Future Forum 2013 - Stefano Baroni

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Future Forum 2013 - Stefano Baroni

  1. 1. i  sapori  dell’arcobaleno vedere  colori  e  colorare  il  cibo  con  il  computer Stefano  Baroni Scuola  Internazionale  Superiore  di  Studi  Avanza9 Trieste breve  conferenza  tenuta  al  Friuli  Future  Forum,  Udine,  27  novembre  2013
  2. 2. il  sapore  dell’arcobaleno
  3. 3. il  sapore  dell’arcobaleno ☛ mercato  globale  di  1.45  miliardi  di  dollari  nel  2009
  4. 4. il  sapore  dell’arcobaleno ☛ mercato  globale  di  1.45  miliardi  di  dollari  nel  2009 ☛ per  un  totale  di  50,000  tonnellate  /  anno
  5. 5. il  sapore  dell’arcobaleno ☛ mercato  globale  di  1.45  miliardi  di  dollari  nel  2009 ☛ per  un  totale  di  50,000  tonnellate  /  anno bibite 28% alcolici 5% cibo 67%
  6. 6. il  sapore  dell’arcobaleno ☛ mercato  globale  di  1.45  miliardi  di  dollari  nel  2009 ☛ per  un  totale  di  50,000  tonnellate  /  anno bibite 28% alcolici 5% cibo 67% Altri 18% Cina 8% Giappone 10% Europa 36% USA 28%
  7. 7. il  sapore  dell’arcobaleno ☛ mercato  globale  di  1.45  miliardi  di  dollari  nel  2009 ☛ per  un  totale  di  50,000  tonnellate  /  anno bibite 28% alcolici 5% cibo 67% ☛ Altri 18% Cina 8% Giappone 10% Europa 36% USA 28% mercato  dei  coloran9  naturali  cresciuto  del  35%  nel  quinquennio  2005-­‐2009
  8. 8. “To help businesses discover, develop, and deploy new materials twice as fast, we’re launching what we call the Materials Genome Initiative. The invention of silicon circuits and lithium ion batteries made computers and iPods and iPads possible, but it took years to get those technologies from the drawing board to the market place. We can do it faster.” -President Obama (6/11)
  9. 9. “To help businesses discover, develop, and deploy new materials twice as fast, we’re launching what we call the Materials Genome Initiative. The invention of silicon circuits and lithium ion batteries made computers and iPods and iPads possible, but it took years to get those$  100  M  requested  drawing board technologies from the in  2012   to the market place. We can do it faster.” -President Obama (6/11)
  10. 10. “To help businesses discover, develop, and deploy new materials twice as fast, we’re launching what we call the Materials Genome Initiative. The invention of silicon circuits and lithium ion batteries made computers and iPods and iPads possible, but it took years to get those technologies from the drawing board to the market place. We can do it faster.” -President Obama (6/11)
  11. 11. The high-throughput highway to computa materials design REVIEW ARTICLE PUBLISHED ONLINE: 20 FEBRUARY 2013 | DOI: 10.1038/NMAT3568 Stefano Curtarolo1,2*, Gus L. W. Hart2,3, Marco Buongiorno Nardelli2,4,5, Natalio Ming Stefano Sanvito2,7 and Ohad Levy1,2,8 The high-throughput highway todesign is an emerging area of materials science. By combi computational High-throughput computational materials dynamic and electronic-structure methods with intelligent data mining and database construction, and materials design current supercomputer architectures, scientists generate, manage and analyse enormous data reposi Stefano Curtarolo1,2*, Gus L. W. Hart2,3,In this Review we Nardelli2,4,5current snapshot of this rapidly evolving field, and highl of novel materials. Marco Buongiorno provide a , Natalio Mingo2,6, Stefano Sanvito2,7opportunities that lie ahead. and Ohad Levy1,2,8 E High-throughput computational materials design is an emerging area of materials science. By combining advanced thermovery methods with is intimately related database construction, and exploiting The HT of dynamic and electronic-structure technology intelligent data mining andto a particular materials the power experimental approach was p current supercomputer architectures,steam engines that powered the industrialdata repositories for the discovery Edison4 and Ciamician5, bu set. The scientists generate, manage and analyse enormous revolution years ago by of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and in the eighteenth century were made of steel and, information cient and accurate theoretical tools and in opportunities that lie ahead. and communication technologies are underpinned by silicon. Once computational counterpart has become a a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade c very technology is intimately related to a particular materials The HT experimental approach was pioneered over a hundred because of the investments associated with Edison4 and Ciamician5, but with the advent of effi- emerged3,6–16 following the als research has set. The steam engines that powered the industrial revolution years ago by establishing large-scale in the eighteenth century were made of steelThis information cient and accurate theoretical tools and inexpensive computers, its 17–19. In the literature, HT m production lines. and, means that changing the materials set in HT approaches and communication technologies are underpinned by silicon. Once computational counterpart has become a viable path for tackling an established technology is a rare event and must be considered confused with the combinatorial evaluatio a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade computational HT materias associated with establishing large-scale als research has emerged3,6–16 following the impetus of experimental attempts have been made Although a few because of the investments a revolution. Moreover, the initial choice of a material is absoproduction lines. This lutely that changing the materials set in success of a17–19. In the literature,sector. means crucial for the long-lasting HT approaches technological HT materials research is20–22, the distinction is not yet rigo concepts often an established technology is a rare event and must be considered confused with the combinatorial evaluation of materials properties. Importantly, recent times have Although a few attempts technological as define the two as a revolution. Moreover, the initial choice of a material is abso- seen a surge of new have been made to clearly the throughput of data that is way too hi niches, each of a of them