquires 3D information, which determines the functionality and mechanical properties of nature, by rotating a sample with respect to the X-ray source. µCT is a crucial tool for several scientific disciplines such as physics, biology, and chemistry.
Over the last decade, µCT has become a technique capable of not only recording 3D information but also filming dynamical processes. Several breakthroughs have made this possible: i) intense X-ray sources (synchrotron light sources), ii) efficient and fast X-ray detectors, and iii) fast 3D reconstruction algorithms. Despite all of these developments, the acquisition protocols remain unchanged, i.e., the sample is only rotated faster. This fast rotation introduces forces which may alter the studied dynamics and ultimately limit the achievable temporal resolution.
My project is to establish an X-ray microscope that avoids the sample rotation, obtaining 3D information from a single X-ray flash by splitting it into nine-angularly resolved beams which illuminate the sample simultaneously. This approach, when implemented at intense X-ray sources such as synchrotron light sources and X-ray free-electron lasers, will allow the filming of natural processes with micrometer to nanometer resolution and resolve dynamics from microseconds to femtoseconds. To demonstrate its capabilities, I will study fundamental processes in cellulose fibers, a renewable biomaterial, which can replace fossil-based materials, such as plastics. This technique will open up the possibility to film dynamics in 3D to answer questions coming from industry and natural sciences at rates not accessible today.
Summary
I aim to develop an X-ray imaging technique capable of filming processes in 3D, with a temporal resolution several orders of magnitude faster than up-to-date 3D X-ray imaging techniques.
The unique penetration power of X-rays allows us to study systems in their native environment. This property has led to the development of X-ray microtomography (µCT). µCT acquires 3D information, which determines the functionality and mechanical properties of nature, by rotating a sample with respect to the X-ray source. µCT is a crucial tool for several scientific disciplines such as physics, biology, and chemistry.
Over the last decade, µCT has become a technique capable of not only recording 3D information but also filming dynamical processes. Several breakthroughs have made this possible: i) intense X-ray sources (synchrotron light sources), ii) efficient and fast X-ray detectors, and iii) fast 3D reconstruction algorithms. Despite all of these developments, the acquisition protocols remain unchanged, i.e., the sample is only rotated faster. This fast rotation introduces forces which may alter the studied dynamics and ultimately limit the achievable temporal resolution.
My project is to establish an X-ray microscope that avoids the sample rotation, obtaining 3D information from a single X-ray flash by splitting it into nine-angularly resolved beams which illuminate the sample simultaneously. This approach, when implemented at intense X-ray sources such as synchrotron light sources and X-ray free-electron lasers, will allow the filming of natural processes with micrometer to nanometer resolution and resolve dynamics from microseconds to femtoseconds. To demonstrate its capabilities, I will study fundamental processes in cellulose fibers, a renewable biomaterial, which can replace fossil-based materials, such as plastics. This technique will open up the possibility to film dynamics in 3D to answer questions coming from industry and natural sciences at rates not accessible today.
Max ERC Funding
1 999 213 €
Duration
Start date: 2021-03-01, End date: 2026-02-28
Project acronym AI-DEMON
Project Artificial intelligence design of molecular nano-magnets and molecular qubits
Researcher (PI) Alessandro LUNGHI
Host Institution (HI) THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD, OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Country Ireland
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Summary
As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Max ERC Funding
1 499 786 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym Allosteric-CRISPR
Project Computational Investigations of Allostery between Proteins and Nucleic Acids in CRISPR-Cas9
Researcher (PI) Giulia Palermo
Host Institution (HI) TECHNISCHE UNIVERSITAET MUENCHEN
Country Germany
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Many emerging allosteric targets are large protein/nucleic acid complexes responsible for genome editing and regulation, whose underlying signaling remains poorly understood. Here, we focus on CRISPR-Cas9, a large nucleoprotein complex widely employed as a genome editing tool with enormous promises for medicine and biotechnology. In this system, an intricate allosteric signaling is suggested to span the multi-domain Cas9 protein and its associated nucleic acids, controlling the system’s function and specificity. However, in spite of extensive experimental characterization, the molecular basis for this allosteric response are largely unknown, hampering also efficient engineering for improving its genome editing capability. Allosteric-CRISPR will investigate the allosteric regulation in CRISPR-Cas9 by introducing a novel synergistic approach. This will implement the combination of state-of-the-art theoretical methods, including enhanced and multiscale approaches based on classical and ab-initio methods, with network models derived from graph theory and novel centrality analyses that are emerging as powerful to investigate allostery. This will create an innovative protocol that will enable determining the allosteric network of communication over multiple timescales, as well as the relation between allostery and catalysis, which remains unaddressed through classical approaches. This novel way to describe allostery can impact future studies of large nucleoprotein complexes, including newly discovered CRISPR systems, which are governed by similar allosteric rules and hold tremendous potential for genome editing. Finally, by delivering fundamental knowledge on the basic mechanisms underlying genome editing, Allosteric-CRISPR will help the design of improved genome editing tools, impacting their application across the field of life sciences.
