Monthly Archives: October 2021

A library of properties for nanomaterials

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Researchers at the University of Birmingham (UK) announced the development of a library of nanomaterial properties according to a June 8, 2021 news item on Nanowerk (Note: Links have been removed),

Researchers have developed a ‘library of properties’ to help identify the environmental impact of nanomaterials faster and more cost effectively.

Whilst nanomaterials have benefited a wide range of industries and revolutionized everyday life, there are concerns over potential adverse effects—including toxic effects following accumulation in different organs and indirect effects from transport of co-pollutants.

The European Union H2020-funded NanoSolveIT project is developing a ground-breaking computer-based Integrated Approach to Testing and Assessment (IATA) for the environmental health and safety of nanomaterials.

A June 8, 2021 University of Birmingham press release (also on EurekAlert) spells out the details,

Over the last two years, researchers from the University of Birmingham have worked with experts at NovaMechanics, in Nicosia, Cyprus to develop a decision support system in the form of both stand-alone open software and a Cloud platform.

The team has developed a freely available cloud library containing full physicochemical characterisation of 69 nanomaterials, plus calculated molecular descriptors to increase the value of the available information, details of which are published in NanoImpact. [link and citation follow]

Professor Iseult Lynch, from the University of Birmingham commented: “One of the limitations to widespread application of computer-based approaches is the lack of large well-organised high-quality datasets, or of data with adequate metadata that will allow dataset interoperability and their combination to create larger datasets.”

“Making the library of calculated and experimental descriptors available to the community, along with the detailed description of how they were calculated is a key first step towards filling this datagap.”

Development of the cloud-based nanomaterials library is the fifth freely available web-based application that the project has delivered.

Antreas Afantitis, from NovaMechanics, commented: “Over the last two years, this project has already presented some very impressive results with more than 30 publications, making NanoSolveIT one of the most active projects in the nanomaterials safety and informatics space.”

Concerns about nanomaterials are also arising as risk assessment is lagging behind product development, mainly because current approaches to assessing exposure, hazard and risk are expensive and time-consuming, and frequently involve testing in animal models. The NanoSolveIT project aspires to address these challenges.

The latest development aims to enrich our knowledge of nanomaterials properties and the link from property to (cytotoxic) effect. The enriched dataset contains over 70 descriptors per nanomaterial.

The dataset was used to develop a computer-based workflow to predict nanomaterials’ effective surface charge (zeta-potential) based on a set of descriptors that can be used to help design and produce safer and more functional nanomaterials.

The resulting predictive read-across model has been made publicly and freely available as a web service through the Horizon 2020 (H2020) NanoCommons project (http://enaloscloud.novamechanics.com/nanocommons/mszeta/ ) and via the H2020 NanoSolveIT Cloud Platform (https://mszeta.cloud.nanosolveit.eu/ ) to ensure accessibility to the community and interested stakeholders.

In addition, the full data set, ready for further computational modeling, is available through the NanoPharos database, as the project consortium supports the FAIR data principles – committing to making its data Findable, Accessible, Interoperable and Re-usable.

I quite like this image of how the scales are illustrated (BTW, you can find NanoSolveIT here the NanoCommons project [closing date May 15, 2021] here, and NovaMechanics here)

Scales of descriptors – from whole nanoparticle to unit cell to individual atoms Courtesy University of Birmingham and NanoSolveIT

Here’s a link to and a citation for the paper,

Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform by Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki. NanoImpact Volume 22, April 2021, 100308 DOI: https://doi.org/10.1016/j.impact.2021.100308 Available online 18 March 2021

This paper is open access.

Thousands of terragrams of mineral nanoparticles

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A June 8, 2021 news item on phys.org announces some intriguing research (Note: Links have been removed),

Globally, the Earth system has thousands of terragrams (Tg) (1 Tg = 1012g) of mineral nanoparticles moving around the planet each year. These mineral nanoparticles are ubiquitously distributed throughout the atmosphere, oceans, waters, soils, in and/or on most living organisms, and even within proteins such as ferritin. In natural environments, mineral nanozymes can be produced by two pathways: ‘top down’ and ‘bottom up’ processes. Specifically, the weathering or human-promoted breakdown of bulk materials can result in nanomaterials directly (a top-down process), or nanomaterials can grow from precursors through crystallization, reaction, or biological roles (a bottom-up process).