sector. concepts20–22, the distinction is not yet rigorous. lysed by the researcher’s direct interventio lutely crucial for the long-lasting success one technologicalpotentially looking for a different mate- Here we define HT Importantly, recent times have seen a surgethenew technological the the throughput of data that is way too high to be produced or anarials set. Thus, of pressure on as development of new materials performed automatically: HT implies an a niches, each one of them potentially looking for a different mate- lysed by the researcher’s direct intervention, and must therefore be is becoming formidable. materials performed automatically: counts. They to results. The rials set. Thus, the pressure on the development of new These should score on manyHT implies an automatic flow from ideas confusion of HT with com is becoming formidable. These shouldtailored on counts. They to results. Thethat the of HT with combinatorial approaches is The latter, in fact, specifies should be score on many the specific property confusion technology is thus resolved. should be tailored on the specific property often should be is thus resolved. The otherin fact, specifies how the degrees of freebased on, they that the technology compatible with latter, technologies, dom are investigated, whereas HT strictly based on, they often should be compatible with other technologies, dom are investigated, whereas HT strictly defines the overwhelming should not contain in large quanti- and automatic flow of in large quanti- and automatic flow of the investigations. should not contain toxic elements, and, if needed toxic elements, and, if needed the investigations. ties, should be made of ties, should be made of cheap for materials. Asimplementation of computational Theis practical implementation of com cheap raw materials. As such, searching raw The practical such, searching for HT highly materials is a multi-dimensional problem where many boxes should non-trivial. The method is employed in three strictly connected materials is a multi-dimensional problem virtual materials boxes should non-trivial. The method is employed in where many growth: thermodynamic and electronic be ticked at the same time. steps: (i) be ticked at the same time. Although the demand for materials is endlessly growing, experi- structure calculations of materials3,23; (ii) rationalsteps: (i) virtual materials growth: therm materials storage: mental discovery is bound by high costs and time-consuming systematic storage of growing, experithe information in database repositories24,25; Although the demand for materials is endlessly structure calculations of materials3,23; (ii) r procedures of synthesis. Is there another way? Indeed, this is the (iii) materials characterization and selection: data analysis aimed at mental materials science called ‘high- selecting novel materials or gaining new physical systematic storage of the information in burgeoning area of computationaldiscovery is bound by high costs and time-consuming insights15,19,26. procedures of design. It is Is there High-throughput is often this for the throughput’ (HT) computational materialssynthesis. based on another way? Indeed,known is thelarge(iii) materials characterization and selecti databases it genthe marriage between burgeoning quantum-mechanical–ther- erates (for example, the AFLOWLIB.org consortium24 and the materials or gaining new p computational area of computational materials science called ‘highselecting novel 1,2 25 E modynamic approaches and a multitude of techniques rooted in Materials Project ). Here we posit that all three HT stages are highly
  12. 12. REVIEW ARTICLE PUBLISHED ONLINE: 20 FEBRUARY 2013 | DOI: 10.1038/NMAT3568 The high-throughput highway to computational materials design Stefano Curtarolo1,2*, Gus L. W. Hart2,3, Marco Buongiorno Nardelli2,4,5, Natalio Mingo2,6, Stefano Sanvito2,7 and Ohad Levy1,2,8 E High-throughput computational materials design is an emerging area of materials science. By combining advanced thermovery methods with is intimately related database construction, and exploiting the power of dynamic and electronic-structure technology intelligent data mining andto a particular materials current supercomputer architectures,steam engines that powered the industrialdata repositories for the discovery set. The scientists generate, manage and analyse enormous revolution of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and in the eighteenth century were made of steel and, information opportunities that lie ahead. and communication technologies are underpinned by silicon. Once a material is chosen for a given technology, it gets locked with it very technology is intimately related to a particular materials The HT experimental approach was pioneered over a hundred because of the investments associated with Edison4 and Ciamician5, but with the advent of effiset. The steam engines that powered the industrial revolution years ago by establishing large-scale in the eighteenth century were made of steelThis information cient and accurate theoretical tools and inexpensive computers, its production lines. and, means that changing the materials set in and communication technologies are underpinned by silicon. Once computational counterpart has become a viable path for tackling an established technology is a rare event and must be considered a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade computational HT materias associated with establishing large-scale als research has emerged3,6–16 following the impetus of experimental because of the investments a revolution. Moreover, the initial choice of a material is absoproduction lines. This lutely that changing the materials set in success of a17–19. In the literature,sector. means crucial for the long-lasting HT approaches technological HT materials research is often an established technology is a rare event and must be considered confused with the combinatorial evaluation of materials properties. Importantly, recent times have Although a few attempts technological as a revolution. Moreover, the initial choice of a material is abso- seen a surge of new have been made to clearly define the two niches, each of a of