Summary
Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Many emerging allosteric targets are large protein/nucleic acid complexes responsible for genome editing and regulation, whose underlying signaling remains poorly understood. Here, we focus on CRISPR-Cas9, a large nucleoprotein complex widely employed as a genome editing tool with enormous promises for medicine and biotechnology. In this system, an intricate allosteric signaling is suggested to span the multi-domain Cas9 protein and its associated nucleic acids, controlling the system’s function and specificity. However, in spite of extensive experimental characterization, the molecular basis for this allosteric response are largely unknown, hampering also efficient engineering for improving its genome editing capability. Allosteric-CRISPR will investigate the allosteric regulation in CRISPR-Cas9 by introducing a novel synergistic approach. This will implement the combination of state-of-the-art theoretical methods, including enhanced and multiscale approaches based on classical and ab-initio methods, with network models derived from graph theory and novel centrality analyses that are emerging as powerful to investigate allostery. This will create an innovative protocol that will enable determining the allosteric network of communication over multiple timescales, as well as the relation between allostery and catalysis, which remains unaddressed through classical approaches. This novel way to describe allostery can impact future studies of large nucleoprotein complexes, including newly discovered CRISPR systems, which are governed by similar allosteric rules and hold tremendous potential for genome editing. Finally, by delivering fundamental knowledge on the basic mechanisms underlying genome editing, Allosteric-CRISPR will help the design of improved genome editing tools, impacting their application across the field of life sciences.
Max ERC Funding
1 399 632 €
Duration
Start date: 2021-08-01, End date: 2026-07-31
Project acronym AMIGA
Project Autonomous Computing Artificial Cells
Researcher (PI) Tom DE GREEF
Host Institution (HI) TECHNISCHE UNIVERSITEIT EINDHOVEN
Country Netherlands
Call Details Consolidator Grant (CoG), PE4, ERC-2020-COG
Summary We propose an ambitious 5-year multidisciplinary program that seeks to pioneer and establish a fundamentally new paradigm in molecular information systems that is based on novel conceptual and experimental advances on the integration of DNA-based chemical reaction networks (CRNs) and semipermeable microcapsules, i.e. protocells. In AutonoMous computInG Artificial cells (AMIGA), we will establish a platform technology, based on molecular communication between interacting protocells, capable of revolutionary new modes of molecular sensing, computation and data storage/retrieval.
Progress in this emerging field requires i) the development of computer-aided design (CAD) strategies to implement large-scale CRNs consisting of hundreds of components, ii) formulating suitable micro-substrates, such as droplets or vesicles, to spatially localize CRNs and ways to manipulate their interconnection and iii) strategies that allow direct recording of molecular operations onto a chemical storage medium such as DNA. We address these challenges via a comprehensive research program in which we implement large-scale, DNA-based CRNs by localization of components in interacting protocells resulting in distributed molecular circuits programmed to display advanced computational functions such as (i) asynchronous logic, (ii) integral feedback control and (iii) molecular pattern recognition. Combining protocell localization with recent advances in CRISPR base editors, we will construct an integrated system where molecular operations can write instructions on permanent memory storage elements. The developed methodology finds applications in emerging technologies aimed at using molecular circuits for in-vitro diagnostics and the use of synthetic DNA as a storage medium for digital data.