These mineral nanoparticles can possess multiple enzyme-like properties, e.g., oxidase, peroxidase, catalase, and superoxide dismutase, depending on the local environment. Iron-containing minerals, e.g., ferrihydrite, hematite, and magnetite, are ubiquitous in Earth systems and possess peroxidase-like activity. Among these iron (oxyhydr)oxides, ferrihydrite exhibited the highest peroxidase-like activity, owing to its smallest particle size and largest specific surface area. Because of the presence of ferrous iron, magnetite has considerably high peroxidase-like activity.

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A June 8, 2021 Science China Press press release on EurekAlert, which originated the news item, delves further into the research,

Compared with natural enzymes, mineral nanozymes show several advantages, such as low cost, increased stability, sustainable catalytic activity, and robustness to harsh environments. Because of their larger specific surface area, high ratios of surface atoms, wide band gap, and strong catalytic activities, mineral nanozymes play essential roles in biogeochemical cycles of elements in ecosystems.

Fungi and bacteria contribute approximately 70 Gt carbon (C) (1 Gt = 10 9 t) and 120 Gt C to global biomass, respectively. Given that fungal hyphae can cumulatively extend hundreds of kilometers in soils kg-1 in environments such as the rhizosphere (i.e., 200-800 km kg-1) and that more than 94% of land plants and fungi form a symbiotic relationship, mineral nanozymes may have important implications in microbial-mineral coevolution, nutrient cycling in the surface Earth system, mineral carbon sequestration, and alleviation of global climate changes.

In Earth systems, taxonomically and functionally diverse microorganisms are a vast source of superoxide (O2* -) or hydrogen peroxides (H2O2). These mineral nanozymes can regulate the levels of reactive oxygen species (ROS), including H2O2, O2* – and hydroxyl radicals (HO* ). By producing a strong oxidative HO* , the interaction between mineral nanozymes and microorganisms may play an important role in driving the biogeochemical cycle of elements (Figure 2).

“All of the investigations on mineral nanozymes are still in the laboratory stage and are not field studies,” said Guang-Hui Yu, a scientist at the School of Earth System Science, Tianjin University, in the Chinese city of Tianjin.

“The catalytic activity of mineral nanozymes is mainly determined by the oxygen vacancies (OVs) on the mineral surface”, the researchers wrote in an article titled “Fungal Nanophase Particles Catalyze Iron Transformation for Oxidative Stress Removal and Iron Acquisition.”

“These oxygen vacancies are often occupied by hydroxyl groups on the mineral surface,” they explained.

Since mineral nanozymes can catalyze H2O2 to produce highly oxidizing HO* , they have been extensively used in the field of environmental remediation. Compared with natural enzymes, mineral nanozymes can degrade organic pollutants in a wider pH range. For example, by degrading H2O2, Fe3O4 nanoparticles could effectively remove rhodamine B (RhB) in the pH range from 3.0 to 9.0.

“The effects of mineral nanozymes on microbial communities in the environment remain unclear,” wrote the two researchers, “the findings of mineral nanozymes may have revealed a previously unknown feedback route of microbe-mineral coevolution that could shed light on a number of long-standing questions, such as the origin and evolution of life by modulating ROS levels.”

These two scholars likewise revealed in the study, which was published in the Science China Earth Sciences, that the discovery of nanomaterials as new enzyme mimetics has changed the traditional idea that nanomaterials are chemically inert in Earth systems. Given the terragram (Tg)-level abundance of mineral nanoparticles in Earth systems, it is statistically highly probable for some of them, particularly those of biotic origin, to behave as mineral nanozymes to catalyze superoxide and H2O2 and promote the biogeochemical cycles of oxygen and other elements.

