diff --git a/db/citations.yml b/db/citations.yml index 52364b46..a298d958 100644 --- a/db/citations.yml +++ b/db/citations.yml @@ -386,36 +386,6 @@ anker;npjcm22: url: https://doi.org/10.1038/s41524-022-00896-3 volume: '8' year: '2022' -guo;npjcm24: - ackno: Work in the Lipson group was supported by U.S. National Science Foundation under AI Institute for - Dynamical Systems grant 372 2112085. Work in the Billinge group was supported by the U.S. Department of - Energy, Office of Science, Office of Basic Energy Sciences (DOE-BES) under contract No. DE-SC0024141. - author: - - Gabe Guo - - Judah Goldfeder - - Ling Lan - - Aniv Ray - - Albert Hanming Yang - - Boyuan Chen - - Simon J. L. Billinge - - Hod Lipson - doi: '' - entrytype: article - facility: '' - grant: doeneutron23 - journal: npj Computational Materials - month: '' - nb: '' - number: '' - note: to be published. Available on arXiv - pages: '' - professional_summary: "" - synopsis: '' - tags: ml, structure_solution, modeling - title: - url: https://doi.org/10.1038/s41524-022-00896-3 - volume: '8' - year: '2022' antic;jpcm13: author: - Antic, B. @@ -5853,6 +5823,39 @@ guguc;sa18: wwwemail: '' wwwpub: http://slapper.apam.columbia.edu/bib-eu9iifae/papers/ year: '2018' +guo;npjcm24: + ackno: Work in the Lipson group was supported by U.S. National Science Foundation + under AI Institute for Dynamical Systems grant 372 2112085. Work in the Billinge + group was supported by the U.S. Department of Energy, Office of Science, Office + of Basic Energy Sciences (DOE-BES) under contract No. DE-SC0024141. + author: + - Gabe Guo + - Judah Goldfeder + - Ling Lan + - Aniv Ray + - Albert Hanming Yang + - Boyuan Chen + - Simon J. L. Billinge + - Hod Lipson + doi: 10.1038/s41524-024-01401-8 + entrytype: article + facility: '' + grant: doeneutron23 + journal: npj Computational Materials + month: Sep + nb: '' + number: '' + optnote: to be published. Available on arXiv + pages: '209' + professional_summary: '' + synopsis: The variational encoder that can regress electron density in cubic and + trigonal systems. + tags: ml, structure_solution, modeling + title: Towards end-to-end structure determination from x-ray diffraction data using + deep learning + url: https://doi.org/10.1038/s41524-024-01401-8 + volume: '10' + year: '2024' guo;unpub23: ackno: Work in the Lipson group was supported by U.S. National Science Foundation under AI Institute for Dynamical Systems grant 2112085. Work in the Billinge group diff --git a/db/institutions.yml b/db/institutions.yml index 2fd6c9b0..96976e43 100644 --- a/db/institutions.yml +++ b/db/institutions.yml @@ -2698,6 +2698,18 @@ ucsandiego: updated: 2021-11-23 07:20:22.864254 uuid: ce930e69-3a2c-4ef8-87a7-1ac70eb0832c zip: '92093' +ucsb: + city: Santa Barbara + country: USA + date: 2021-11-23 + departments: + matsci: + name: Materials Department + name: University of California Santa Barbara + state: CA + updated: 2021-11-23 07:20:22.864254 + uuid: ce930e69-3a2c-4ef8-87a7-1ac70eb0832c + zip: '93106' udaressalaam: aka: - daressalaamu diff --git a/db/people.yml b/db/people.yml index f36092e4..4a3d0a2c 100644 --- a/db/people.yml +++ b/db/people.yml @@ -239,6 +239,26 @@ alappas: group: bg name: Alexandros Lappas position: professor +alwu: + active: true + aka: [] + avatar: https://github.com/alisnwu.png + bio: bio + education: [] + email: cw3465@columbia.edu + employment: + - begin_date: 2024-10-01 + end_date: 2024-12-31 + organization: columbiau + position: undergraduate researcher + group: bg + advisor: sbillinge + status: undergrad + github_id: alisnwu + grp_mtg_active: true + name: Alison Wu + orcid_id: '' + position: undergraduate researcher amasadeh: active: false aka: [] @@ -357,7 +377,7 @@ ayang: email: ay2546@columbia.edu employment: - begin_date: 2023-06-01 - end_date: 2024-08-31 + end_date: 2024-12-31 organization: columbiau position: undergraduate research assistant group: bg @@ -1678,6 +1698,26 @@ fatassi: group: bg name: Farad Atassi position: unknown +fballerini: + active: true + aka: [] + avatar: https://github.com/filippo1598.png + bio: bio + education: [] + email: filippo.ballerini@unimi.it + employment: + - begin_date: 2024-10-01 + end_date: 2025-05-31 + organization: columbiau + position: visiting student + group: bg + advisor: sbillinge + status: visitor-unsupported + github_id: filippo1598 + grp_mtg_active: true + name: Filippo Ballerini + orcid_id: 