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Primeur weekly 2020-05-18

Focus

SiPearl's chip will be a platform open to other start-ups to develop accelerators for - An interview with Philippe Notton from SiPearl ...

Exascale supercomputing

Atos launches first supercomputer equipped with NVIDIA A100 GPU ...

Quantum computing

Start of the Fraunhofer Competence Center "Quantum Computing Baden-Württemberg" ...

ADVA brings post-quantum security to packet networks ...

Atos delivers its Quantum Learning Machine to Japan ...

Machine learning cracks quantum chemistry conundrum ...

Making quantum 'waves' in ultrathin materials ...

Seeqc UK awarded GBP 1,8 million in grants to advance quantum computing initiatives ...

Rising investments in quantum technology to boost market growth ...

Total is exploring quantum algorithms to improve CO2 capture ...

Middleware

Mentor Graphics and the University of Basel join the OpenMP effort ...

Hardware

NVIDIA's new Ampere data centre GPU in full production ...

Karlsruhe Institute of Technology procures new supercomputer ...

DDN announces A3I suppport for NVIDIA DGX A100 ...

Yvonne Walker recognized as one of CRN's 2020 Women of the Channel ...

Supermicro expands portfolio with fully integrated NVIDIA A100 GPU-powered systems delivering 5 Petaflop/s of AI performance in a single 4U server ...

Artificial synapses on design ...

Light, fantastic: the path ahead for faster, smaller computer processors ...

OpenFabrics Alliance (OFA) and Gen-Z Consortium announce MoU agreement ...

Tachyum achieves 90 percent of silicon laid for its Prodigy Universal Processor ...

Intel Capital invests $132 million in 11 disruptive technology start-ups ...

Inspur releases five new AI servers powered by NVIDIA A100 Tensor Core GPUs ...

Research and Markets issues Global High-Performance Computing as a Service Market Analysis and Industry Forecasts to 2026 ...

Asperitas and maincubes partner to deliver immersion cooling solutions in dedicated colocation suites ...

Delivery of supercomputer Fugaku completed ...

Applications

Supercomputer simulations help advance electrochemical reaction research ...

3D-printed nuclear reactor promises faster, more economical path to nuclear energy ...

GCS centres support research to mitigate impact of COVID-19 pandemic ...

NIST scientists create new recipe for single-atom transistors ...

ACM service awards recognize leaders who strengthen the computing community ...

New research launched on airborne virus transmission in buildings ...

Seeing the universe through new lenses ...

Liqid delivers industry's fastest single-socket server ...

National Science Foundation grant backs funcX: A smart, automated delegator for computational research ...

Educating the next generation of supercomputer users with Blue Waters ...

The Cloud

Innovium introduces TERALYNX 8, world's highest performance programmable switch for data centre networks with 25,6 Tbps throughput and support for 112 Gbps SerDes I/O ...

Wiwynn unveils standalone rack-level liquid cooling solution for OCP ORV3 at 2020 OCP Virtual Summit ...

Crowd Machine Platform is powered by Oracle Cloud and now available in the Oracle Cloud Marketplace ...

Machine learning cracks quantum chemistry conundrum

12 May 2020 New York - A new machine learning tool can calculate the energy required to make - or break - simple molecules with higher accuracy than conventional methods. Extensions to more complicated molecules may help reveal the inner workings of the chemical reactions that nourish the global ecosystem. While the tool can currently only handle simple molecules, it paves the way for future insights in quantum chemistry.

"Using machine learning to solve the fundamental equations governing quantum chemistry has been an open problem for several years, and there's a lot of excitement around it right now", stated co-creator Giuseppe Carleo, a research scientist at the Flatiron Institute's Center for Computational Quantum Physics in New York City. A better understanding of the formation and destruction of molecules, he says, could reveal the inner workings of the chemical reactions vital to life.

Giuseppe Carleo and collaborators Kenny Choo of the University of Zurich and Antonio Mezzacapo of the IBM Thomas J. Watson Research Center in Yorktown Heights, New York, presented their work May 12 in Nature Communications .

The team's tool estimates the amount of energy needed to assemble or pull apart a molecule, such as water or ammonia. That calculation requires determining the molecule's electronic structure, which consists of the collective behavior of the electrons that bind the molecule together.

A molecule's electronic structure is a tricky thing to calculate, requiring the determination of all the potential states the molecule's electrons could be in, plus each state's probability.

Since electrons interact and become quantum mechanically entangled with one another, scientists can't treat them individually. With more electrons, more entanglements crop up, and the problem gets exponentially harder. Exact solutions don't exist for molecules more complex than the two electrons found in a pair of hydrogen atoms. Even approximations struggle with accuracy when they involve more than a few electrons.

One of the challenges is that a molecule's electronic structure includes states for an infinite number of orbitals going farther and farther from the atoms. Additionally, one electron is indistinguishable from another, and two electrons can't occupy the same state. The latter rule is a consequence of exchange symmetry, which governs what happens when identical particles switch states.

Antonio Mezzacapo and colleagues at IBM Quantum developed a method for constraining the number of orbitals considered and imposing exchange symmetry. This approach, based on methods developed for quantum computing applications, makes the problem more akin to scenarios where electrons are confined to preset locations, such as in a rigid lattice.