them sector. concepts20–22, the distinction is not yet rigorous. lutely crucial for the long-lasting success one technologicalpotentially looking for a different mate- Here we define HT Importantly, recent times have seen a surgethenew technological the the throughput of data that is way too high to be produced or anarials set. Thus, of pressure on as development of new materials niches, each one of them potentially looking for a different mate- lysed by the researcher’s direct intervention, and must therefore be is becoming formidable. materials performed automatically: counts. They rials set. Thus, the pressure on the development of new These should score on manyHT implies an automatic flow from ideas is becoming formidable. These shouldtailored on counts. They to results. Thethat the of HT with combinatorial approaches is should be score on many the specific property confusion technology is should be tailored on the specific property often should be is thus resolved. The otherin fact, specifies how the degrees of freebased on, they that the technology compatible with latter, technologies, based on, they often should be compatible with other technologies, dom are investigated, whereas HT strictly defines the overwhelming should not contain in large quanti- and automatic flow of in large quantishould not contain toxic elements, and, if needed toxic elements, and, if needed the investigations. ties, should be made of ties, should be made of cheap for materials. Asimplementation of computational HT is highly cheap raw materials. As such, searching raw The practical such, searching for materials is a multi-dimensional problem where many boxes should non-trivial. The method is employed in three strictly connected materials is a multi-dimensional problem virtual materials boxes should where many growth: thermodynamic and electronic be ticked at the same time. steps: (i) be ticked at the same time. Although the demand for materials is endlessly growing, experi- structure calculations of materials3,23; (ii) rational materials storage: mental discovery is bound by high costs and time-consuming systematic storage of growing, experiAlthough the demand for materials is endlessly the information in database repositories24,25; E procedures of synthesis. Is there another way? Indeed, this is the burgeoning area of computational materials science called ‘highthroughput’ (HT) computational materials design. It is based on the marriage between computational quantum-mechanical–thermodynamic approaches1,2 and a multitude of techniques rooted in (iii) materials characterization and selection: data analysis aimed at selecting novel materials or gaining new physical insights15,19,26. High-throughput is often known for the large databases it generates (for example, the AFLOWLIB.org consortium24 and the Materials Project25). Here we posit that all three HT stages are highly
  13. 13. REVIEW ARTICLE PUBLISHED ONLINE: 20 FEBRUARY 2013 | DOI: 10.1038/NMAT3568 The high-throughput highway to computational materials design Stefano Curtarolo1,2*, Gus L. W. Hart2,3, Marco Buongiorno Nardelli2,4,5, Natalio Mingo2,6, Stefano Sanvito2,7 and Ohad Levy1,2,8 E High-throughput computational materials design is an emerging area of materials science. By combining advanced thermovery methods with is intimately related database construction, and exploiting the power of dynamic and electronic-structure technology intelligent data mining andto a particular materials current supercomputer architectures,steam engines that powered the industrialdata repositories for the discovery set. The scientists generate, manage and analyse enormous revolution of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and in the eighteenth century were made of steel and, information opportunities that lie ahead. and communication technologies are underpinned by silicon. Once a material is chosen for a given technology, it gets locked withpioneered over a hundred it very technology is intimately related to a particular materials The HT experimental approach was because of the investments associated ago by Edison4 and Ciamician5, but with the advent of effiset. The steam engines that powered the industrial revolution years with establishing large-scale in the eighteenth century were made of steelThis information cient and accurate theoretical tools and inexpensive computers, its production lines. and, means that changing the materials set in and communication technologies are underpinned by silicon. Once computational counterpart has become a viable path for tackling an established technology is a rare event and must be considered a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade computational HT materias associated with establishing large-scale als research has emerged3,6–16 following the impetus of experimental because of the investments a revolution. Moreover, the initial choice of a material is absoproduction lines. This lutely that changing the materials set in success of a17–19. In the literature, HT materials research is often means crucial for the long-lasting HT approaches technological sector. an established technology is a rare event and must be considered confused with the combinatorial evaluation of materials properties. Importantly, recent times have Although a few attempts technological as a revolution. Moreover, the initial choice of a material is abso- seen a surge of new have been made to clearly define the two niches, each of a of them sector. concepts20–22, the distinction is not matelutely crucial for the long-lasting success one technologicalpotentially looking for a differentyet rigorous. Here we define HT Importantly, recent times have seenThus, thenew technological the the throughput of data new way too high to be produced or anarials set. a surge of pressure on as development of that is materials niches, each one of them potentially looking for a different mate- lysed by the researcher’s direct intervention, and must therefore be is becoming formidable. materials performed automatically: counts. an automatic flow from ideas rials set. Thus, the pressure on the development of new These should score on manyHT implies They should be score on