0009-0006-7970-7341 + position: visiting student fbertolotti: active: false aka: [] @@ -4269,11 +4309,10 @@ sbillinge: the 2022 Distinguished Powder Diffractionist Prize of the European Powder Diffraction Conference, the 2018 Warren Award of the American Crystallographic Association and being honored in 2011 for contributions to the nation as an immigrant by the - Carnegie Corporation of New York, the 2010 J. D. Hanawalt Award of the International - Center for Diffraction Data, University, Distinguished Faculty award at Michigan - State, and the Thomas H. Osgood Undergraduate Teaching Award. He is Section Editor - of Acta Crystallographica Section A: Advances and Foundations. He regularly chairs - and participates in reviews of major facilities and federally funded programs." + Carnegie Corporation of New York. He is the 2025 laureate of the Gregori Aminoff + prize of the Royal Swedish Academy of Sciences. He is Section Editor of Acta Crystallographica + Section A: Advances and Foundations. He regularly chairs and participates in reviews + of major facilities and federally funded programs." bios: - Professor Simon Billinge is a professor of Materials Science and of Applied Physics and of Applied Mathematics at Columbia University. He teaches materials @@ -4313,18 +4352,17 @@ sbillinge: problem, where the goal is to find an unknown synthesis recipe given a desired material product. These are non trivial ill-posed inverse problems that require novel applied math and computational approaches to solve.\nProf. Billinge has - published more than 300 papers in scholarly journals. He is a fellow of the + published more than 350 papers in scholarly journals. He is a fellow of the American Physical Society and the Neutron Scattering Society of America, a former Fulbright and Sloan fellow and has earned a number of awards including being honored in 2011 for contributions to the nation as an immigrant by the Carnegie Corporation of New York, the 2020 distinguished powder diffraction prize of the European Powder Diffraction Conference, the 2018 Warren award of the American Crystallographic Association, the 2010 J. D. Hanawalt Award of the International - Center for Diffraction Data, the University Distinguished Faculty award at Michigan - State, and the Thomas H. Osgood Undergraduate Teaching Award. He is Section - Editor of Acta Crystalographica Section A: Advances and Foundations. He regularly - chairs and participates in reviews of major facilities and federally funded - programs." + Center for Diffraction Data, He is the 2025 laureate of the Gregori Aminoff + prize of the Royal Swedish Academy of Sciences. He is Section Editor of Acta + Crystalographica Section A: Advances and Foundations. He regularly chairs and + participates in reviews of major facilities and federally funded programs." - I give a brief bio about myself here. I have been carrying research in materials science since 1989 and been a faculty member in Physics and Applied Physics departments since 1994, though my training is in materials science. I have @@ -4362,66 +4400,6 @@ sbillinge: a pair distribution function study ' email: sb2896@columbia.edu employment: - - organization: iucr - begin_year: 2011 - department: aca - not_in_cv: true - position: editor - coworkers: - - aallen - - aaltomare - - lbourgeois - - pdominiak - - jeon - - ugrimm - - dkeen - - wkuhs - - lmarks - - hschenk - - ktsuda - - ivartaniants - - pwillmott - - mnespolo - - organization: iucr - begin_year: 2011 - department: aca - not_in_cv: true - position: editor - coworkers: - - aallen - - aaltomare - - lbourgeois - - pdominiak - - jeon - - ugrimm - - dkeen - - wkuhs - - lmarks - - hschenk - - ktsuda - - ivartaniants - - pwillmott - - mnespolo - - organization: iucr - begin_year: 2011 - department: aca - not_in_cv: true - position: editor - coworkers: - - aallen - - aaltomare - - lbourgeois - - pdominiak - - jeon - - ugrimm - - dkeen - - wkuhs - - lmarks - - hschenk - - ktsuda - - ivartaniants - - pwillmott - - mnespolo - organization: Columbia University begin_year: 2008 group: bg @@ -4570,6 +4548,8 @@ sbillinge: month: 5 day: 12 honors: + - name: Gregori Aminoff prize of the Royal Swedish Academy of Sciences + year: 2025 - name: Flack Lecturer of the Swiss Society for Crystallography year: 2023 - name: Distinguished Powder Diffractionist Prize of the European Powder Diffraction diff --git a/db/presentations.yml