Summary
We propose an ambitious 5-year multidisciplinary program that seeks to pioneer and establish a fundamentally new paradigm in molecular information systems that is based on novel conceptual and experimental advances on the integration of DNA-based chemical reaction networks (CRNs) and semipermeable microcapsules, i.e. protocells. In AutonoMous computInG Artificial cells (AMIGA), we will establish a platform technology, based on molecular communication between interacting protocells, capable of revolutionary new modes of molecular sensing, computation and data storage/retrieval.
Progress in this emerging field requires i) the development of computer-aided design (CAD) strategies to implement large-scale CRNs consisting of hundreds of components, ii) formulating suitable micro-substrates, such as droplets or vesicles, to spatially localize CRNs and ways to manipulate their interconnection and iii) strategies that allow direct recording of molecular operations onto a chemical storage medium such as DNA. We address these challenges via a comprehensive research program in which we implement large-scale, DNA-based CRNs by localization of components in interacting protocells resulting in distributed molecular circuits programmed to display advanced computational functions such as (i) asynchronous logic, (ii) integral feedback control and (iii) molecular pattern recognition. Combining protocell localization with recent advances in CRISPR base editors, we will construct an integrated system where molecular operations can write instructions on permanent memory storage elements. The developed methodology finds applications in emerging technologies aimed at using molecular circuits for in-vitro diagnostics and the use of synthetic DNA as a storage medium for digital data.
Max ERC Funding
1 999 497 €
Duration
Start date: 2022-02-01, End date: 2027-01-31
Project acronym DeepProton
Project Deep multi-scale modelling of electrified metal oxide nanostructures
Researcher (PI) Chao ZHANG
Host Institution (HI) UPPSALA UNIVERSITET
Country Sweden
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary One promising solution toward a sustainable society and a green economy is to use metal oxide-based materials. Metal oxides are a class of inorganic materials that have various energy and environmental applications such as heterogeneous catalyst, fuel cell, lithium-ion battery, supercapacitor, water treatment and antimicrobial application. Most metal oxides are synthesized as nanostructures which leads to unique properties and reduced economical costs. The very properties that make the metal oxide nanostructures attractive and indispensable in modern science and technology also cause an issue for the environment and human safety. In both the functioning and the degradation of metal oxide nanostructures, aqueous interface plays a vital role. The metal oxide-aqueous solution aqueous interface is electrified in working conditions due to acid-base chemistry and composed of protonic electric double layer. Given the importance of metal oxide surfaces in practical applications, surprisingly little is known about the relation between atomic structure of protonic double layer and the interfacial reactivity. This is largely due to the fact that our knowledge is mostly based on macroscopic observations such as current and concentration in electrochemistry and microscopic information of protonic double layer is difficult to be obtained in experiments. Therefore, developing a novel deep-learning empowered multi-scale modelling framework and providing a revolutionizing understanding at microscopic level of the functioning and degradation of electrified metal oxide nanostructures are the aims of this proposal. The outcome of this project will not only lead to the knowledge discovery about the impact of protonic electric double layer on porous metal oxide-based supercapacitors and on the degradation of metal oxide nanoparticles, but it will also propose useful design principles for synthesis and fabrication.
Summary
One promising solution toward a sustainable society and a green economy is to use metal oxide-based materials. Metal oxides are a class of inorganic materials that have various energy and environmental applications such as heterogeneous catalyst, fuel cell, lithium-ion battery, supercapacitor, water treatment and antimicrobial application. Most metal oxides are synthesized as nanostructures which leads to unique properties and reduced economical costs. The very properties that make the metal oxide nanostructures attractive and indispensable in modern science and technology also cause an issue for the environment and human safety. In both the functioning and the degradation of metal oxide nanostructures, aqueous interface plays a vital role. The metal oxide-aqueous solution aqueous interface is electrified in working conditions due to acid-base chemistry and composed of protonic electric double layer. Given the importance of metal oxide surfaces in practical applications, surprisingly little is known about the relation between atomic structure of protonic double layer and the interfacial reactivity. This is largely due to the fact that our knowledge is mostly based on macroscopic observations such as current and concentration in electrochemistry and microscopic information of protonic double layer is difficult to be obtained in experiments. Therefore, developing a novel deep-learning empowered multi-scale modelling framework and providing a revolutionizing understanding at microscopic level of the functioning and degradation of electrified metal oxide nanostructures are the aims of this proposal. The outcome of this project will not only lead to the knowledge discovery about the impact of protonic electric double layer on porous metal oxide-based supercapacitors and on the degradation of metal oxide nanoparticles, but it will also propose useful design principles for synthesis and fabrication.