Here’s a link to and a citation for the paper,

Nanozyme-mediated elemental biogeochemical cycling and environment by Zhi-Lai Chi & Guang-Hui Yu. Science China Earth Sciences (2021) DOI: https://doi.org/10.1007/s11430-020-9756-5 Published: 03 June 2021

This paper is behind a paywall.

Here’s a link to and a citation for the earlier paper mentioned in the press release,

Fungal Nanophase Particles Catalyze Iron Transformation for Oxidative Stress Removal and Iron Acquisition by Guang-Hui Yu, Zhi-La iChi, Andreas Kapp, Fu-Sheng Sun, Cong-Qiang Liu, Hui Henry Teng, Geoffrey Michael Gadd. Current Biology Volume 30, Issue 15, 3 August 2020, Pages 2943-2950.e4 DOI: https://doi.org/10.1016/j.cub.2020.05.058 Available online 11 June 2020

This paper is behind a paywall.

The coolest paint

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It’s the ‘est’ of it all. The coolest, the whitest, the blackest … Scientists and artists are both pursuing the ‘est’. (More about the pursuit later in this posting.)

In this case, scientists have developed the coolest, whitest paint yet. From an April 16, 2021 news item on Nanowerk,

In an effort to curb global warming, Purdue University engineers have created the whitest paint yet. Coating buildings with this paint may one day cool them off enough to reduce the need for air conditioning, the researchers say.

In October [2020], the team created an ultra-white paint that pushed limits on how white paint can be. Now they’ve outdone that. The newer paint not only is whiter but also can keep surfaces cooler than the formulation that the researchers had previously demonstrated.

“If you were to use this paint to cover a roof area of about 1,000 square feet, we estimate that you could get a cooling power of 10 kilowatts. That’s more powerful than the central air conditioners used by most houses,” said Xiulin Ruan, a Purdue professor of mechanical engineering.

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Caption: Xiulin Ruan, a Purdue University professor of mechanical engineering, holds up his lab’s sample of the whitest paint on record. Credit: Purdue University/Jared Pike

This is nicely done. Researcher Xiulin Ruan is standing close to a structure that could be said to resemble the sun while in shirtsleeves and sunglasses and holding up a sample of his whitest paint in April (not usually a warm month in Indiana).

An April 15, 2021 Purdue University news release (also on EurkeAlert), which originated the news item, provides more detail about the work and hints about its commercial applications both civilian and military,

The researchers believe that this white may be the closest equivalent of the blackest black, “Vantablack,” [emphasis mine; see comments later in this post] which absorbs up to 99.9% of visible light. The new whitest paint formulation reflects up to 98.1% of sunlight – compared with the 95.5% of sunlight reflected by the researchers’ previous ultra-white paint – and sends infrared heat away from a surface at the same time.

Typical commercial white paint gets warmer rather than cooler. Paints on the market that are designed to reject heat reflect only 80%-90% of sunlight and can’t make surfaces cooler than their surroundings.

The team’s research paper showing how the paint works publishes Thursday (April 15 [2021]) as the cover of the journal ACS Applied Materials & Interfaces.

What makes the whitest paint so white

Two features give the paint its extreme whiteness. One is the paint’s very high concentration of a chemical compound called barium sulfate [emphasis mine] which is also used to make photo paper and cosmetics white.

“We looked at various commercial products, basically anything that’s white,” said Xiangyu Li, a postdoctoral researcher at the Massachusetts Institute of Technology who worked on this project as a Purdue Ph.D. student in Ruan’s lab. “We found that using barium sulfate, you can theoretically make things really, really reflective, which means that they’re really, really white.”

The second feature is that the barium sulfate particles are all different sizes in the paint. How much each particle scatters light depends on its size, so a wider range of particle sizes allows the paint to scatter more of the light spectrum from the sun.

“A high concentration of particles that are also different sizes gives the paint the broadest spectral scattering, which contributes to the highest reflectance,” said Joseph Peoples, a Purdue Ph.D. student in mechanical engineering.

There is a little bit of room to make the paint whiter, but not much without compromising the paint.”Although a higher particle concentration is better for making something white, you can’t increase the concentration too much. The higher the concentration, the easier it is for the paint to break or peel off,” Li said.

