Outline of examples of LLM interaction as
https://writings.stephenwolfram.com/2023/05/the-new-world-of-llm-functions-integrating-llm-technology-into-the-wolfram-language/
Examples of computational Chemistry
https://blog.wolfram.com/2023/05/26/computational-chemistry-find-the-solution-with-wolfram-technologies/
Saw this kind of thing proposed for packaging, with an Origami template, could it work?
Your Next Wooden Chair Could Arrive Flat, Then Dry into a 3D Shape
American Chemical Society
August 23, 2022
Researchers at Israel's Hebrew University of Jerusalem have developed a process in which flat wooden shapes produced by three-dimensional (3D) printers can be programmed to transform into complex 3D shapes. The researchers used a water-based “ink” comprised of wood-waste microparticles and plant-based binders in the printers; they found the pathway of the ink, print speed, and stacking of printed layers determined the final shape of the printed piece as its moisture content evaporates, and that these factors can be controlled to produce different shapes. Said Eran Sharon, one of the project’s principal investigators, “We hope to show that under some conditions we can make these elements responsive—to humidity, for example—when we want to change the shape of an object again.” .... '
Useful direction, having worked in a chem lab, would be useful to have an idea of what you will get.
Quantum Computers Get Smarter at Simulating Chemistry
IEEE Spectrum, Charles Q. Choi, March 17, 2022
A team of scientists from multiple institutions used Google's Sycamore quantum processor to conduct the largest chemistry simulations involving quantum computers to date, tapping a new method that may better handle quantum circuits' noise. The researchers ran a fermionic quantum Monte Carlo algorithm in a hybrid classical-quantum computation framework so it would scale well. They employed 16 quantum bits (qubits) on the 53-qubit Sycamore system to calculate the ground state of H4 molecules, molecular nitrogen, and solid diamond molecules. Google Quantum AI's William Huggins said the hybrid approach can produce very precise ground-state estimates with more noise than the previous holder of the record for chemical simulations with quantum computing, the variational quantum eigensolver (VQE) algorithm, can tolerate. ... '
ew approaches to chemical design
Chemical Reaction Design Goes Virtual
Hokkaido University (Japan),March 14, 2022
A virtual ligand-assisted (VLA) screening method developed by researchers at Japan's Hokkaido University could reduce time spent on trial and error during transition metal catalyst development. The VLA screening method surveys a wide range of values for different properties to determine the most promising features of ligands, which are molecules bonded to the central metal atom of a catalyst. Virtual ligands mimic the presence of real ligands, but rather than being described by numerous individual constituent atoms, they are described using only their steric (space-filling) and electronic properties. Researchers then generate a contour map detailing the combination of steric and electronic effects a ligand should have to best catalyze a specific reaction, so chemists only must test real ligands meeting this criteria. "As the VLA screening can be conducted in silico, it would save a lot of time and resources in the lab," said Hokkaido's Satoshi Maeda. ... '
Consider the value and complexity of multifunctional materials, and how much we are getting from understanding them in detail for design, manufacturing and delivery. Ever new methods for delivering changes in chemistry, physics, crystals and electronics.
ACM TECHNEWS
A Blueprint for Designing, Synthesizing Multifunctional Materials By Penn Today, University of Pennsylvania
Nanocrystal combinations can yield new multifunctional materials through an inverse design blueprint.
Researchers at the University of Pennsylvania (UPenn) and the University of Michigan developed the template by combining theory, computational simulations, chemical synthesis, and assembly.
UPenn's Katherine Elbert said the research intends to surmount the tendency for differently-sized and -shaped nanocrystals to cohere heterogeneously.
The researchers applied a library of laboratory-synthesized nanocrystals and simulations to refine the nanocrystal coating in order to induce ordered solids rather than heterogeneous aggregates.
By following specialized lab methods, the researchers formed a mixed and stable film from two distinct infrared active nanocrystals, highlighting the unexpected value of surface molecule modifications in triggering nanocrystal assemblies.
Stuck me, since some of my earliest looks at code were in this realm. Steganographic or hidden information. Not combine it with AI based methods?