Max ERC Funding
1 400 600 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym DETECT
Project Discovering New Dual and Triple Atom Catalysts
Researcher (PI) Jakob KIBSGAARD
Host Institution (HI) DANMARKS TEKNISKE UNIVERSITET
Country Denmark
Call Details Consolidator Grant (CoG), PE4, ERC-2020-COG
Summary The central goal of DETECT is to discover and establish new fundamental insight into the catalytic activity of duel and triple atom catalysts.
Many chemical reactions – several related to securing a wider penetration of renewable energy – are not viable today due to a number of limitations of traditional catalysts including unfavorable scaling relations of intermediates, lack of selectivity, and cost and availability of precious metals. Employing catalysts consisting of only two or three atoms offers a possible route to circumvent these limitations, while also lifting the constraints of current single atom catalysts and opening the possibility of catalyzing more complex reactions that may require more than a single atom to proceed. This proposal will target electrochemical hydrogen peroxide formation, CO2 reduction to fuels and chemicals, and ammonia synthesis. The dual and triple atom catalysts will be developed in an interactive feedback loop involving computation, synthesis, characterization and activity testing. The dual and triple atom catalysts will be created using a cluster source with time-of-flight based mass filter, which enables even synthesis of dual and triple atoms catalyst with more than one elements - something that are virtually unexplored for catalysis. Further, this synthesis method will produce well-defined catalysts that can be characterized on the atomic level, which is essential for comparing with computational predictions in the feedback loop.
DETECT could open for more energy efficient and more selective catalysts that could facilitate a number of ‘Dream Reactions’ for which we have no viable catalyst today. Not least in the field of transforming our current fossil fuel based society, new catalysts are desperately needed to ensure a greater penetration of sustainable energy.
Summary
The central goal of DETECT is to discover and establish new fundamental insight into the catalytic activity of duel and triple atom catalysts.
Many chemical reactions – several related to securing a wider penetration of renewable energy – are not viable today due to a number of limitations of traditional catalysts including unfavorable scaling relations of intermediates, lack of selectivity, and cost and availability of precious metals. Employing catalysts consisting of only two or three atoms offers a possible route to circumvent these limitations, while also lifting the constraints of current single atom catalysts and opening the possibility of catalyzing more complex reactions that may require more than a single atom to proceed. This proposal will target electrochemical hydrogen peroxide formation, CO2 reduction to fuels and chemicals, and ammonia synthesis. The dual and triple atom catalysts will be developed in an interactive feedback loop involving computation, synthesis, characterization and activity testing. The dual and triple atom catalysts will be created using a cluster source with time-of-flight based mass filter, which enables even synthesis of dual and triple atoms catalyst with more than one elements - something that are virtually unexplored for catalysis. Further, this synthesis method will produce well-defined catalysts that can be characterized on the atomic level, which is essential for comparing with computational predictions in the feedback loop.
DETECT could open for more energy efficient and more selective catalysts that could facilitate a number of ‘Dream Reactions’ for which we have no viable catalyst today. Not least in the field of transforming our current fossil fuel based society, new catalysts are desperately needed to ensure a greater penetration of sustainable energy.
Max ERC Funding
2 000 000 €
Duration
Start date: 2021-09-01, End date: 2026-08-31
Project acronym ELECTROLYTE
Project The Electrolytic Revolution: Harnessing Coulomb Physics and Soft Matter Chemistry to Design Electrolyte Materials
Researcher (PI) Susan PERKIN
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Consolidator Grant (CoG), PE4, ERC-2020-COG
Summary Electrolytes fill our natural environment and are crucial to many areas of modern technology. Animals and plants are made up of electrolyte and our oceans are enormous reservoirs of electrolyte covering 2/3 of the surface of the Earth. Energy storage and conversion technologies, such as batteries and fuel cells, incorporate electrolyte as a central and essential ingredient. Despite this enormous ubiquity and importance of electrolytes, these fluids are often relegated to the ‘background’, considered simply as a solvating environment or conduits for charge transfer, sufficiently well defined by a few general parameters. Recently, a new perspective has begun to emerge: of electrolytes as a complex, central player: A vast library of different chemistries are being discovered including molecular ions, eutectic mixtures, self-assembling liquid salts, and oligomeric solvents. This diversity brings an unexplored jungle of nano-architectures and dynamic heterogeneities, relevant across many orders of magnitude in time and space.