many the specific property that the of HT with combinatorial approaches is is becoming formidable. These shouldtailored on counts. They to results. The confusion technology is should be tailored on the specific property often should be is thus resolved. Theotherin fact, specifies how the degrees of freebased on, they that the technology compatible with latter, technologies, based on, they often should be compatible with other technologies, dom are investigated, whereas HT strictly defines the overwhelming should not contain in large quanti- and automatic flow of in large quantishould not contain toxic elements, and, if needed toxic elements, and, if needed the investigations. ties, should be made of ties, should be made of cheap for materials. Asimplementation of computational HT is highly cheap raw materials. As such, searching raw The practical such, searching for materials is a multi-dimensional problem where many boxes should problem where manyis employed in three strictly connected method materials is a multi-dimensional non-trivial. The materials boxes should be ticked at the same time. steps: (i) virtual growth: thermodynamic and electronic be ticked at the same time. Although the demand for materials is endlessly growing, experi- structure calculations of materials3,23; (ii) rational materials storage: mental discovery is bound by high costs and time-consuming systematic storage of growing, experiAlthough the demand for materials is endlessly the information in database repositories24,25; E procedures of synthesis. Is there another way? Indeed, this is the burgeoning area of computational materials science called ‘highthroughput’ (HT) computational materials design. It is based on the marriage between computational quantum-mechanical–thermodynamic approaches1,2 and a multitude of techniques rooted in (iii) materials characterization and selection: data analysis aimed at selecting novel materials or gaining new physical insights15,19,26. High-throughput is often known for the large databases it generates (for example, the AFLOWLIB.org consortium24 and the Materials Project25). Here we posit that all three HT stages are highly
  14. 14. REVIEW ARTICLE PUBLISHED ONLINE: 20 FEBRUARY 2013 | DOI: 10.1038/NMAT3568 The high-throughput highway to computational materials design Stefano Curtarolo1,2*, Gus L. W. Hart2,3, Marco Buongiorno Nardelli2,4,5, Natalio Mingo2,6, Stefano Sanvito2,7 and Ohad Levy1,2,8 E High-throughput computational materials design is an emerging area of materials science. By combining advanced thermovery methods with is intimately related database construction, and exploiting the power of dynamic and electronic-structure technology intelligent data mining andto a particular materials current supercomputer architectures,steam engines that powered the industrialdata repositories for the discovery set. The scientists generate, manage and analyse enormous revolution of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and in the eighteenth century were made of steel and, information opportunities that lie ahead. and communication technologies are underpinned by silicon. Once a material is chosen for a given technology, it gets locked withpioneered over a hundred it very technology is intimately related to a particular materials The HT experimental approach was because of the investments associated ago by Edison4 and Ciamician5, but with the advent of effiset. The steam engines that powered the industrial revolution years with establishing large-scale in the eighteenth century were made of steelThis information cient and accurate theoretical tools and inexpensive computers, its production lines. and, means that changing the materials set in and communication technologies are underpinned by silicon. Once computational counterpart has become a viable path for tackling an established technology is a rare event and must be considered a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade computational HT materias associated with establishing large-scale als research has emerged3,6–16 following the impetus of experimental because of the investments a revolution. Moreover, the initial choice of a material is absoproduction lines. This lutely that changing the materials set in success of a17–19. In the literature, HT materials research is often means crucial for the long-lasting HT approaches technological sector. an established technology is a rare event and must be considered confused with the combinatorial evaluation of materials properties. Importantly, recent times have Although a few attempts technological as a revolution. Moreover, the initial choice of a material is abso- seen a surge of new have been made to clearly define the two niches, each of a of them sector. concepts20–22, the distinction is not matelutely crucial for the long-lasting success one technologicalpotentially looking for a differentyet rigorous. Here we define HT Importantly, recent times have seenThus, thenew technological the the throughput of data new way too high to be produced or anarials set. a surge of pressure on as development of that is materials niches, each one of them potentially looking for a different mate- lysed by the researcher’s direct intervention, and must therefore be is becoming formidable. materials performed automatically: counts. an automatic flow from ideas rials set. Thus, the pressure on the development of new These should score on manyHT implies They should be score on many the specific property that the of HT with combinatorial approaches is is becoming formidable. These shouldtailored on counts. They to results. The confusion technology is should be tailored on the specific property often should be is thus resolved. Theotherin fact, specifies how the degrees of freebased on, they that the technology compatible with latter, technologies, based on, they often should be compatible with other technologies, dom are investigated, whereas HT strictly defines the overwhelming should not contain in large quanti- and automatic flow of in large quantishould not contain toxic elements, and, if needed toxic elements, and, if needed the investigations. ties, should be made of ties, should be made of