b/db/presentations.yml index eeae4d39..49fa9b24 100644 --- a/db/presentations.yml +++ b/db/presentations.yml @@ -5394,33 +5394,6 @@ title: 'ML-Edex-4STEM: Teaching and applying machine learning to physical science students' type: poster -2404sb_rpi: - abstract: Nanoparticles, nanoporous materials and nanostructured bulk materials - are at the heart of next generation technological solutions in sustainable energy, - effective new pharmac euticals and environmental remediation. A key to making - progress is to be able to understand the nanoparticle structure, the arrangements - of atoms in the nanoparticles and nanoscale structures. Also critical is understanding - the distribution of the nanoparticles and how they change in time as devices run - and reactions take place. We use advanced x-ray, neutron and electron scattering - methods to get at this problem. I will talk about these methods and show some - recent success-stories in the fields of sustainable energy, environmental remediation - and cultural heritage preservation. However, I will also discuss the fundamental - limitations on our ability to extract information from the data and how we are - now turning to machine learning and artificial intelligence techniques to give - more insights. - authors: - - sbillinge - begin_date: 2024-04-03 - department: physics - end_date: 2024-04-03 - institution: rensselaerpolytechnicinstitute - notes: [] - project: - - all - status: accepted - title: 'From saving pharmaceuticals to saving priceless historical artefacts via - saving the planet: understanding nanostructure with x-rays and algorithms' - type: colloquium 2404sb_seattle,wa: abstract: "Development of next generation materials for applications in sustainable energy and beyond require us to study the structure of real materials in real @@ -5689,6 +5662,144 @@ data' title: '{AI} at your service' type: invited +2409sb_rpi: + abstract: At the heart of materials science studies for next generation materials + is an idea that we want to be studying real materials doing real things, often + in real devices. A key to making progress is to be able to understand the nanoparticle + structure, the arrangements of atoms in the nanoparticles and nanoscale structures. + Also critical is understanding the distribution of the nanoparticles and how they + change in time as devices run and reactions take place. We use advanced x-ray, + neutron and electron scattering methods to get at this problem. In practice, this + presents a number of key data analysis and interpretation challenges because it + implies we are studying ever more complicated samples, often in complex heterogeneous + environments and in time-resolved operando setups, and we are interrogating our + data for more and more subtle effects such as microstructures and evolving defects + and local structures. We use advanced x-ray, neutron and electron scattering methods + to get at this problem. I will talk about these methods and show some recent + success-stories in the fields of sustainable energy, environmental remediation + and cultural heritage preservation. In particular, we need to study not only + the atomic-scale structure (everything), but how it varies with position in the + device (everywhere), and how it varies with time (all at once). I will also discuss + the fundamental challenges on our ability to extract information from the data + and how we are now turning to machine learning and artificial intelligence techniques + to give more insights. Some of these powerful tools are clearly ready to be applied + more broadly in the community and others are still in the future but look very + promising. They include unsupervised and supervised machine learning approaches, + conventional ML and deep neural networks, including generative models, as well + as approaches for autonomous time-resolved experimentation. + authors: + - sbillinge + begin_date: 2024-09-18 + department: physics + end_date: 2024-09-18 + institution: rpi + notes: [] + project: + - all + status: accepted + title: 'Real materials in action: everything, everywhere, all at once' + type: colloquium +2409sb_universityofliverpool,uk: + abstract: We have recently been thinking, somewhat whimsically, about the materials + genome. The materials genome initiative (MGI) is a US government initiative from + 2011 to speed up, and lower the cost of, the discovery of new