BigDFT https://l_sim.gitlab.io/bigdft-doc A fast, precise and flexible DFT code for ab-initio atomistic simulation Fri, 22 Jan 2021 08:52:59 +0000 en-US hourly 1 https://wordpress.org/?v=5.5.6 Post doctoral offer: Modeling antibody/antigen assemblies https://l_sim.gitlab.io/bigdft-doc/news/modelingaapolarisbigdft/ https://l_sim.gitlab.io/bigdft-doc/news/modelingaapolarisbigdft/#respond Fri, 22 Jan 2021 08:52:59 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=293 Host Laboratory: Joliot Institute, CEA-Saclay and Paris-Saclay University, France. Collaboration with SANOFI and CEA-Grenoble. Position open on March 2021. Targeting specific monoclonal antibodies can be routinely achieved, however increasing the antigen affinity as much as desired is still a challenging task. Most of the available theoretical tools in this field mainly focus on investigating close [...]

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Host Laboratory:
Joliot Institute, CEA-Saclay and Paris-Saclay University, France. Collaboration with SANOFI and CEA-Grenoble. Position open on March 2021.

Targeting specific monoclonal antibodies can be routinely achieved, however increasing the antigen affinity as much as desired is still a challenging task. Most of the available theoretical tools in this field mainly focus on investigating close contact antibody/antigen (AA) local regions and usually ignore the effect on affinity of more distant domains. Because of the size of AA assemblies only standard pairwise molecular modeling force fields or empirical cost functions are used to quantify the strength of their interactions. However these theoretical approaches are known to be based on crude approximations preventing to reach a level of accuracy which is sufficienlty high.
Our project, awarded by a SANOFI iTech Award 2020, adds two new steps to standard computational protocols used to model AA assemblies (like for instance the popular one based on the Rosetta package of programs) to further evaluate and refine their solutions. These two steps consist in:

(1) in investigating the AA potential energy surface from molecular dynamics Replica Exchange simulations based on a polarizable multi-scale molecular modeling approach;
(2) in refining the simulation results using the complexity reduction framework of BigDFT, that enables to compute the quantum interaction energy of full AA assemblies.

The candidate will work in collaboration with three teams, of the French Energy Agency (CEA) and of the pharmaceutical group SANOFI.
He/She will in particular help in coupling the above new modeling approaches with standard docking ones in order to build a numerical tool that will be used routinely in pharmaceutical industry R&D workflows.

Competences required:
Solid competence in docking and/or molecular dynamics of Antigen/Antibody assemblies, competence in Python programming. Competence in other languages like Perl or C++ will be appreciated.

Duration:
The position is initially open for a twelve-month period. Opportunities for renewal are possible.

Contacts:
Michel Masella, Joliot Institute, CEA-Saclay, email : michel DOT masella AT cea.fr, website : http://biodev.cea.fr/polaris/
Luigi Genovese, L_Sim laboratory, CEA- Grenoble, email : luigi DOT genovese AT cea.fr, (bigdft.org)
Alessandro Masiero, SANOFI R&D, Vitry sur Seine, email : Alessandro DOT Masiero AT sanofi.com

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Pilot Study Project of BigDFT awarded on Fugaku Supercomputer in Japan https://l_sim.gitlab.io/bigdft-doc/news/pilot-study-project-of-bigdft-awarded-on-fugaku-supercomputer-in-japan/ https://l_sim.gitlab.io/bigdft-doc/news/pilot-study-project-of-bigdft-awarded-on-fugaku-supercomputer-in-japan/#respond Tue, 08 Dec 2020 08:28:30 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=258 A project which plans to utilize BigDFT to study potential inhibitors of the SARS-CoV-2 virus was approved as a Pilot Study Project for the new Fugaku supercomputer [1]. The Fugaku supercomputer, hosted at the RIKEN Center for Computational Science, features a novel Fujitsu A64FX CPU and is currently the fastest supercomputer in the world. This [...]

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A project which plans to utilize BigDFT to study potential inhibitors of the SARS-CoV-2 virus was approved as a Pilot Study Project for the new Fugaku supercomputer [1]. The Fugaku supercomputer, hosted at the RIKEN Center for Computational Science, features a novel Fujitsu A64FX CPU and is currently the fastest supercomputer in the world. This computational power will be exploited by BigDFT to perform full quantum mechanical modeling of the main protease of the SARS-CoV-2 virus in complex with a large database of potential inhibitors. For an overview of what kinds of scientific information can be derived from a BigDFT calculation of biological systems, we recommend the recent review Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations [2].