How the whitest paint is also the coolest

The paint’s whiteness also means that the paint is the coolest on record. Using high-accuracy temperature reading equipment called thermocouples, the researchers demonstrated outdoors that the paint can keep surfaces 19 degrees Fahrenheit cooler than their ambient surroundings at night. It can also cool surfaces 8 degrees Fahrenheit below their surroundings under strong sunlight during noon hours.

The paint’s solar reflectance is so effective, it even worked in the middle of winter. During an outdoor test with an ambient temperature of 43 degrees Fahrenheit, the paint still managed to lower the sample temperature by 18 degrees Fahrenheit.

This white paint is the result of six years of research building on attempts going back to the 1970s to develop radiative cooling paint as a feasible alternative to traditional air conditioners.

Ruan’s lab had considered over 100 different materials, narrowed them down to 10 and tested about 50 different formulations for each material. Their previous whitest paint was a formulation made of calcium carbonate, an earth-abundant compound commonly found in rocks and seashells.

The researchers showed in their study that like commercial paint, their barium sulfate-based paint can potentially handle outdoor conditions. The technique that the researchers used to create the paint also is compatible with the commercial paint fabrication process.

Patent applications for this paint formulation have been filed through the Purdue Research Foundation Office of Technology Commercialization. This research was supported by the Cooling Technologies Research Center at Purdue University and the Air Force Office of Scientific Research [emphasis mine] through the Defense University Research Instrumentation Program (Grant No.427 FA9550-17-1-0368). The research was performed at Purdue’s FLEX Lab and Ray W. Herrick Laboratories and the Birck Nanotechnology Center of Purdue’s Discovery Park.

Here’s a link to and a citation for the paper,

Ultrawhite BaSO4 Paints and Films for Remarkable Daytime Subambient Radiative Cooling by Xiangyu Li, Joseph Peoples, Peiyan Yao, and Xiulin Ruan. ACS Appl. Mater. Interfaces 2021, XXXX, XXX, XXX-XXX DOI: https://doi.org/10.1021/acsami.1c02368 Publication Date:April 15, 2021 © 2021 American Chemical Society

This paper is behind a paywall.

Vantablack and the ongoing ‘est’ of blackest

Vantablack’s 99.9% light absorption no longer qualifies it for the ‘blackest black’. A newer standard for the ‘blackest black’ was set by the US National Institute of Standards and Technology at 99.99% light absorption with its N.I.S.T. ultra-black in 2019, although that too seems to have been bested.

I have three postings covering the Vantablack and blackest black story,

The third posting (December 2019) provides a brief summary of the story along with what was the latest from the US National Institute of Standards and Technology. There’s also a little bit about the ‘The Redemption of Vanity’ an art piece demonstrating the blackest black material from the Massachusetts Institute of Technology, which they state has 99.995% (at least) absorption of light.

From a science perspective, the blackest black would be useful for space exploration.

I am surprised there doesn’t seem to have been an artistic rush to work with the whitest white. That impression may be due to the fact that the feuds get more attention than quiet work.

Dark side to the whitest white?

Andrew Parnell, research fellow in physics and astronomy at the University of Sheffield (UK), mentions a downside to obtaining the material needed to produce this cooling white paint in a June 10, 2021 essay on The Conversation (h/t Fast Company), Note: Links have been removed,

… this whiter-than-white paint has a darker side. The energy required to dig up raw barite ore to produce and process the barium sulphite that makes up nearly 60% of the paint means it has a huge carbon footprint. And using the paint widely would mean a dramatic increase in the mining of barium.

Parnell ends his essay with this (Note: Links have been removed),

Barium sulphite-based paint is just one way to improve the reflectivity of buildings. I’ve spent the last few years researching the colour white in the natural world, from white surfaces to white animals. Animal hairs, feathers and butterfly wings provide different examples of how nature regulates temperature within a structure. Mimicking these natural techniques could help to keep our cities cooler with less cost to the environment.