advanced materials, + and their deployment in technologies, by applying data analytic methods inspired + by biological genomics. Of course, materials do not have a genome. However, + if we generalize the concept of a genome as a 1D discrete quantity that codes + for the 3-dimensional arrangement of atoms, then we do have quantities that could + serve as this generalized genome. At MACSMIN 2023 I introduced our thinking on + this idea in a general sense. In this talk I will briefly reiterate the main + idea, and then discuss, developments in our thinking over the past year. Two new + concepts emerge from this framing. One is the idea of the materials ribosome. The + ribosome is the cellular machinery that converts a DNA code into protein molecules + which then fold to unique 3D structures. For our materials genome, this would + be machinery, either physical or computational, that can convert our materials + genome code into a 3D structure. The other concept is that of heredity, whereby + we can find new materials similar to existing ones by making small mutations to + our materials genome. I will discuss how this way of thinking can guide us in + our search for genomic codings for materials structure. Something that has emerged + from this thinking is a realization that the field of crystallography doesn't + have clear definitions of what constitutes a structure. I call this the "structure + definition problem" and I suggest that, as a community it would be most beneficial + if we could address this problem and formalize definitions for what is meant by + materials structure. + authors: + - sbillinge + begin_date: 2024-09-09 + end_date: 2024-09-13 + location: University of Liverpool, UK + meeting_name: MACSMIN 2024 + notes: [] + project: + - all + status: accepted + title: Materials genomics, materials heredity and the structure definition problem + type: invited + webinar: true +2410sb_ucsb_matsci: + abstract: At the heart of materials science studies for next generation materials + is an idea that we want to be studying real materials doing real things, often + in real devices. A key to making progress is to be able to understand the nanostructure, + the arrangements of atoms on nanometer lengthscales. Also critical is understanding + the spatial distribution of structures and how they change in time as devices + run and reactions take place. In practice, this presents a number of key data + analysis and interpretation challenges because it implies we are studying ever + more complicated samples, often in complex heterogeneous environments and in time-resolved + operando setups, and we are interrogating our data for more and more subtle effects + such as microstructures and evolving defects and local structures. We use advanced + x-ray, neutron and electron scattering methods to get at this problem. I will + talk about these methods and show some recent success-stories in the fields of + sustainable energy, environmental remediation and cultural heritage preservation. In + particular, we need to study not only the atomic-scale structure (everything), + but how it varies with position in the device (everywhere), and how it varies + with time (all at once). I will also discuss the fundamental challenges on our + ability to extract information from the data and how we are now turning to machine + learning and artificial intelligence techniques to give more insights. + authors: + - sbillinge + begin_date: 2024-10-07 + department: matsci + end_date: 2024-10-08 + institution: ucsb + notes: [] + project: + - all + status: accepted + title: 'Watching real materials in Action: everything, everywhere, all at once' + type: colloquium +2411sb_shivnadarinstitutionofeminence-delhincr,india: + abstract: "Density functional theory has revolutionized our understanding of materials. but can it also lead + us astray when it gets things wrong? We understand that DFT is approximate because of the poorly determined + exchange correlation functional. However, in this talk I would like to bring up another issue that I + think does not get sufficient attention: that DFT is generally carried out on the average structure of a + material. What I mean by average structure is that the inputs to DFT are generally the crystal structure of + the material. The crystal structure is a periodically averaged version of the actual structure. In the + absence of disorder the