The similarity to rigid lattices was the key to making the problem more manageable. Giuseppe Carleo previously trained neural networks to reconstruct the behaviour of electrons confined to the sites of a lattice. By extending those methods, the researchers could estimate solutions to Antonio Mezzacapo's compacted problems. The team's neural network calculates the probability of each state. Using this probability, the researchers can estimate the energy of a given state. The lowest energy level, dubbed the equilibrium energy, is where the molecule is the most stable.

The team's innovations made calculating a basic molecule's electronic structure simpler and faster. The researchers demonstrated the accuracy of their methods by estimating how much energy it would take to pull a real-world molecule apart, breaking its bonds. They ran calculations for dihydrogen (H2), lithium hydride (LiH), ammonia (NH3), water (H2O), diatomic carbon (C2) and dinitrogen (N2). For all the molecules, the team's estimates proved highly accurate even in ranges where existing methods struggle.

In the future, the researchers aim to tackle larger and more complex molecules by using more sophisticated neural networks. One goal is to handle chemicals like those found in the nitrogen cycle, in which biological processes build and break nitrogen-based molecules to make them usable for life. "We want this to be a tool that could be used by chemists to process these problems", Giuseppe Carleo stated.

Giuseppe Carleo, Kenny Choo and Antonio Mezzacapo aren't alone in tapping machine learning to tackle problems in quantum chemistry. The researchers first presented their work on arXiv.org in September 2019. In that same month, a group in Germany and another at Google's DeepMind in London each released research using machine learning to reconstruct the electronic structure of molecules.

The other two groups use a similar approach to one another that doesn't limit the number of orbitals considered. This inclusiveness, however, is more computationally taxing, a drawback that will only worsen with more complex molecules. With the same computational resources, the approach by Giuseppe Carleo, Kenny Choo and Antonio Mezzacapo yields higher accuracy, but the simplifications made to obtain this accuracy could introduce biases.

"Overall, it's a trade-off between bias and accuracy, and it's unclear which of the two approaches has more potential for the future", Giuseppe Carleo stated. "Only time will tell us which of these approaches can be scaled up to the challenging open problems in chemistry."

Source: Simons Foundation

Back to Table of contents

Primeur weekly 2020-05-18

Focus

SiPearl's chip will be a platform open to other start-ups to develop accelerators for - An interview with Philippe Notton from SiPearl ...

Exascale supercomputing

Atos launches first supercomputer equipped with NVIDIA A100 GPU ...

Quantum computing

Start of the Fraunhofer Competence Center "Quantum Computing Baden-Württemberg" ...

ADVA brings post-quantum security to packet networks ...

Atos delivers its Quantum Learning Machine to Japan ...

Machine learning cracks quantum chemistry conundrum ...

Making quantum 'waves' in ultrathin materials ...

Seeqc UK awarded GBP 1,8 million in grants to advance quantum computing initiatives ...

Rising investments in quantum technology to boost market growth ...

Total is exploring quantum algorithms to improve CO2 capture ...

Middleware

Mentor Graphics and the University of Basel join the OpenMP effort ...

Hardware

NVIDIA's new Ampere data centre GPU in full production ...

Karlsruhe Institute of Technology procures new supercomputer ...

DDN announces A3I suppport for NVIDIA DGX A100 ...

Yvonne Walker recognized as one of CRN's 2020 Women of the Channel ...

Supermicro expands portfolio with fully integrated NVIDIA A100 GPU-powered systems delivering 5 Petaflop/s of AI performance in a single 4U server ...

Artificial synapses on design ...

Light, fantastic: the path ahead for faster, smaller computer processors ...

OpenFabrics Alliance (OFA) and Gen-Z Consortium announce MoU agreement ...

Tachyum achieves 90 percent of silicon laid for its Prodigy Universal Processor ...

Intel Capital invests $132 million in 11 disruptive technology start-ups ...

Inspur releases five new AI servers powered by NVIDIA A100 Tensor Core GPUs ...

Research and Markets issues Global High-Performance Computing as a Service Market Analysis and Industry Forecasts to 2026 ...

Asperitas and maincubes partner to deliver immersion cooling solutions in dedicated colocation suites ...

Delivery of supercomputer Fugaku completed ...

Applications

Supercomputer simulations help advance electrochemical reaction research ...

3D-printed nuclear reactor promises faster, more economical path to nuclear energy ...

GCS centres support research to mitigate impact of COVID-19 pandemic ...

NIST scientists create new recipe for single-atom transistors ...

ACM service awards recognize leaders who strengthen the computing community ...

New research launched on airborne virus transmission in buildings ...

Seeing the universe through new lenses ...

Liqid delivers industry's fastest single-socket server ...

National Science Foundation grant backs funcX: A smart, automated delegator for computational research ...

Educating the next generation of supercomputer users with Blue Waters ...

The Cloud

Innovium introduces TERALYNX 8, world's highest performance programmable switch for data centre networks with 25,6 Tbps throughput and support for 112 Gbps SerDes I/O ...

Wiwynn unveils standalone rack-level liquid cooling solution for OCP ORV3 at 2020 OCP Virtual Summit ...

Crowd Machine Platform is powered by Oracle Cloud and now available in the Oracle Cloud Marketplace ...