The overall vision of the ELECTROLYTE project is to explore and understand molecular interactions in complex and high-concentration electrolytes. The philosophy and methodology of the project involves drawing on theories and predictions from far-separated disciplines of Coulomb physics, ionic liquid chemistry, soft matter, and the biology of halophiles and electric fish. From these foundations, a series of hypotheses will be tested through experimental investigations of the structure, dynamics, electrochemical, mechanical and confinement properties of a wide range of electrolytic materials. This will lead to deep insight into the properties of concentrated electrolytes, and demonstrations of radically new electrolytic materials with properties outside of what is currently possible. Ultimately, the project will bring new mindsets for understanding and innovating electrolyte materials for future technologies.
Summary
Electrolytes fill our natural environment and are crucial to many areas of modern technology. Animals and plants are made up of electrolyte and our oceans are enormous reservoirs of electrolyte covering 2/3 of the surface of the Earth. Energy storage and conversion technologies, such as batteries and fuel cells, incorporate electrolyte as a central and essential ingredient. Despite this enormous ubiquity and importance of electrolytes, these fluids are often relegated to the ‘background’, considered simply as a solvating environment or conduits for charge transfer, sufficiently well defined by a few general parameters. Recently, a new perspective has begun to emerge: of electrolytes as a complex, central player: A vast library of different chemistries are being discovered including molecular ions, eutectic mixtures, self-assembling liquid salts, and oligomeric solvents. This diversity brings an unexplored jungle of nano-architectures and dynamic heterogeneities, relevant across many orders of magnitude in time and space.
The overall vision of the ELECTROLYTE project is to explore and understand molecular interactions in complex and high-concentration electrolytes. The philosophy and methodology of the project involves drawing on theories and predictions from far-separated disciplines of Coulomb physics, ionic liquid chemistry, soft matter, and the biology of halophiles and electric fish. From these foundations, a series of hypotheses will be tested through experimental investigations of the structure, dynamics, electrochemical, mechanical and confinement properties of a wide range of electrolytic materials. This will lead to deep insight into the properties of concentrated electrolytes, and demonstrations of radically new electrolytic materials with properties outside of what is currently possible. Ultimately, the project will bring new mindsets for understanding and innovating electrolyte materials for future technologies.
Max ERC Funding
1 996 357 €
Duration
Start date: 2021-10-01, End date: 2026-09-30
Project acronym EXISTAR
Project EXtending Interface Science To Atmospheric-pressure Reactions
Researcher (PI) Robert WEATHERUP
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary This project aims to deliver a step change in our understanding of electrode and catalyst interfaces, by pioneering operando measurement capabilities that can reveal the chemistry and structure of functional interfaces under working conditions in liquid and gas environments at atmospheric-pressures and above. We will exploit enclosed reaction cells sealed with X-ray, electron and neutron transparent windows, extending their operation to conditions of temperature and pressure where industrial catalytic reactions occur, as well as the liquid environments of electrochemical energy storage. These cells will be portable across complementary characterisation tools to reveal the chemical and structural evolution of material interfaces during operation. Solid-liquid studies will focus on electrode materials for Li-ion batteries, that are critical to energy storage for a low carbon economy. This will reveal the degradation mechanisms that lead to capacity fade across varying conditions of stress (T, voltage, rate) during electrochemical cycling. Solid-gas studies will focus on heterogeneous catalysts for sustainable production of useful chemical feedstocks from environmentally harmful waste streams. We aim to reveal the nature of the active sites in catalysts used for chemical synthesis from carbon dioxide, and understand how combining these catalysts with oxide supports influences their activity and selectivity. Relationships will be established between the interfacial structure and function of these materials in terms of their electrochemical cycling performance and catalytic activity/selectivity. This will ultimately inform the design of new functional materials for use in technologies that are critical to a sustainable economy. The scope for research problems that can benefit from this atmospheric pressure operando approach is vast, providing many future research opportunities.