average structure and the actual structure are the same. However, in the presence + of defects and other non-periodic components to the structure, the actual structure is different from the + average structure, so we are feeding DFT the wrong structure. Does this matter? In other words, if + there are small defects, is the DFT electronic structure a good starting point and we can perturbatively + find the actual behavior? I will argue that actually this is not the case in general, because the + average of the properties is not equal to the properties of the average. This is strictly true even if + the defects are small (e.g., phonons) but in practice is probably not a big deal. However, local + structural measurements using x-ray and neutron total scattering and PDF measurements, often indicate + that materials with interesting properties have quite highly perturbed broken symmetry local structures. + In this case, DFT can yield qualitatively incorrect predictions of properties, as we will discuss. + However, emerging methods that use machine-learned potentials open the door to methods that can address + this issue in practice. I am not an expert in DFT, so this talk is more a call-to-arms and a description + of the problem rather than describing actual solutions." + authors: + - sbillinge + begin_date: 2024-11-10 + end_date: 2024-11-17 + location: Shiv Nadar Institution of Eminence - Delhi NCR, India + meeting_name: Sagamore XX - 2024 + notes: [] + project: + - all + status: accepted + title: 'All density functional theory ({DFT}) is wrong: the average of the properties + $\ne$ the properties of the average' + type: invited 2411sb_tsukuba,japan: abstract: At the heart of materials science studies for next generation materials is an idea that we want to be studying real materials doing real things, often @@ -5751,54 +5862,3 @@ status: accepted title: Structure-property studies of nano-materials using synchrotron radiation type: invited -2409sb_krakow,poland: - abstract: tbd - authors: - - sbillinge - begin_date: 2024-09-01 - end_date: 2024-09-08 - location: Krakow, Poland - meeting_name: RAC International Summer School 2024 - notes: [] - project: - - all - status: accepted - title: tbd - type: invited -2409sb_universityofliverpool,uk: - abstract: We have recently been thinking, somewhat whimsically, about the materials - genome. The materials genome initiative (MGI) is a US government initiative from - 2011 to speed up, and lower the cost of, the discovery of new advanced materials, - and their deployment in technologies, by applying data analytic methods inspired - by biological genomics. Of course, materials do not have a genome. However, - if we generalize the concept of a genome as a 1D discrete quantity that codes - for the 3-dimensional arrangement of atoms, then we do have quantities that could - serve as this generalized genome. At MACSMIN 2023 I introduced our thinking on - this idea in a general sense. In this talk I will briefly reiterate the main - idea, and then discuss, developments in our thinking over the past year. Two new - concepts emerge from this framing. One is the idea of the materials ribosome. The - ribosome is the cellular machinery that converts a DNA code into protein molecules - which then fold to unique 3D structures. For our materials genome, this would - be machinery, either physical or computational, that can convert our materials - genome code into a 3D structure. The other concept is that of heredity, whereby - we can find new materials similar to existing ones by making small mutations to - our materials genome. I will discuss how this way of thinking can guide us in - our search for genomic codings for materials structure. Something that has emerged - from this thinking is a realization that the field of crystallography doesn't - have clear definitions of what constitutes a structure. I call this the "structure - definition problem" and I suggest that, as a community it would be most beneficial - if we could address this problem and formalize definitions for what is meant by - materials structure. - authors: - - sbillinge - begin_date: 2024-09-09 - end_date: 2024-09-13 - location: University of Liverpool, UK - meeting_name: MACSMIN 2024 - notes: [] - project: - - all - status: accepted - title: Materials genomics, materials heredity and the structure definition problem - type: invited - webinar: true