References:
[1] High Performance Computing Infrastructure of Japan [see here]
[2] The Journal of Chemical Physics 2020 152 (19) 194110, DOI

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BigDFT webinar in the MaX Centre Webinar of Flagship Codes series https://l_sim.gitlab.io/bigdft-doc/news/bigdft-webinar-in-the-max-centre-webinar-of-flagship-codes-series/ https://l_sim.gitlab.io/bigdft-doc/news/bigdft-webinar-in-the-max-centre-webinar-of-flagship-codes-series/#respond Thu, 12 Nov 2020 08:08:17 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=188 In this webinar, titled “Flexibilities of Wavelets for Electronic Structure Calculations in Large Systems", we will present some of the features that have been made possible by the peculiar properties of Daubechies wavelets. In particular, we focus our attention on the usage of DFT for large-scale systems. We show how the localized description of [...]

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In this webinar, titled “Flexibilities of Wavelets for Electronic Structure Calculations in Large Systems”, we will present some of the features that have been made possible by the peculiar properties of Daubechies wavelets. In particular, we focus our attention on the usage of DFT for large-scale systems. We show how the localized description of the KS problem, emerging from the features of the basis set, is helpful in providing a simplified description of large-scale electronic structure calculations. During the presentation, we will highlight how the MaX consortium enabled the possibility of the implementation of advanced functionalities in the contest of pre-exascale computing. Join our webinar on 12th November, 10am (CET).

See here the webinar video: http://www.max-centre.eu/webinar/flexibilities-wavelets-electronic-structure-calculations-large-systems

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Paper “Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations” published by Laura Ratcliff et al. in The Journal of Chemical Physics https://l_sim.gitlab.io/bigdft-doc/news/paper-flexibilities-of-wavelets-as-a-computational-basis-set-for-large-scale-electronic-structure-calculations-published-by-laura-ratcliff-et-al-in-the-journal-of-chemical-physics/ https://l_sim.gitlab.io/bigdft-doc/news/paper-flexibilities-of-wavelets-as-a-computational-basis-set-for-large-scale-electronic-structure-calculations-published-by-laura-ratcliff-et-al-in-the-journal-of-chemical-physics/#respond Wed, 20 May 2020 07:09:44 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=127 The BigDFT project was started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn–Sham (KS) density functional theory (DFT) with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features of flexibility, performance, and precision of the [...]

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The BigDFT project was started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn–Sham (KS) density functional theory (DFT) with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features of flexibility, performance, and precision of the results. In particular, the employed formalism has enabled the implementation of an algorithm able to tackle DFT calculations of large systems, up to many thousands of atoms, with a computational effort that scales linearly with the number of atoms. In this work, we recall some of the features that have been made possible by the peculiar properties of Daubechies wavelets.

Reference:

Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations

Laura E. Ratcliff, William Dawson, Giuseppe Fisicaro, Damien Caliste, Stephan Mohr, Augustin Degomme, Brice Videau, Viviana Cristiglio, Martina Stella, Marco D’Alessandro, Stefan Goedecker, Takahito Nakajima, Thierry Deutsch, and Luigi Genovese

The Journal of Chemical Physics 2020 152 (19) 194110, DOI: 10.1063/5.0004792

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Granted 15 million CPU hours on Joliot-Curie supercomputer to search inhibitors of COVID-19 https://l_sim.gitlab.io/bigdft-doc/news/granted-15-million-cpu-hours-on-joliot-curie-supercomputer-to-search-inhibitors-of-covid-19/ https://l_sim.gitlab.io/bigdft-doc/news/granted-15-million-cpu-hours-on-joliot-curie-supercomputer-to-search-inhibitors-of-covid-19/#respond Mon, 20 Apr 2020 08:06:48 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=186 Researchers from the Grenoble Interdisciplinary Research Institute (IRIG) and the CEA's Joliot Institute are working on the search of inhibitors in COVID-19. The SPIKE protein allows the virus to penetrate the cell membrane. Thanks to the simulation of the electronic structure of the protein and the associated inhibitor, it is possible to provide precise information [...]