cheap for materials. Asimplementation of computational HT is highly cheap raw materials. As such, searching raw The practical such, searching for materials is a multi-dimensional problem where many boxes should problem where manyis employed in three strictly connected method materials is a multi-dimensional non-trivial. The materials boxes should be ticked at the same time. steps: (i) virtual growth: thermodynamic and electronic be ticked at the same time. Although the demand for materials is endlessly growing, experi- structure calculations of materials3,23; (ii) rational materials storage: mental discovery is bound by high costs and time-consuming systematic storage of growing, experiAlthough the demand for materials is endlessly the information in database repositories24,25; E procedures of synthesis. Is there another way? Indeed, this is the burgeoning area of computational materials science called ‘highthroughput’ (HT) computational materials design. It is based on the marriage between computational quantum-mechanical–thermodynamic approaches1,2 and a multitude of techniques rooted in (iii) materials characterization and selection: data analysis aimed at selecting novel materials or gaining new physical insights15,19,26. High-throughput is often known for the large databases it generates (for example, the AFLOWLIB.org consortium24 and the Materials Project25). Here we posit that all three HT stages are highly
  15. 15. REVIEW ARTICLE PUBLISHED ONLINE: 20 FEBRUARY 2013 | DOI: 10.1038/NMAT3568 The high-throughput highway to computational materials design Stefano Curtarolo1,2*, Gus L. W. Hart2,3, Marco Buongiorno Nardelli2,4,5, Natalio Mingo2,6, Stefano Sanvito2,7 and Ohad Levy1,2,8 E High-throughput computational materials design is an emerging area of materials science. By combining advanced thermovery methods with is intimately related database construction, and exploiting the power of dynamic and electronic-structure technology intelligent data mining andto a particular materials current supercomputer architectures,steam engines that powered the industrialdata repositories for the discovery set. The scientists generate, manage and analyse enormous revolution of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and in the eighteenth century were made of steel and, information opportunities that lie ahead. and communication technologies are underpinned by silicon. Once a material is chosen for a given technology, it gets locked withpioneered over a hundred it very technology is intimately related to a particular materials The HT experimental approach was because of the investments associated ago by Edison4 and Ciamician5, but with the advent of effiset. The steam engines that powered the industrial revolution years with establishing large-scale in the eighteenth century were made of steelThis information cient and accurate theoretical tools and inexpensive computers, its production lines. and, means that changing the materials set in and communication technologies are underpinned by silicon. Once computational counterpart has become a viable path for tackling an established technology is a rare event and must be considered a material is chosen for a given technology, it gets locked with it materials design. Thus, in the past decade computational HT materias associated with establishing large-scale als research has emerged3,6–16 following the impetus of experimental because of the investments a revolution. Moreover, the initial choice of a material is absoproduction lines. This lutely that changing the materials set in success of a17–19. In the literature, HT materials research is often means crucial for the long-lasting HT approaches technological sector. an established technology is a rare event and must be considered confused with the combinatorial evaluation of materials properties. Importantly, recent times have Although a few attempts technological as a revolution. Moreover, the initial choice of a material is abso- seen a surge of new have been made to clearly define the two niches, each of a of them sector. concepts20–22, the distinction is not matelutely crucial for the long-lasting success one technologicalpotentially looking for a differentyet rigorous. Here we define HT Importantly, recent times have seenThus, thenew technological the the throughput of data new way too high to be produced or anarials set. a surge of pressure on as development of that is materials niches, each one of them potentially looking for a different mate- lysed by the researcher’s direct intervention, and must therefore be is becoming formidable. materials performed automatically: counts. an automatic flow from ideas rials set. Thus, the pressure on the development of new These should score on manyHT implies They should be score on many the specific property that the of HT with combinatorial approaches is is becoming formidable. These shouldtailored on counts. They to results. The confusion technology is should be tailored on the specific property often should be is thus resolved. Theotherin fact, specifies how the degrees of freebased on, they that the technology compatible with latter, technologies, based on, they often should be compatible with other technologies, dom are investigated, whereas HT strictly defines the overwhelming should not contain in large quanti- and automatic flow of in large quantishould not contain toxic elements, and, if needed toxic elements, and, if needed the investigations. ties, should be made of ties, should be made of cheap for materials. Asimplementation of computational HT is highly cheap raw materials. As such, searching raw The practical such, searching for materials is a multi-dimensional problem where many boxes should problem where manyis employed in three strictly connected method materials is a multi-dimensional non-trivial. The materials boxes should be ticked at the same time. steps: (i) virtual growth: thermodynamic and electronic be ticked at the same time. Although the demand for materials is endlessly growing, experi- structure calculations of materials3,23; (ii) rational materials storage: mental discovery is bound by high costs and time-consuming systematic storage of growing, experiAlthough the demand for materials is endlessly the information in database repositories24,25; E procedures of synthesis. Is there another way? Indeed, this is the burgeoning area of computational materials science called ‘highthroughput’ (HT) computational materials design. It is based on the marriage between computational quantum-mechanical–thermodynamic approaches1,2 and a multitude of techniques rooted in (iii) materials characterization and selection: data analysis aimed at selecting novel materials or gaining new physical insights15,19,26. High-throughput is often known for the large databases it generates (for example, the AFLOWLIB.org consortium24 and the Materials Project25). Here we posit that all three HT stages are highly
