NIPS 2017 Workshop on Machine Learning for Molecules and Materials
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José Miguel Hernández-Lobato
Sep 19, 2017, 10:00:10 AM9/19/17
to Machine Learning News
#### CALL FOR PAPERS ####
NIPS 2017 Workshop on Machine Learning for Molecules and Materials
Friday, December 8, 2017
Long Beach Convention Center, Long Beach, CA, USA
http://www.quantum-machine.org/workshops/nips2017/
Please direct questions and submissions to: qm.ni...@gmail.com
NOTE: The main NIPS 2017 conference is currently sold out. A limited number of workshop registrations are still available, so please register ASAP if you intend to submit a paper. Registrations can be cancelled before Nov. 15th, 2017 for a full refund.
IMPORTANT DATES:
* Mon Oct 18, 2017: Submission deadline at 11:59am UTC
* Fri Nov 10, 2017: Acceptance notification (Poster)
* Thu Nov 16, 2017: NIPS deadline to cancel registration (with full refund)
* Fri Dec 08, 2017: Workshop
============================================================================
#### ABSTRACT
The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.
Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.
Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule and material data. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.
The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.
#### PAPER SUBMISSION
We are calling for contributions on theoretical models, empirical studies, and applications of machine learning for molecules and materials. We also welcome challenge papers on possible applications or datasets. Submitted papers should either address new/interesting problems and insights for chemistry and quantum physics or present progress on established problems.
Topics of interest (though not exhaustive) include: chemoinformatics, applications of deep learning to predict molecular properties, drug-discovery and material design, retrosynthesis and synthetic route prediction, modeling and prediction of chemical reaction data, and the analysis of molecular dynamics simulations.
SUBMISSION INSTRUCTIONS:
Researchers interested in contributing should upload non-anonymized papers of up to 10 pages, including text, figures and bibliographic references by Wednesday, October 18, 2017, 11:59am UTC.
Please submit via email to: qm.ni...@gmail.com
Papers should adhere to the NIPS conference paper format, via the NIPS LaTeX style file:
https://nips.cc/Conferences/2017/PaperInformation/StyleFiles
PEER REVIEW AND ACCEPTANCE CRITERIA
All submissions will undergo non-anonymized peer review.
Accepted papers will be chosen based on technical merit and suitability to the workshop's goals. All accepted papers will be included in the poster sessions on the day of the workshop. Accepted papers will also be invited to give brief, two minute spotlight presentations at the workshop.
COPYRIGHT FOR ACCEPTED PAPERS
This workshop will be informally published online but not officially archived. This means:
* Authors will retain full copyright of their papers.
* Acceptance to this workshop does not preclude publication of the same material in another journal or conference.
We encourage (but do not require) accepted papers to be posted on arXiv. With author permission, we will post links to accepted short papers on our workshop website.
Our workshop does allow submission of papers that are under review or have been recently published in a conference or a journal. Authors should clearly state any overlapping published work at time of submission.
#### CONFIRMED SPEAKERS:
Klaus-Robert Müller (Technische Universität Berlin)
Alán Aspuru-Guzik (Harvard University)
Alexandre Tkatchenko (Université du Luxembourg)
Vijay Pande (Stanford University)
Robert A. DiStasio Jr. (Cornell University)
O. Anatole von Lilienfeld (Universität Basel)
Klavs F. Jensen (MIT)
Kieron Burke (UC Irvine)
Giuseppe Carleo (ETH Zürich)
Lucy Colwell (University of Cambridge)
Alexander J. Smola (AWS/Amazon)
Risi Kondor (University of Chicago)
Koji Tsuda (University of Tokyo)
Michele Ceriotti (École polytechnique fédérale de Lausanne)
Stefan Chmiela (Technische Universität Berlin)
Oriol Vinyals (Google DeepMind)
David Duvenaud (University of Toronto)
Marwin Segler (Benevolent AI)
Kristof T. Schütt (Technische Universität Berlin)
José Miguel Hernández-Lobato (University of Cambridge)
#### ORGANIZERS:
Stefan Chmiela (Technische Universität Berlin)
José Miguel Hernández-Lobato (University of Cambridge)
Kristof T. Schütt (Technische Universität Berlin)
Alán Aspuru-Guzik (Harvard University)
Alexandre Tkatchenko (Université du Luxembourg)
Bharath Ramsundar (Stanford University)
O. Anatole von Lilienfeld (Universität Basel)
Matt J. Kusner (Alan Turing Institute)
Koji Tsuda (University of Tokyo)
Brooks Paige (University of Oxford)
Klaus-Robert Müller (Technische Universität Berlin)
NIPS 2017 Workshop on Machine Learning for Molecules and Materials
Friday, December 8, 2017
Long Beach Convention Center, Long Beach, CA, USA
http://www.quantum-machine.org/workshops/nips2017/
Please direct questions and submissions to: qm.ni...@gmail.com
NOTE: The main NIPS 2017 conference is currently sold out. A limited number of workshop registrations are still available, so please register ASAP if you intend to submit a paper. Registrations can be cancelled before Nov. 15th, 2017 for a full refund.