Summary
This project aims to deliver a step change in our understanding of electrode and catalyst interfaces, by pioneering operando measurement capabilities that can reveal the chemistry and structure of functional interfaces under working conditions in liquid and gas environments at atmospheric-pressures and above. We will exploit enclosed reaction cells sealed with X-ray, electron and neutron transparent windows, extending their operation to conditions of temperature and pressure where industrial catalytic reactions occur, as well as the liquid environments of electrochemical energy storage. These cells will be portable across complementary characterisation tools to reveal the chemical and structural evolution of material interfaces during operation. Solid-liquid studies will focus on electrode materials for Li-ion batteries, that are critical to energy storage for a low carbon economy. This will reveal the degradation mechanisms that lead to capacity fade across varying conditions of stress (T, voltage, rate) during electrochemical cycling. Solid-gas studies will focus on heterogeneous catalysts for sustainable production of useful chemical feedstocks from environmentally harmful waste streams. We aim to reveal the nature of the active sites in catalysts used for chemical synthesis from carbon dioxide, and understand how combining these catalysts with oxide supports influences their activity and selectivity. Relationships will be established between the interfacial structure and function of these materials in terms of their electrochemical cycling performance and catalytic activity/selectivity. This will ultimately inform the design of new functional materials for use in technologies that are critical to a sustainable economy. The scope for research problems that can benefit from this atmospheric pressure operando approach is vast, providing many future research opportunities.
Max ERC Funding
1 491 265 €
Duration
Start date: 2021-07-01, End date: 2026-06-30
Project acronym FIAMMA
Project Fully Integrating Atomistic Modeling with Machine Learning
Researcher (PI) Michele CERIOTTI
Host Institution (HI) ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Country Switzerland
Call Details Consolidator Grant (CoG), PE4, ERC-2020-COG
Summary Computer simulations of molecules and materials are undergoing a profound transformation. Machine learning (ML) has become essential to extend the reach and increase the predictive power of atomic-scale modeling. The potential of ML in association with quantum mechanical (QM) and statistical mechanical (SM) methods has been shown, but the link has been rather superficial, due to the complex, interdisciplinary effort needed to combine the three approaches. Without full convergence, ML-powered simulations cannot address modern modeling challenges, which involve complex materials in realistic conditions, and require increasingly predictive accuracy.
The objective of this project is to create a “plug and play” framework by which ML can be seamlessly combined with physics-based modeling, substituting individual steps of a QM calculation, or making direct predictions across complex SM workflows. Full integration of the three approaches will greatly extend the reach of atomistic simulations, and allow an insightful critical comparison of the role of inductive and deductive paradigms in theory and modeling. The development of an open-source software that unifies QM, SM and ML shall facilitate early adoption and broaden impact. We will demonstrate the benefits of our integrated framework through two challenging and compelling platform problems: (i) investigating stabilities and properties of flexible drug-like molecules and assemblies, and (ii) discovering fundamental structure-activity relationships of porous aluminosilicates for clean chemical technologies.
Critical knowledge gaps that will be filled include: (1) the description of long-range physics within the same conceptual framework that has been used for short-range interactions; (2) the symmetry-adapted representation of input and outputs of each step of a QM calculation; and (3) the rigorous characterization of SM ensembles to enable end-to-end predictions of equilibrium properties with uncertainty quantification.
Summary
Computer simulations of molecules and materials are undergoing a profound transformation. Machine learning (ML) has become essential to extend the reach and increase the predictive power of atomic-scale modeling. The potential of ML in association with quantum mechanical (QM) and statistical mechanical (SM) methods has been shown, but the link has been rather superficial, due to the complex, interdisciplinary effort needed to combine the three approaches. Without full convergence, ML-powered simulations cannot address modern modeling challenges, which involve complex materials in realistic conditions, and require increasingly predictive accuracy.
The objective of this project is to create a “plug and play” framework by which ML can be seamlessly combined with physics-based modeling, substituting individual steps of a QM calculation, or making direct predictions across complex SM workflows. Full integration of the three approaches will greatly extend the reach of atomistic simulations, and allow an insightful critical comparison of the role of inductive and deductive paradigms in theory and modeling. The development of an open-source software that unifies QM, SM and ML shall facilitate early adoption and broaden impact. We will demonstrate the benefits of our integrated framework through two challenging and compelling platform problems: (i) investigating stabilities and properties of flexible drug-like molecules and assemblies, and (ii) discovering fundamental structure-activity relationships of porous aluminosilicates for clean chemical technologies.