  16. 16. coloran9  alimentari
  17. 17. coloran9  alimentari tecnologia  di  nicchia
  18. 18. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore
  19. 19. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore ✔ valore  di  mercato:  naturalezza
  20. 20. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore ✔ valore  di  mercato:  naturalezza ✔ valore  finanziario:  abbondanza  (naturale)
  21. 21. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore ✔ valore  di  mercato:  naturalezza ✔ valore  finanziario:  abbondanza  (naturale) ✔ valore  tecnico:  flessibilità  funzionale
  22. 22. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore ✔ valore  di  mercato:  naturalezza ✔ valore  finanziario:  abbondanza  (naturale) ✔ valore  tecnico:  flessibilità  funzionale =            antocianine
  23. 23. coloran9  alimentari tecnologia  di  nicchia proprietà  specifica:  il  colore ✔ valore  di  mercato:  naturalezza ✔ valore  finanziario:  abbondanza  (naturale) ✔ valore  tecnico:  flessibilità  funzionale =            antocianine
  24. 24. anthocyanins chromenylium phenyl sugar
  25. 25. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  26. 26. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  27. 27. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  28. 28. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  29. 29. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  30. 30. anthocyanins chromenylium phenyl sugar anthocyanin R1 R2 R3 R7 cyanin −OH −OH −H −OH peonin −OCH3 −OH −H −OH rosinin −OH −OH −H −OCH3 malvin −OCH3 −OH −OCH3 −OH delphinin −OH −OH −OCH3 −OH pelargonin −H −OH −OH −OH antho-­‐0 −H −H −H −OH
  31. 31. anthocyanins:  the  role  of  acidity 1 pH 13 ous med 1.1. ANTHOCYANINS Figure 1.4: The main four equilibrium forms of anthocyanin existing in aque [31]. m for Figure 1.5: The distribution of the di↵erent Malvindin-3-glucoside equilibriu
  32. 32. anthocyanins:  the  role  of  hydroxyla9on
  33. 33. anthocyanins:  the  role  of  copigmenta9on
  34. 34. anthocyanins:  the  hurdles  towards  a  ra9onal  design
  35. 35. anthocyanins:  the  hurdles  towards  a  ra9onal  design the  stability  and  color  func9on  of  anthocyanins  are  affected  by  many   and  diverse  factors: structural  diversity  (phenols,  sugars,  and  acyla9on)   pH  sensi9vity co-­‐pigmenta9on  
  36. 36. anthocyanins:  the  hurdles  towards  a  ra9onal  design the  stability  and  color  func9on  of  anthocyanins  are  affected  by  many   and  diverse  factors: structural  diversity  (phenols,  sugars,  and  acyla9on)   pH  sensi9vity co-­‐pigmenta9on   the  high  reac9vity  of  the  (phenolic)  chromophore  makes  synthesis   extremely  difficult most  of  research  simply  aims  at  isola9ng  from  natural  sources   (highly  expensive  and  difficult) very  liale  research  is  being  done  in  this  area
  37. 37. anthocyanins:  the  hurdles  towards  a  ra9onal  design the  stability  and  color  func9on  of  anthocyanins  are  affected  by  many   and  diverse  factors: structural  diversity  (phenols,  sugars,  and  acyla9on)   pH  sensi9vity co-­‐pigmenta9on   the  high  reac9vity  of  the  (phenolic)  chromophore  makes  synthesis   extremely  difficult most  of  research  simply  aims  at  isola9ng  from  natural  sources   (highly  expensive  and  difficult) very  liale  research  is  being  done  in  this  area very  liale  is  known  on  the  microscopic  mechanisms  that  determine  the   stability  and  the  chroma9c  proper9es  of  anthocyanins  and  the  rela9on   between  structure  an  color
  38. 38. molecular  and  materials  modeling back  in  the  fibies
  39. 39. molecular  and  materials  modeling back  in  the  fibies 1962
  40. 40. molecular  and  materials  modeling third  millennium back  in  the  fibies 1962
  41. 41. molecular  modeling third   Michael  Levia millennium 2013 "for  the  development  of   mul9scale  [computa9onal] back  iomplex   models  for  cn  the  fibies chemical  systems" 1962 Mar9n  Karplus Arieh  Warshel
  42. 42. what  color  is  all  about ?
  43. 43. what  color  is  all  about
  44. 44. what  color  is  all  about
  45. 45. what  color  is  all  about
  46. 46. what  color  is  all  about 450 550 anycolor(λ)  =  r(λ)  +  g(λ)  +  b(λ) 650
  47. 47. reflec9on  vs.  transmission
  48. 48. reflec9on  vs.  transmission
  49. 49. brings a lot of commercial value to the research of this phenomenon [33]. An example of the copigmentation in nature is the bluish purple flowers of the Japanese garden iris [34]. Even though fundamental for the color expression, we will not consider explicitly copigmentation in the rest of this thesis. reflec9on  vs.  transmission 1.2 Simulating molecular colors When a beam of light impinges on the surface of a material, several di↵erent processes occur as illustrated in Fig. (1.6). Part of the light is directly reflected by the surface, while the rest is transmitted into the material. The amount of reflected light depends on the refractive index of the material, the smoothness of the surface and the incidence angle ✓. This process gives rise to the so-called surface gloss [35]. The surface gloss under a white light source is usually also white, despite the fact that the material itself may have other colors. However materials with a strong optical dispersion (i.e. with a refractive index that strongly depends on the wavelength) display a colored gloss, such as metals.
