Sunday, November 10, 2024

AI training method can drastically shorten time for calculations in quantum mechanics



The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising AI for their respective fields of study. KAIST researchers have now succeeded in dramatically shortening the calculation time of highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel approach to teach AI.



Professor Yong-Hoon Kim's team from the School of Electrical Engineering has developed a 3D computer vision artificial neural network-based calculation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations performed using supercomputers to derive the properties of materials.

The density functional theory (DFT) calculations in quantum mechanics using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow for fast and accurate prediction of quantum properties.

However, in current density functional theory (DFT) calculations, a complex self-consistent field (SCF) process of generating three-dimensional electron densities and solving quantum mechanical equations must be repeated tens to hundreds of times, which limits its application to hundreds or thousands of atoms.

Professor Yong-Hoon Kim's research team asked whether it would be possible to avoid the self-consistent field process using the artificial intelligence technique that has recently been rapidly developing. As a result, they developed the DeepSCF model to accelerate calculations by learning chemical bond information distributed in three-dimensional space through a neural network algorithm in the field of computer vision.

The research team focused on the fact that according to density functional theory, electron density contains all the quantum mechanical information of electrons, and in addition, the residual electron density, which is the difference between the total electron density and the sum of the electron densities of the constituent atoms, contains chemical bond information, and selected it as a target for machine learning.

Afterwards, the team adopted a data set of organic molecules containing various chemical bond characteristics, and the atomic structures of the molecules included in it were subjected to arbitrary rotations and deformations to further improve the accuracy and generalization performance of the model. Finally, the research team demonstrated the validity and efficiency of the DeepSCF methodology for complex and large systems.

Professor Yong-Hoon Kim, who led this research, said, "We have found a way to correspond quantum mechanical chemical bonding information distributed in three-dimensional space to an artificial neural network. Since quantum mechanical electronic structure calculations are the basis for all-scale material property simulations, we have established the overall basic principles for accelerating material calculations through artificial intelligence."

 

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Thursday, November 7, 2024

Opinion | Hinduism and Quantum Physics: How Vedas Inspired Western Scientists


The great thinkers of ancient Bharat spoke of how energy and matter at atomic levels could influence one another from long distances. This is now called Quantum Entanglement



Hinduism is the only spiritual tradition which accurately estimates the age of the universe as per modern science. Sanatana Dharma is also an intellectual and multi-cultural tradition attached to sophisticated schools of philosophy, its own system of medicine and psychology, architecture, astronomy, cosmology, metaphysics, metrology, mathematics, and various art forms. Sacred geometry, trigonometry, calculus, the concept of zero and negative numerals, the decimal system, pi, sine and cosine, the idea of multiple big bangs and an inter-dimensional multiverse, can all be traced back to the great minds of Vedic sages.

The very notion of this relationship was unknown and laughable to many in the scientific communities of the West. It was even unknown and laughable to many Indian scientists during and after the colonial rule. It wasn’t until 1989 and 1993, when author Deepak Chopra’s books, ‘Quantum Healing’ and ‘Ageless Body, Timeless Mind’ became popular that the idea that any religion could have much of anything to do with the field of modern science was at all acceptable in any level of academia.


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Tuesday, November 5, 2024

A new model offers robots precise pick-and-place solutions

SimPLE learns to pick, regrasp, and place objects using the objects’ computer-aided design model.





Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy.

“In industry, you often see that [manufacturers] end up with very tailored solutions to the particular problem that they have, so a lot of engineering and not so much flexibility in terms of the solution,” Maria Bauza Villalonga PhD ’22, a senior research scientist at Google DeepMind where she works on robotics and robotic manipulation. “SimPLE solves this problem and provides a solution to pick-and-place that is flexible and still provides the needed precision.”

A new paper by MechE researchers published in the journal Science Robotics explores pick-and-place solutions with more precision. In precise pick-and-place, also known as kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement. The approach, dubbed SimPLE (Simulation to Pick Localize and placE), learns to pick, regrasp and place objects using the object’s computer-aided design (CAD) model, and all without any prior experience or encounters with the specific objects.

“The promise of SimPLE is that we can solve many different tasks with the same hardware and software using simulation to learn models that adapt to each specific task,” says Alberto Rodriguez, an MIT visiting scientist who is a former member of the MechE faculty and now associate director of manipulation research for Boston Dynamics. SimPLE was developed by members of the Manipulation and Mechanisms Lab at MIT (MCube) under Rodriguez’ direction.

“In this work we show that it is possible to achieve the levels of positional accuracy that are required for many industrial pick and place tasks without any other specialization,” Rodriguez says.

Using a dual-arm robot equipped with visuotactile sensing, the SimPLE solution employs three main components: task-aware grasping, perception by sight and touch (visuotactile perception), and regrasp planning. Real observations are matched against a set of simulated observations through supervised learning so that a distribution of likely object poses can be estimated, and placement accomplished.

In experiments, SimPLE successfully demonstrated the ability to pick-and-place diverse objects spanning a wide range of shapes, achieving successful placements over 90 percent of the time for 6 objects, and over 80 percent of the time for 11 objects.

“There’s an intuitive understanding in the robotics community that vision and touch are both useful, but [until now] there haven’t been many systematic demonstrations of how it can be useful for complex robotics tasks,” says mechanical engineering doctoral student Antonia Delores Bronars SM ’22. Bronars, who is now working with Pulkit Agrawal, assistant professor in the department of Electrical Engineering and Computer Science (EECS), is continuing her PhD work investigating the incorporation of tactile capabilities into robotic systems.

“Most work on grasping ignores the downstream tasks,” says Matt Mason, chief scientist at Berkshire Grey and professor emeritus at Carnegie Mellon University who was not involved in the work. “This paper goes beyond the desire to mimic humans, and shows from a strictly functional viewpoint the utility of combining tactile sensing, vision, with two hands.”

Ken Goldberg, the William S. Floyd Jr. Distinguished Chair in Engineering at the University of California at Berkeley, who was also not involved in the study, says the robot manipulation methodology described in the paper offers a valuable alternative to the trend toward AI and machine learning methods.

“The authors combine well-founded geometric algorithms that can reliably achieve high-precision for a specific set of object shapes and demonstrate that this combination can significantly improve performance over AI methods,” says Goldberg, who is also co-founder and chief scientist for Ambi Robotics and Jacobi Robotics. “This can be immediately useful in industry and is an excellent example of what I call 'good old fashioned engineering' (GOFE).”

Bauza and Bronars say this work was informed by several generations of collaboration.

“In order to really demonstrate how vision and touch can be useful together, it’s necessary to build a full robotic system, which is something that’s very difficult to do as one person over a short horizon of time,” says Bronars. “Collaboration, with each other and with Nikhil [Chavan-Dafle PhD ‘20] and Yifan [Hou PhD ’21 CMU], and across many generations and labs really allowed us to build an end-to-end system.”

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AI training method can drastically shorten time for calculations in quantum mechanics

The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in P...