Author Archive: Pavlo O. Dral

Videos from SMLQC-2023

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SMLQC-2023 (The Second International Symposium on Machine Learning in Quantum Chemistry) is over, but we have a collection of videos with the talks!

All SMLQC-2023 videos: https://www.youtube.com/playlist… (originally from the conference host’s channel: https://www.youtube.com/playlist… – worth subscribing!)

Browse and subscribe to the YouTube channel of SMLQC https://www.youtube.com/@mlqc/playlists

Here is the talk by the first speaker Olexandr Isayev on AIMNet 2: A Neural Network Potential to Meet your Neutral, Charged, Organic, & Elemental-Organic Needs:

SMLQC 2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry

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The 2nd edition of the International Symposium on Machine Learning in Quantum Chemistry, SMLQC 2023, is approaching and will be held from November 29 to December 1 in Uppsala, Sweden. See the symposium’s website https://www.smlqc2023.com/ for more information.

The deadline for abstract submission for poster presentations and early-bird registration is 31 October 2023.

SMLQC gathers the theoretical and computational chemists, who use machine learning to accelerate and improve quantum chemical simulations. Many of the leading specialists in this expanding field will be giving invited talks at SMLQC 2023, see https://www.smlqc2023.com/77-2/.

SMLQC is a biannual event and the first edition, SMLQC 2021 in Xiamen, China, was a great success with a selection of inspiring talks available online: http://smlqc.mlatom.com/symposia/smlqc-2021/.

The SMLQC 2023 is organized in five thematic sessions reflecting relevant trends of machine learning application to quantum chemistry.

  1. The first theme is using ML for learning the very core of quantum chemistry – the wavefunction, either via unsupervised or supervised approaches, which can be then used to derive the required molecular and material properties.
  2. The second thematic session is one of the biggest and most mature – applications of ML as a force field which can in turn be employed for such typical simulations as geometry optimizations and dynamics.
  3. The third session is dedicated to highlighting recent progress in improving electronic structure methods with ML.
  4. The fourth theme is more of an applied nature and will deal with advances in ML for molecular engineering and materials discovery with desired quantum chemical properties.
  5. The final, fifth, thematic session will give the stage to highlighting how ML can be used to obtain not just numbers but insights through unsupervised learning and interpretable ML as well as performing analysis of molecular structure through simulating and interpreting spectra.

We are looking forward to seeing you in Uppsala!

The organizing committee:

Roland Lindh, Chao Zhang, and Ignacio F. Galván (Uppsala University, Sweden)

Pavlo O. Dral (Xiamen University, China)

SMLQC-2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry

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The second edition of the International Symposium on Machine Learning in Quantum Chemistry will be held in person in Uppsala from 29 Nov. to 1 Dec. 2023. More updates to follow on the Symposium website smlqc2023.com!

Following the success of the 1st International Symposium on Machine Learning in Quantum Chemistry (SMLQC-2021) held online on Nov. 12–14, 2021 and hosted in Xiamen, China, we are happy to announce that the the next edition will be held in Uppsala, Sweden. We thank its main organizer Prof. Roland Lindh for his generous offer to host SMLQC-2023. Updates will be posted on the Symposium website as well as on Facebook via group Machine learning in chemistry and on Twitter. 

The International Symposium on Machine Learning in Quantum Chemistry (SMLQC) 29/11 – 1/12, 2023, at Uppsala University, Uppsala, Sweden, will gather theoretical and computational chemists, who use machine learning to accelerate and improve quantum chemical simulations. The symbiosis between machine learning technology and quantum chemistry offer a new ground for novel and impressive leaps forward for computational chemistry – this is the place with the most interesting development potential over the next few years. The themes of the conference include, but will not be limited to, the development of new quantum chemical techniques improved by machine learning, development of new machine learning methods for describing potential energy surfaces and running molecular dynamics, and application of machine learning for description of various physicochemical processes. The organizing committee is pleased to welcome you to participate at the conference, share view with other researchers in the field, to relax and have fun, and to listen to the world leaders in this expanding field of research presenting their recent accomplishments.


You are very welcome!
On behalf of the organizing committee,
Roland Lindh

Organising committee:

Pavlo Dral, Xiamen University, China
Chao Zhang, Uppsala University, Sweden
Ignacio F. Galván, Uppsala University, Sweden
Roland Lindh, Uppsala University, Sweden

SMLQC seminar by Fang Liu, May 18, 2023

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The 7th SMLQC seminar will be given by Fang Liu on May 18, 2023 (15:00 Paris | 21:00 Beijing | 09:00 New York).