  50. 50. what  makes  things  gliaer  the  way  they  do s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity
  51. 51. what  makes  things  gliaer  the  way  they  do s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity
  52. 52. what  makes  things  gliaer  the  way  they  do -./01234( !"#$ 560.7(589:47;+ ! ! ! !"# $"# ""# '()*+, ! ! %"# &"# s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity S( )
  53. 53. what  makes  things  gliaer  the  way  they  do -./01234( !"#$ 560.7(589:47;+ light ! ! ! !"# $"# ""# '()*+, ! ! %"# absorbing  medium &"# T(x, ) s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity S( ) ⇥ e (⇥)x x
  54. 54. what  makes  things  gliaer  the  way  they  do -./01234( !"#$ 560.7(589:47;+ ! ! ! !"# $"# ""# '()*+, ! ! %"# &"# s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity S( ) ⇥ e κ(λ) (⇥)x 400 500 600 λ[nm] 700
  55. 55. what  makes  things  gliaer  the  way  they  do b !"#$ 560.7(589:47;+ ! ! ! !"# $"# ""# '()*+, r rgb -./01234( g ! ! %"# &"# λ[nm] s9mulus  =   illuminant  ×  trasmission  ×  sensi9vity S( ) ⇥ e (⇥)x ⇥ rgb( ) κ(λ) 400 500 600 λ[nm] 700
  56. 56. what  makes  things  gliaer  the  way  they  do b !"#$ 560.7(589:47;+ ! ! !"# $"# ! ! ""# '()*+, r rgb -./01234( g ! %"# &"# λ[nm] RGB(x) = Z κ(λ) S( )e (⇥)x rgb( )d 400 500 600 λ[nm] 700
  57. 57. a  puzzle  for  you
  58. 58. a  puzzle  for  you hint:  the  answer  is  contained  in  one  of  the  previous  slides  
  59. 59. what  computer  modeling   is  all  about
  60. 60. the  saga  of  9me  and  length  scales length [m] 10-3 macro scale =0 10-6 nano scale 10-9 =1 10-15 10-12 time [s] 10-9 10-6 10-3
  61. 61. the  saga  of  9me  and  length  scales length [m] 10-3 macro scale =0 hic sunt leones 10-6 nano scale 10-9 =1 10-15 10-12 time [s] 10-9 10-6 10-3
  62. 62. the  saga  of  9me  and  length  scales length [m] classical (electro-) dynamics, macro scale thermodynamics & finite elements =0 10-3 kinetic Monte Carlo hic sunt leones classical molecular 10-6 dynamics 10-9 electronic structure nano scale methods =1 10-15 10-12 time [s] 10-9 10-6 10-3
  63. 63. size  vs.  accuracy  of  atomis9c  modeling size classical empirical methods ☛ pair potentials ☛ force fields ☛ shell models quantum empirical methods ☛ tight-binding ☛ embedded atom quantum self-consistent methods ☛ density Functional Theory ☛ Hartree-Fock quantum many-body methods ☛ quantum Monte Carlo ☛ MP2, CCSD(T), CI ☛ GW, BSE accuracy
  64. 64. size  vs.  accuracy  of  atomis9c  modeling size classical empirical methods ☛ pair potentials ☛ force fields ☛ shell models quantum empirical methods ☛ tight-binding ☛ embedded atom quantum self-consistent methods ☛ density Functional Theory ☛ Hartree-Fock quantum many-body methods ☛ quantum Monte Carlo ☛ MP2, CCSD(T), CI ☛ GW, BSE accuracy
  65. 65. ab  ini9o  simula9ons ⇥ (r, R; t) i = ⇥t 2 2 ⇥ 2M ⇥R2 2 2 ⇥ 2m ⇥r2 ⇥ + V (r, R) (r, R; t)
  66. 66. ab  ini9o  simula9ons ⇥ (r, R; t) i = ⇥t 2 2 ⇥ 2M ⇥R2 2 2 ⇥ 2m ⇥r2 ⇥ + V (r, R) (r, R; t) M m:  the  Born-­‐Oppenheimer  approxima9on   2 ⇥2 2m ⇥r2 ⇥E(R) ¨ MR = ⇥R ⇥ + V (r, R) (r|R) = E(R) (r|R)
  67. 67. from  chemistry  to  color pelargonin C21H21O10
  68. 68. from  chemistry  to  color pelargonin C21H21O10
  69. 69. from  chemistry  to  color pelargonin C21H21O10
  70. 70. pelargonin C21H21O10 HOMO-­‐1 3.2. EFFECTS OF SIDE GROUPS L 0 sugar phenyl chromenylium -1.5 A A -2 energy KS orbital energy (ev) from  chemistry  to  color HOMO-­‐1 A B C B B C -2.5 C pelargonin peonin malvin
  71. 71. pelargonin C21H21O10 HOMO-­‐1 LUMO 3.2. EFFECTS OF SIDE GROUPS LUMO L 0 sugar phenyl chromenylium -1.5 A A -2 energy KS orbital energy (ev) from  chemistry  to  color HOMO-­‐1 A B C B B C -2.5 C pelargonin peonin malvin
  72. 72. pelargonin C21H21O10 HOMO-­‐1 HOMO-­‐4 LUMO 3.2. EFFECTS OF SIDE GROUPS LUMO L 0 sugar phenyl chromenylium -1.5 A A -2 energy KS orbital energy (ev) from  chemistry  to  color HOMO-­‐1 A B C B HOMO-­‐4 B C -2.5 C pelargonin peonin malvin
