A machine learning solution for designing materials with desired optical properties

A Machine Learning Solution for Designing Materials with Desired Optical Properties
Controlling light-matter interactions is central to a variety of important applications, such as quantum dots, which can be used as light emitters and sensors. Credit: PlasmaChem

Understanding how matter interacts with light—its optical properties—is critical in a myriad of energy and biomedical technologies, such as targeted drug delivery, quantum dots, fuel combustion, and cracking of biomass. But calculating these properties is computationally intensive, and the inverse problem—designing a structure with desired optical properties—is even harder.

Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems—calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. Their study was published in Cell Reports Physical Science.

“Our model performs bi-directionally with high accuracy and its interpretation qualitatively recovers physics of how metal and dielectric materials interact with light,” said corresponding author Sean Lubner.

Lubner notes that understanding radiative properties (which includes optical properties) is equally important in the natural world for calculating the impact of aerosols such as black carbon on climate change.

The machine learning model proposed in this study was trained on spectral emissivity data from nearly 16,000 particles of various shapes and materials that can be experimentally fabricated.

“Our machine learning model speeds up the inverse design process by at least two to three orders of magnitude as compared to the traditional method of inverse design,” said co-author Ravi Prasher, who is also Berkeley Lab’s Associate Director for Energy Technologies.

Mahmoud Elzouka, Charles Yang, and Adrian Albert, all scientists in Berkeley Lab’s Energy Technologies Area, were also co-authors.

Inverse design software automates design process for optical, nanophotonic structures

More information:
Mahmoud Elzouka et al, Interpretable Forward and Inverse Design of Particle Spectral Emissivity Using Common Machine-Learning Models, Cell Reports Physical Science (2020). DOI: 10.1016/j.xcrp.2020.100259
Provided by
Lawrence Berkeley National Laboratory

A machine learning solution for designing materials with desired optical properties (2020, December 2)
retrieved 2 December 2020
from https://phys.org/news/2020-12-machine-solution-materials-desired-optical.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.

Source Article

Read more

Quantum computing may make current encryption obsolete, a quantum internet could be the solution

“The quantum threat is basically going to destroy the security of networks as we know them today,” declared Bruno Huttner, who directs strategic quantum initiatives for Geneva, Switzerland-based ID Quantique. No other commercial organization since the turn of the century has been more directly involved in the development of science and working theories for the future quantum computer network.

One class of theory involves cryptographic security. The moment a quantum computer (QC) breaks through the dam currently held in place by public-key cryptography (PKC), every encrypted message in the world will become vulnerable. That’s Huttner’s “quantum threat.”


“A quantum-safe solution,” he continued, speaking to the Inside Quantum Technology Europe 2020 conference in late October, “can come in two very different aspects. One is basically using classical [means] to address the quantum threat. The other is to fight quantum with quantum, and that’s what we at ID Quantique are doing most of the time.”

There is a movement called post-quantum cryptography (PQC), which incorporates efforts to generate more robust classical means to secure encrypted communications, once quantum methods are made reliable. The other method, to which Huttner subscribes, seeks to encrypt all communications through quantum means. Quantum key distribution (QKD) involves the generation of a cryptographic key by a QC, for use in sending messages through a quantum information network (QIN).

Interfacing a QIN with an electronic Internet, the way we think about such connections today, is physically impossible. Up until recently, it’s been an open question whether any mechanism could be created, however fantastic or convoluted it may become, to exchange usable information between these two systems — which, at the level of physics, reside on different planes of existence.

Could a quantum Internet connect non-quantum computers?

At IQT Europe, however, there were notes of hope.


“I don’t see why you would need a quantum computer,” remarked Mathias Van Den Bossche, who directs research into telecommunications and navigation systems for orbital satellite components producer Thales Alexia Space, “to operate a quantum information network. Basically the tasks will be rather simple.”

The implications of what Van Den Bossche is implying, during a presentation to IQT Europe, may not be self-evident today, though certainly they will be over the course of history. A quantum information network (QIN) is a theoretical concept, enabling the intertwining of pairs of quantum computers (QC) as though they were physically joined to one another. The product of a QIN connection would be not so much an interfacing of two processors but a binding of two systems, whose resulting computational limit would be 2 to the power of the sum of their quantum components, or qubits. It would work, so long as our luck with leveraging quantum mechanics the way we’ve done so far, continues to pan out in our favor.

Van Den Bossche’s speculation is not meant to imply that quantum networking could be leveraged to bind together conventional, electronic computers in the same way — for example, giving any two desktop computers as

Read more

Top US investment consultant selected Diligend solution to automate and digitize investment managers operational due diligence

NEW YORK, Oct. 27, 2020 /PRNewswire/ — Diligend, a leading provider of investment management software, has been selected by FEG Investment Advisors (FEG), an independent investment consulting and OCIO firm, to automate and digitize the collection of manager data and documents in its operational due diligence (ODD) of investment managers across public and private markets and hedge funds.