Title

Machine learning aided fast and accurate quantum chemistry for solvated molecules

Abstract

The fast and accurate description of the solvent environment is crucial for quantum chemical (QC) discovery in the solution phase. We combine high-performance computing hardware and machine learning (ML) techniques to improve the efficiency and accuracy of QC discovery of solvated molecules.   To improve the efficiency, we developed strategies to accelerate both the implicit and explicit solvent models. For the implicit solvent models such as the conductor-like polarization model (C-PCM), we exploited graphical processing units (GPUs) to accelerate the calculation and achieved hundreds of speedups for ground- and excited- state calculations. For the explicit solvent model, we developed AutoSolvate, an open-source toolkit to streamline the QC calculation workflow of explicitly solvated molecules.  To improve the accuracy, we develop ML models to reduce the discrepancy between experimental measurements and computationally predicted molecular properties in both implicit and explicit solvent models. For ground-state redox potential calculations, our ML models can reduce both the systematic bias and the number of outliers, and the ML corrected results demonstrate less sensitivity to density functional theory (DFT) functional choice. This ML correction technique is transferrable to predicting other molecular properties in the solution phase, including the excited state redox potential and absorption/fluorescence wavelength.

Introduction to the speaker

Dr. Fang Liu is an assistant professor at the Department of Chemistry, Emory University. Her research focuses on developing algorithms to accelerate quantum chemistry calculations on GPUs and using machine learning to accelerate chemistry discovery. Dr. Liu obtained her B.S. in Chemical Physics at the University of Science and Technology of China in 2011, and Ph.D. in Chemistry at Stanford University in 2017. She did postdoctoral research in MIT from 2017 to 2020 and joined Emory University in 2020.

How to join

Join Zoom Meeting 
https://zoom.us/j/86004422973?pwd=WjNKQlEydmdFL3hJbUx4NjByYjVJZz09

Meeting ID: 860 0442 2973 

Passcode: 703098

Seminars on Machine Learning in  Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC)

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We are pleased to announce the launch of Seminars on Machine Learning in  Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC) that are flexible online lectures and tutorials for highlighting recent developments and providing hands-on experience on the title topics as well as networking opportunities. SMLQC sessions are organized to bridge Symposia on Machine Learning and Quantum Chemistry, the first one held in 2021 in Xiamen, China (online) and the second one to be held in 2023 in Uppsala, Sweden (hybrid). The real-time interaction is enabled via a dedicated Slack workspace which already has many researchers active in the title fields.

Continue reading→

SMLQC-2021 is over. Viva SMLQC!

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Symposium on Machine Learning in Quantum Chemistry 2021 (SMLQC-2021) has been a huge success with many great talks and discussions, chats after talks, and poster sessions! This success has prompted us to continue and expand this kind of events on request and suggestions of many participants. We will have biweekly online talks on the topic of machine learning in quantum chemistry and biannual symposia. The next edition SMLQC-2023 will be held in Uppsala, Sweden.

We have had three days filled with exciting talks by inspiring speakers presenting their work on machine learning in quantum chemistry research. In addition, I am very thankful to the speakers for hanging around after their session was over and chatting with the attendees in the breakout rooms. I definitely enjoyed this informal part of the symposium as I could see and talk so many good friends of mine like in in-person conference, and, equally importantly, meet so many new people. This sentiment is shared by many participants and as a result we have got many requests and suggestions to continue with this kind of events.

Particularly impressive was enthusiastic engagement of Prof. Roland Lindh from Uppsala University, who really deserves a badge of honor for staying around for the whole events despite a 7-hour time-zone difference! His generous offer to host the next edition SMLQC-2023 in Uppsala is greatly appreciated. I will post updates here, on Facebook via group Machine learning in chemistry, and on Twitter. I hope to see you at SMLQC-2023 in person in ca. 24 months! We plan to hold SMLQC biannually in different regions of the world.

Screenshot from the closing of SMLQC-2021

You do not need to wait another two years though, and can also join our biweekly online talks on the topic of machine learning in quantum chemistry. The talks are planned to be recorded and openly posted online. I will post updates on this soon too.

Finally, I personally would like to thank all the co-organizers and helping hands, who worked very hard overtime at late night, in the early mornings and the weekends to make the event possible and enjoyable.