Critical knowledge gaps that will be filled include: (1) the description of long-range physics within the same conceptual framework that has been used for short-range interactions; (2) the symmetry-adapted representation of input and outputs of each step of a QM calculation; and (3) the rigorous characterization of SM ensembles to enable end-to-end predictions of equilibrium properties with uncertainty quantification.
Max ERC Funding
1 998 015 €
Duration
Start date: 2022-01-01, End date: 2026-12-31
Project acronym FLUCTENZ
Project The Fluctuating Enzyme: From Catalysis to Vibrational Dynamics
Researcher (PI) Shlomi REUVENI
Host Institution (HI) TEL AVIV UNIVERSITY
Country Israel
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary Enzymes spin the wheel of life by catalyzing a myriad of chemical reactions central to the growth, development, and metabolism of all living organisms. Without enzymes, essential processes would progress so slowly that life would virtually grind to a halt, and the quest to determine their inner workings thus continues to attract and fascinate scientists over a broad range of disciplines. Cutting-edge methods now allow one to observe and manipulate individual enzymes in their reaction course. These revealed that chemistry at the single-molecule level is inherently stochastic and, at times, extremely unintuitive. Thermal fluctuations push enzymes to vibrate erratically and force a probabilistic description, but classical approaches are deeply entrenched in determinism, and so is our basic expectation for how things should behave in the world around us. The groundbreaking purpose of this proposal is to bring advanced theoretical methods and mathematical tools to the analysis of stochastic fluctuations of enzymes and proteins at the single-molecule level. These will be based on state-of-the-art approaches in statistical physics and stochastic processes that I will adapt and further advance to need. Equipped with mathematical techniques that have so far been foreign to the field, I expect to rectify fundamental flaws in our understanding, predict the emergence of novel phenomena, and show how single-molecule fluctuations can be extracted from bulk, steady-state, concentrations—despite belief that this is fundamentally impossible. Novel analysis methods that I will apply to tens of thousands of protein structures would complement these efforts and aid in the discovery of unifying principles governing thermal fluctuations of enzymes and proteins. The amalgamation of all these efforts will pave the way to large-scale, multi-tier, characterization of fluctuations which is expected to transform our understanding of enzymes and enzymatic catalysis.
Summary
Enzymes spin the wheel of life by catalyzing a myriad of chemical reactions central to the growth, development, and metabolism of all living organisms. Without enzymes, essential processes would progress so slowly that life would virtually grind to a halt, and the quest to determine their inner workings thus continues to attract and fascinate scientists over a broad range of disciplines. Cutting-edge methods now allow one to observe and manipulate individual enzymes in their reaction course. These revealed that chemistry at the single-molecule level is inherently stochastic and, at times, extremely unintuitive. Thermal fluctuations push enzymes to vibrate erratically and force a probabilistic description, but classical approaches are deeply entrenched in determinism, and so is our basic expectation for how things should behave in the world around us. The groundbreaking purpose of this proposal is to bring advanced theoretical methods and mathematical tools to the analysis of stochastic fluctuations of enzymes and proteins at the single-molecule level. These will be based on state-of-the-art approaches in statistical physics and stochastic processes that I will adapt and further advance to need. Equipped with mathematical techniques that have so far been foreign to the field, I expect to rectify fundamental flaws in our understanding, predict the emergence of novel phenomena, and show how single-molecule fluctuations can be extracted from bulk, steady-state, concentrations—despite belief that this is fundamentally impossible. Novel analysis methods that I will apply to tens of thousands of protein structures would complement these efforts and aid in the discovery of unifying principles governing thermal fluctuations of enzymes and proteins. The amalgamation of all these efforts will pave the way to large-scale, multi-tier, characterization of fluctuations which is expected to transform our understanding of enzymes and enzymatic catalysis.
Max ERC Funding
1 496 875 €
Duration
Start date: 2021-10-01, End date: 2026-09-30