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Researchers from the Grenoble Interdisciplinary Research Institute (IRIG) and the CEA’s Joliot Institute are working on the search of inhibitors in COVID-19. The SPIKE protein allows the virus to penetrate the cell membrane. Thanks to the simulation of the electronic structure of the protein and the associated inhibitor, it is possible to provide precise information on the strength of the inhibition but also structural information to identify the amino acids concerned and their associated polarities. This initial work made it possible to validate the approach and thus to submit a 15 million hour project to PRACE to study the microscopic and thermodynamic factors that may or may not favour the interaction between the main SARS-CoV-2 protease and promising new inhibitors. The objective is to build an ab initio in silico tool to estimate accurately the interaction properties of proteins interacting with all types of ligand families.

Source: https://www.genci.fr/en/node/1046

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Paper “Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding” published by William Dawson et al. in Journal of Chemical Theory and Computation https://l_sim.gitlab.io/bigdft-doc/news/paper-complexity-reduction-in-density-functional-theory-calculations-of-large-systems-system-partitioning-and-fragment-embedding-published-by-william-dawson-et-al-in-journal-of-che/ https://l_sim.gitlab.io/bigdft-doc/news/paper-complexity-reduction-in-density-functional-theory-calculations-of-large-systems-system-partitioning-and-fragment-embedding-published-by-william-dawson-et-al-in-journal-of-che/#respond Fri, 27 Mar 2020 07:09:41 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=126 With the development of low order scaling methods for performing Kohn–Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large [...]

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With the development of low order scaling methods for performing Kohn–Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.

Reference:

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding

William Dawson, Stephan Mohr, Laura E. Ratcliff, Takahito Nakajima, and Luigi Genovese

Journal of Chemical Theory and Computation 2020 16 (5), 2952-2964

DOI: 10.1021/acs.jctc.9b01152

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Awarded the MaX Centre for Materials design at the Exascale (MaX-2) EU HPC Center of Excellence https://l_sim.gitlab.io/bigdft-doc/news/awarded-the-max-centre-for-materials-design-at-the-exascale-max-2-eu-hpc-center-of-excellence/ https://l_sim.gitlab.io/bigdft-doc/news/awarded-the-max-centre-for-materials-design-at-the-exascale-max-2-eu-hpc-center-of-excellence/#respond Sat, 01 Dec 2018 07:09:46 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=128 MaX is a user-focused, problem-oriented European Centre of Excellence (call H2020 INFRAEDI-02-2018 - HPC PPP - Centres of Excellence on HPC) aimed to disenthrall the EU leadership in materials modelling, simulations, discovery, and design. It works at the frontiers of the current and future High Performance Computing (HPC) technologies, to enable the best use and [...]

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MaX is a user-focused, problem-oriented European Centre of Excellence (call H2020 INFRAEDI-02-2018 – HPC PPP – Centres of Excellence on HPC) aimed to disenthrall the EU leadership in materials modelling, simulations, discovery, and design. It works at the frontiers of the current and future High Performance Computing (HPC) technologies, to enable the best use and evolution of HPC for materials research and innovation. MaX aims to create an ecosystem of capabilities, ambitious applications, data workflows and analysis, and user-oriented services. MaX strategy will be focused on enabling the exascale transition in the materials domain, by developing advanced programming models, novel algorithms, domain-specific libraries, in-memory data management, software/hardware co-design, and technology-transfer actions. BigDFT is a flagship code of MaX.

Source: Cordis Europe – Materials Design at the eXascale 

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GPU-accelerated BigDFT available in NVIDIA NGC Catalog https://l_sim.gitlab.io/bigdft-doc/news/gpu-accelerated-bigdft-available-in-nvidia-ngc-catalog/ https://l_sim.gitlab.io/bigdft-doc/news/gpu-accelerated-bigdft-available-in-nvidia-ngc-catalog/#respond Fri, 01 Jun 2018 07:00:26 +0000 https://l_sim.gitlab.io/bigdft-doc/?p=123 The GPU-accelerated BigDFT container is now available for download from the NVIDIA GPU Cloud. The BigDFT container is optimized and tested for reliability to run on NVIDIA Pascal™- and NVIDIA Volta-powered systems with CUDA 9 or newer. For installation and additional information go here To the catalog go here

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The GPU-accelerated BigDFT container is now available for download from the NVIDIA GPU Cloud. The BigDFT container is optimized and tested for reliability to run on NVIDIA Pascal™- and NVIDIA Volta-powered systems with CUDA 9 or newer.

For installation and additional information go here

To the catalog go here

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