IMPORTANT DATES:
* Mon Oct 18, 2017: Submission deadline at 11:59am UTC
* Fri Nov 10, 2017: Acceptance notification (Poster)
* Thu Nov 16, 2017: NIPS deadline to cancel registration (with full refund)
* Fri Dec 08, 2017: Workshop
============================================================================
#### ABSTRACT
The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.
Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.
Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule and material data. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.
The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.
#### PAPER SUBMISSION
We are calling for contributions on theoretical models, empirical studies, and applications of machine learning for molecules and materials. We also welcome challenge papers on possible applications or datasets. Submitted papers should either address new/interesting problems and insights for chemistry and quantum physics or present progress on established problems.
Topics of interest (though not exhaustive) include: chemoinformatics, applications of deep learning to predict molecular properties, drug-discovery and material design, retrosynthesis and synthetic route prediction, modeling and prediction of chemical reaction data, and the analysis of molecular dynamics simulations.
SUBMISSION INSTRUCTIONS:
Researchers interested in contributing should upload non-anonymized papers of up to 10 pages, including text, figures and bibliographic references by Wednesday, October 18, 2017, 11:59am UTC.
Please submit via email to: qm.ni...@gmail.com
Papers should adhere to the NIPS conference paper format, via the NIPS LaTeX style file:
https://nips.cc/Conferences/2017/PaperInformation/StyleFiles
PEER REVIEW AND ACCEPTANCE CRITERIA
All submissions will undergo non-anonymized peer review.
Accepted papers will be chosen based on technical merit and suitability to the workshop's goals. All accepted papers will be included in the poster sessions on the day of the workshop. Accepted papers will also be invited to give brief, two minute spotlight presentations at the workshop.
COPYRIGHT FOR ACCEPTED PAPERS
This workshop will be informally published online but not officially archived. This means:
* Authors will retain full copyright of their papers.
* Acceptance to this workshop does not preclude publication of the same material in another journal or conference.
We encourage (but do not require) accepted papers to be posted on arXiv. With author permission, we will post links to accepted short papers on our workshop website.
Our workshop does allow submission of papers that are under review or have been recently published in a conference or a journal. Authors should clearly state any overlapping published work at time of submission.
#### CONFIRMED SPEAKERS:
Klaus-Robert Müller (Technische Universität Berlin)
Alán Aspuru-Guzik (Harvard University)
Alexandre Tkatchenko (Université du Luxembourg)
Vijay Pande (Stanford University)
Robert A. DiStasio Jr. (Cornell University)
O. Anatole von Lilienfeld (Universität Basel)
Klavs F. Jensen (MIT)
Kieron Burke (UC Irvine)
Giuseppe Carleo (ETH Zürich)
Lucy Colwell (University of Cambridge)
Alexander J. Smola (AWS/Amazon)
Risi Kondor (University of Chicago)
Koji Tsuda (University of Tokyo)
Michele Ceriotti (École polytechnique fédérale de Lausanne)
Stefan Chmiela (Technische Universität Berlin)
Oriol Vinyals (Google DeepMind)
David Duvenaud (University of Toronto)
Marwin Segler (Benevolent AI)
Kristof T. Schütt (Technische Universität Berlin)
José Miguel Hernández-Lobato (University of Cambridge)
#### ORGANIZERS:
Stefan Chmiela (Technische Universität Berlin)
José Miguel Hernández-Lobato (University of Cambridge)
Kristof T. Schütt (Technische Universität Berlin)
Alán Aspuru-Guzik (Harvard University)
Alexandre Tkatchenko (Université du Luxembourg)
Bharath Ramsundar (Stanford University)
O. Anatole von Lilienfeld (Universität Basel)
Matt J. Kusner (Alan Turing Institute)
Koji Tsuda (University of Tokyo)
Brooks Paige (University of Oxford)
Klaus-Robert Müller (Technische Universität Berlin)
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