Diligend Logo
Diligend Logo


FEG partnered with Diligend for a robust, flexible, and reliable solution to support their growth and the centralization of their ODD practice in order to provide a more effective and efficient due diligence process.

Diligend specializes in the collection and in-depth analysis of qualitative and quantitative manager data, from initial onboarding to ongoing monitoring for ODD, manager research, ESG and compliance teams. The technology frees up much-needed time by automating and simplifying processes that previously required a heavy manual workload. With Diligend’s technology solution, FEG will be able to efficiently collect and digitize both qualitative and quantitative information and produce ODD reports providing scoring on important operational tenets.

The flexible nature of Diligend’s platform combined with their ongoing innovation will enable us to more efficiently support our growing client base and their evolving due diligence needs and expectations,” said Douglas Walouke, CFA, Director of Operational Due Diligence at FEG. “Building upon FEG’s historical diligence efforts with our dedicated ODD practice under development, we identified Diligend as the right fit because their technology solutions will not only help streamline our due diligence processes, but will also enable us to perform critical in-depth analysis and improved reporting on our clients’ investment managers.”

Diligend’s platform gives consultant clients the ability to more easily gather comprehensive and accurate timely due diligence data from their investment managers, allowing them in turn to bring increased oversight and tailored analysis to their own clients.

“We are delighted to have FEG as a client and to have the opportunity to work closely with their team to efficiently digitize their due diligence processes. We are looking forward to a successful long-term relationship with FEG,” said Louise Verga, Managing Partner at Diligend.

About FEG Investment Advisors

FEG has more than three decades of experience helping institutional investors build long-term focused portfolios through services that include traditional consulting and OCIO as well as alternative strategies’ investment manager research, due diligence, and monitoring. For more information, visit www.feg.com.

Logo – https://mma.prnewswire.com/media/1276462/Diligend_Logo.jpg

Louise Verga
[email protected]


View original content:http://www.prnewswire.com/news-releases/top-us-investment-consultant-selected-diligend-solution-to-automate-and-digitize-investment-managers-operational-due-diligence-301159617.html

SOURCE Diligend, Inc.

Source Article

Read more

New Measurement Shows Licensing Restrictions Depress Wage Growth, Here’s The Solution

Topline: A new measurement tool has been introduced by researchers at the Mercatus Center for tracking occupational licensing across states. States and occupations with the greatest licensing restrictions also have the weakest wage growth. The data will be useful for policy analysts and researchers alike to benchmark states and advocate for lower barriers.

You Can’t Improve What You Can’t Measure

Thanks to a new measurement tool developed by Patrick McLaughlin’s team at George Mason University’s Mercatus Center, we have a measure of licensing restrictions in states and occupations. Economists have long viewed these restrictions as roadblocks to competition and wage growth, but lacked reliable and comprehensive ways of measuring them.

McLaughlin remarked that “this dataset, like all the data that we produce, represents an advance in our ability to measure policy. Better measurement leads to better research insights, and we ultimately believe that those insights will lead to better policy. The old adage applies here: What gets measured, gets managed.” That’s arguably why government has become so complex—layers keep getting added on since the difficulty in measurement makes accountability tough.

We can see how states perform with one another. Perhaps not surprisingly, California ranks the worst, followed by Ohio. And, these differences are not just driven by the fact that California and Ohio have large populations and, therefore, more regulation. For example, if you divide occupational licensing restrictions by total restrictions for each state, I obtain a correlation of 66% between the two, meaning that states with more licensing restrictions also have more regulation overall.

What states perform the best? Idaho, Nevada, South Dakota, Montana, North Dakota, and Arizona have the least restrictions. Arizona is an interesting example because it ranks high in gross domestic product (GDP), whereas these other low-licensing states have much lower GDP. That’s not by chance: thanks to Doug Ducey’s efforts, Arizona passed a bill that recognizes out-of-state licenses, allowing many people to obtain their licenses without the traditional barriers.

One way of exploring the economic ramifications of these licensing restrictions is by linking them with wage growth. We can see that the occupations in states with lower earnings growth between 2016 and 2019 are the same states and occupations with more licensing restrictions in 2020. In fact, the correlation between the two is -0.18, which is fairly strong given all the other factors at play.

These results are consistent with economic research. For example, University of Minnesota Professor Morris Kleiner, one of the pioneers in this area, remarked that “overall occupational licensing raises wages between 8 to 18 percent depending on the time period and the national data sets that are used in the analysis. Occupational licensing also reduces employment

Read more