The wings of one intensely white beetle species called Lepidiota stigma appear a strikingly bright white thanks to nanostructures in their scales, which are very good at scattering incoming light. This natural light-scattering property can be used to design even better paints: for example, by using recycled plastic to create white paint containing similar nanostructures with a far lower carbon footprint. When it comes to taking inspiration from nature, the sky’s the limit.

An algorithm for modern quilting

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Caption: Each of the blocks in this quilt were designed using an algorithm-based tool developed by Stanford researchers. Credit: Mackenzie Leake

I love the colours. This research into quilting and artificial intelligence (AI) was presented at SIGGRAPH 2021 in August. (SIGGRAPH is, also known as, ACM SIGGRAPH or ‘Association for Computing Machinery’s Special Interest Group on Computer Graphics and Interactive Techniques’.)

A June 3, 2021 news item on ScienceDaily announced the presentation,

Stanford University computer science graduate student Mackenzie Leake has been quilting since age 10, but she never imagined the craft would be the focus of her doctoral dissertation. Included in that work is new prototype software that can facilitate pattern-making for a form of quilting called foundation paper piecing, which involves using a backing made of foundation paper to lay out and sew a quilted design.

Developing a foundation paper piece quilt pattern — which looks similar to a paint-by-numbers outline — is often non-intuitive. There are few formal guidelines for patterning and those that do exist are insufficient to assure a successful result.

“Quilting has this rich tradition and people make these very personal, cherished heirlooms but paper piece quilting often requires that people work from patterns that other people designed,” said Leake, who is a member of the lab of Maneesh Agrawala, the Forest Baskett Professor of Computer Science and director of the Brown Institute for Media Innovation at Stanford. “So, we wanted to produce a digital tool that lets people design the patterns that they want to design without having to think through all of the geometry, ordering and constraints.”

A paper describing this work is published and will be presented at the computer graphics conference SIGGRAPH 2021 in August.

A June 2, 2021 Stanford University news release (also on EurekAlert), which originated the news item, provides more detail,

Respecting the craft

In describing the allure of paper piece quilts, Leake cites the modern aesthetic and high level of control and precision. The seams of the quilt are sewn through the paper pattern and, as the seaming process proceeds, the individual pieces of fabric are flipped over to form the final design. All of this “sew and flip” action means the pattern must be produced in a careful order.

Poorly executed patterns can lead to loose pieces, holes, misplaced seams and designs that are simply impossible to complete. When quilters create their own paper piecing designs, figuring out the order of the seams can take considerable time – and still lead to unsatisfactory results.

“The biggest challenge that we’re tackling is letting people focus on the creative part and offload the mental energy of figuring out whether they can use this technique or not,” said Leake, who is lead author of the SIGGRAPH paper. “It’s important to me that we’re really aware and respectful of the way that people like to create and that we aren’t over-automating that process.”

This isn’t Leake’s first foray into computer-aided quilting. She previously designed a tool for improvisational quilting, which she presented [PatchProv: Supporting Improvistiional Design Practices for Modern Quilting by Mackenzie Leake, Frances Lai, Tovi Grossman, Daniel Wigdor, and Ben Lafreniere] at the human-computer interaction conference CHI in May [2021]. [Note: Links to the May 2021 conference and paper added by me.]

Quilting theory

Developing the algorithm at the heart of this latest quilting software required a substantial theoretical foundation. With few existing guidelines to go on, the researchers had to first gain a more formal understanding of what makes a quilt paper piece-able, and then represent that mathematically.

They eventually found what they needed in a particular graph structure, called a hypergraph. While so-called “simple” graphs can only connect data points by lines, a hypergraph can accommodate overlapping relationships between many data points. (A Venn diagram is a type of hypergraph.) The researchers found that a pattern will be paper piece-able if it can be depicted by a hypergraph whose edges can be removed one at a time in a specific order – which would correspond to how the seams are sewn in the pattern.

The prototype software allows users to sketch out a design and the underlying hypergraph-based algorithm determines what paper foundation patterns could make it possible – if any. Many designs result in multiple pattern options and users can adjust their sketch until they get a pattern they like. The researchers hope to make a version of their software publicly available this summer.