An Uncrackable Combination of Invisible Ink, AI
American Chemical Society, May 5, 2021
Researchers have printed complexly encoded data using a carbon nanoparticle-based ink that can be read only by an artificial intelligence (AI) model when exposed to ultraviolet (UV) light. The researchers created the ‘invisible’ ink, which appears blue when exposed to UV light, using carbon nanoparticles from citric acid and cysteine. They then trained an AI model to identify symbols written in the ink and illuminated by UV light, and to use a special codebook to decode them. The model, which was tested using a combination of normal red ink and UV fluorescent ink, read the messages with 100% accuracy. The researchers said the algorithms potentially could be used for secure encryption with hundreds of unpredictable symbols because they can detect minute modifications in symbols.
An local example of the use of quantum simulation.
UC Chemists Use Supercomputers to Understand Solvents
University of Cincinnati News, Michael Miller, March 19, 2021
University of Cincinnati (UC) chemists Thomas Beck and Andrew Eisenhart used a supercomputer to understand the basic characteristics of an industrial solvent via quantum simulation. The researchers employed the university’s Advanced Research Computing Center and the Ohio Supercomputer Center to investigate glycerol carbonate. Said Eisenhart, "Quantum simulations have been around for quite a while. But the hardware that's been evolving recently—things like graphics processing units and their acceleration when applied to these problems—creates the ability to study larger systems than we could in the past." Eisenhart said the analysis provided insights into how small modifications to molecular structure can have larger effects on the solvent overall, "and how these small changes make its interactions with very important things like ions and can have an effect on things like battery performance."
Heard this posed an an early 'monte carlo' simulation problem which had potential for solutions with quantum.
AI Solves Schrödinger's Equation, a Fundamental Problem in Quantum Chemistry
SciTechDaily
Scientists at Germany's Freie Universität Berlin (FU) have developed a deep learning artificial intelligence (AI) technique for calculating the ground state of the Schrödinger equation in quantum chemistry. FU's Jan Hermann said, "We believe that deep 'Quantum Monte Carlo', the approach we are proposing, could be equally, if not more successful [than functional theory]. It offers unprecedented accuracy at a still acceptable computational cost." The FU team's deep neural network is a novel approach for representing electronic wave functions, capable of learning the intricate patterns in which electrons surround nuclei by incorporating the functions' antisymmetry, or "Pauli's exclusion principle." FU's Frank Noé said, "Building the fundamental physics into the AI is essential for its ability to make meaningful predictions in the field. This is really where scientists can make a substantial contribution to AI, and exactly what my group is focused on." ...
Some thoughts about how trained networks can be used to further analyse chemical structure.
Opening the Black Box of Neural Networks
Pacific Northwest National Laboratory, Allan Brettman
Pacific Northwest National Laboratory (PNNL) researchers used deep learning neural networks to model water molecule interactions, unearthing data about hydrogen bonds and structural patterns. The PNNL team employed 500,000 water clusters from a database of more than 5 million water cluster minima to train a neural network, relying on graph theory to extract structural patterns of the molecules' aggregation. The method provides additional analysis after the network has been trained, allowing comparison between measurements of the water cluster networks' structural traits and the predicted neural network, enhancing the network's understanding in subsequent analyses. PNNL's Jenna Pope said, "If you were able to train a neural network, that neural network would be able to do computational chemistry on larger systems. And then you could make similar insights in computational chemistry about chemical structure or hydrogen bonding or the molecules’ response to temperature changes.”
This could be a very big deal, considerable details at the link. Had a job in a chem lab long ago that looked at this problem.
Researchers develop a solar tech that splits water into hydrogen and oxygen with record efficiency By Mark Anderson in IEEE Spectrum
Israeli and Italian scientists have developed a renewable energy technology that converts solar energy to hydrogen fuel — and it’s reportedly at the threshold of “practical” viability.
The new solar tech would offer a sustainable way to turn water and sunlight into storable energy for fuel cells, whether that stored power feeds into the electrical grid or goes to fuel-cell powered trucks, trains, cars, ships, planes or industrial processes. ... '