“I didn’t expect to be writing my computer science dissertation on quilting when I started,” said Leake. “But I found this really rich space of problems involving design and computation and traditional crafts, so there have been lots of different pieces we’ve been able to pull off and examine in that space.”

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Researchers from University of California, Berkeley and Cornell University are co-authors of this paper. Agrawala is also an affiliate of the Institute for Human-Centered Artificial Intelligence (HAI).

An abstract for the paper “A Mathematical Foundation for Foundation Paper Pieceable Quilts” by Mackenzie Leake, Gilbert Bernstein, Abe Davis and Maneesh Agrawala can be found here along with links to a PDF of the full paper and video on YouTube.

Afterthought: I noticed that all of the co-authors for the May 2021 paper are from the University of Toronto and most of them including Mackenzie Leake are associated with that university’s Chatham Labs.

Finishing Beethoven’s unfinished 10th Symphony

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Throughout the project, Beethoven’s genius loomed. Circe Denyer

This is an artificial intelligence (AI) story set to music. Professor Ahmed Elgammal (Director of the Art & AI Lab at Rutgers University located in New Jersey, US) has a September 24, 2021 essay posted on The Conversation (and, then, in the Smithsonian Magazine online) describing the AI project and upcoming album release and performance (Note: A link has been removed),

When Ludwig van Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn’t able to make much headway: All he left behind were some musical sketches.

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A full recording of Beethoven’s 10th Symphony is set to be released on Oct. 9, 2021, the same day as the world premiere performance scheduled to take place in Bonn, Germany – the culmination of a two-year-plus effort.

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These excerpts from the Elgammal’s September 24, 2021 essay on the The Conversation provide a summarized view of events. By the way, this isn’t the first time an attempt has been made to finish Beethoven’s 10th Symphony (Note: Links have been removed),

Around 1817, the Royal Philharmonic Society in London commissioned Beethoven to write his Ninth and 10th symphonies. Written for an orchestra, symphonies often contain four movements: the first is performed at a fast tempo, the second at a slower one, the third at a medium or fast tempo, and the last at a fast tempo.

Beethoven completed his Ninth Symphony in 1824, which concludes with the timeless “Ode to Joy.”

But when it came to the 10th Symphony, Beethoven didn’t leave much behind, other than some musical notes and a handful of ideas he had jotted down.

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There have been some past attempts to reconstruct parts of Beethoven’s 10th Symphony. Most famously, in 1988, musicologist Barry Cooper ventured to complete the first and second movements. He wove together 250 bars of music from the sketches to create what was, in his view, a production of the first movement that was faithful to Beethoven’s vision.

Yet the sparseness of Beethoven’s sketches made it impossible for symphony experts to go beyond that first movement.

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In early 2019, Dr. Matthias Röder, the director of the Karajan Institute, an organization in Salzburg, Austria, that promotes music technology, contacted me. He explained that he was putting together a team to complete Beethoven’s 10th Symphony in celebration of the composer’s 250th birthday. Aware of my work on AI-generated art, he wanted to know if AI would be able to help fill in the blanks left by Beethoven.

Röder then compiled a team that included Austrian composer Walter Werzowa. Famous for writing Intel’s signature bong jingle, Werzowa was tasked with putting together a new kind of composition that would integrate what Beethoven left behind with what the AI would generate. Mark Gotham, a computational music expert, led the effort to transcribe Beethoven’s sketches and process his entire body of work so the AI could be properly trained.

The team also included Robert Levin, a musicologist at Harvard University who also happens to be an incredible pianist. Levin had previously finished a number of incomplete 18th-century works by Mozart and Johann Sebastian Bach.

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… We didn’t have a machine that we could feed sketches to, push a button and have it spit out a symphony. Most AI available at the time couldn’t continue an uncompleted piece of music beyond a few additional seconds.

We would need to push the boundaries of what creative AI could do by teaching the machine Beethoven’s creative process – how he would take a few bars of music and painstakingly develop them into stirring symphonies, quartets and sonatas.

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Here’s Elgammal’s description of the difficulties from an AI perspective, from the September 24, 2021 essay (Note: Links have been removed),

First, and most fundamentally, we needed to figure out how to take a short phrase, or even just a motif, and use it to develop a longer, more complicated musical structure, just as Beethoven would have done. For example, the machine had to learn how Beethoven constructed the Fifth Symphony out of a basic four-note motif.

Next, because the continuation of a phrase also needs to follow a certain musical form, whether it’s a scherzo, trio or fugue, the AI needed to learn Beethoven’s process for developing these forms.

The to-do list grew: We had to teach the AI how to take a melodic line and harmonize it. The AI needed to learn how to bridge two sections of music together. And we realized the AI had to be able to compose a coda, which is a segment that brings a section of a piece of music to its conclusion.

Finally, once we had a full composition, the AI was going to have to figure out how to orchestrate it, which involves assigning different instruments for different parts.

And it had to pull off these tasks in the way Beethoven might do so.

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The team tested its work, from the September 24, 2021 essay, Note: A link has been removed,

In November 2019, the team met in person again – this time, in Bonn, at the Beethoven House Museum, where the composer was born and raised.

This meeting was the litmus test for determining whether AI could complete this project. We printed musical scores that had been developed by AI and built off the sketches from Beethoven’s 10th. A pianist performed in a small concert hall in the museum before a group of journalists, music scholars and Beethoven experts.

We challenged the audience to determine where Beethoven’s phrases ended and where the AI extrapolation began. They couldn’t.

A few days later, one of these AI-generated scores was played by a string quartet in a news conference. Only those who intimately knew Beethoven’s sketches for the 10th Symphony could determine when the AI-generated parts came in.

The success of these tests told us we were on the right track. But these were just a couple of minutes of music. There was still much more work to do.

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There is a preview of the finished 10th symphony,

Beethoven X: The AI Project: III Scherzo. Allegro – Trio (Official Video) | Beethoven Orchestra Bonn

Modern Recordings / BMG present as a foretaste of the album “Beethoven X – The AI Project” (release: 8.10.) the edit of the 3rd movement “Scherzo. Allegro – Trio” as a classical music video. Listen now: https://lnk.to/BeethovenX-Scherzo

Album pre-order link: https://lnk.to/BeethovenX

The Beethoven Orchestra Bonn performing with Dirk Kaftan and Walter Werzowa a great recording of world-premiere Beethoven pieces. Developed by AI and music scientists as well as composers, Beethoven’s once unfinished 10th symphony now surprises with beautiful Beethoven-like harmonics and dynamics.

For anyone who’d like to hear the October 9, 2021 performance, Sharon Kelly included some details in her August 16, 2021 article for DiscoverMusic,

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The world premiere of Beethoven’s 10th Symphony on 9 October 2021 at the Telekom Forum in Bonn, performed by the Beethoven Orchestra Bonn conducted by Dirk Kaftan, will be broadcast live and free of charge on MagentaMusik 360.

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Sadly, the time is not listed but MagentaMusik 360 is fairly easy to find online.

You can find out more about Professor Elgammal on his Rutgers University profile page. Elgammal has graced this blog before in an August 16, 2019 posting “AI (artificial intelligence) artist got a show at a New York City art gallery“. He’s mentioned in an excerpt about 20% of the way down the page,

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Ahmed Elgammal thinks AI art can be much more than that. A Rutgers University professor of computer science, Elgammal runs an art-and-artificial-intelligence lab, where he and his colleagues develop technologies that try to understand and generate new “art” (the scare quotes are Elgammal’s) with AI—not just credible copies of existing work, like GANs do. “That’s not art, that’s just repainting,” Elgammal says of GAN-made images. “It’s what a bad artist would do.”

Elgammal calls his approach a “creative adversarial network,” or CAN. It swaps a GAN’s discerner—the part that ensures similarity—for one that introduces novelty instead. The system amounts to a theory of how art evolves: through small alterations to a known style that produce a new one. That’s a convenient take, given that any machine-learning technique has to base its work on a specific training set.

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Finally, thank you to @winsontang whose tweet led me to this story.