  73. 73. pelargonin C21H21O10 HOMO-­‐1 HOMO-­‐4 LUMO 3.2. EFFECTS OF SIDE GROUPS LUMO L 0 sugar phenyl chromenylium -1.5 A A HOMO-­‐1 A B C Energy (ev) 3 C -2.5 2 B HOMO-­‐4 B 2.5 Absorption -2 energy KS orbital energy (ev) from  chemistry  to  color C pelargonin peonin malvin 400 500 600 Wavelength (nm) 700
  74. 74. pelargonin C21H21O10 HOMO-­‐1 HOMO-­‐4 LUMO 3.2. EFFECTS OF SIDE GROUPS LUMO L 0 sugar phenyl chromenylium -1.5 A A HOMO-­‐1 A B C Energy (ev) 3 C -2.5 2 B HOMO-­‐4 B 2.5 Absorption -2 energy KS orbital energy (ev) from  chemistry  to  color C pelargonin peonin malvin 400 500 600 Wavelength (nm) 700
  75. 75. chlorofyll  a C55H72MgN4O
  76. 76. chlorofyll  a ! tddft expt 400 500 600 " [nm] 700
  77. 77. chlorofyll  a ! tddft expt 400 500 600 " [nm] 700
  78. 78. color  and  func9on  of  anthocyanins cyanidin-­‐3-­‐glucoside
  79. 79. color  and  func9on  of  anthocyanins cyanidin-­‐3-­‐glucoside TDDFT  ?
  80. 80. color  and  func9on  of  anthocyanins cyanidin-­‐3-­‐glucoside absorp9on TDDFT  :? TDDFT   -­‐( tddfpt octopus gaussian 300 400 500 λ  [nm] 600 700
  81. 81. op9cal  effect  of  the  solvent !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600
  82. 82. op9cal  effect  of  the  solvent !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600
  83. 83. op9cal  effect  of  the  solvent !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600 C21H21O11Cl@(H2O)95 339  atoms 938  electrons
  84. 84. op9cal  effect  of  the  solvent !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600 C21H21O11Cl@(H2O)95 339  atoms 938  electrons
  85. 85. op9cal  effect  of  the  solvent Energy (ev) 3 2.5 2 Absorption !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600 avg 400 500 600 Wavelength (nm) 700 C21H21O11Cl@(H2O)95 339  atoms 938  electrons
  86. 86. op9cal  effect  of  the  solvent Energy (ev) 3 2.5 2 expt Absorption !"#$%&'$( )!# &*+ 400 500 ,-.(+/ 600 avg 400 500 600 Wavelength (nm) 700 C21H21O11Cl@(H2O)95 339  atoms 938  electrons
  87. 87. the  MARISA  way  to  molecular  design
  88. 88. the  MARISA  way  to  molecular  design • Set  up  and  benchmark  a  mul9scale  modeling  framework  for  simula9ng  the   molecular  structure  and  thermal  fluctua9ons  in  realis9c  solva9on   environments.  This  framework  will  be  based  on  advanced  embedding   techniques,  such  as: • • • MD  (Molecular  Dynamics) QM/MM  (Quantum  Mechanics/Molecular  Mechanics) PCM  (Polarizable  Con9nuum  Model).
  89. 89. the  MARISA  way  to  molecular  design • Set  up  and  benchmark  a  mul9scale  modeling  framework  for  simula9ng  the   molecular  structure  and  thermal  fluctua9ons  in  realis9c  solva9on   environments.  This  framework  will  be  based  on  advanced  embedding   techniques,  such  as: • • • • MD  (Molecular  Dynamics) QM/MM  (Quantum  Mechanics/Molecular  Mechanics) PCM  (Polarizable  Con9nuum  Model). Benchmark  state-­‐of-­‐the  art  quantum  mechanical  modeling  techniques   against  specific  a  molecular  proper9es  (color)  of  a  specific  class  of  molecules   (anthocyanins).  These  techniques  will  be  mainly  based  on   • TDDFT  (Time-­‐Dependent  Density-­‐Func9onal  Theory).
  90. 90. the  MARISA  way  to  molecular  design • Set  up  and  benchmark  a  mul9scale  modeling  framework  for  simula9ng  the   molecular  structure  and  thermal  fluctua9ons  in  realis9c  solva9on   environments.  This  framework  will  be  based  on  advanced  embedding   techniques,  such  as: • • • • QM/MM  (Quantum  Mechanics/Molecular  Mechanics) PCM  (Polarizable  Con9nuum  Model). Benchmark  state-­‐of-­‐the  art  quantum  mechanical  modeling  techniques   against  specific  a  molecular  proper9es  (color)  of  a  specific  class  of  molecules   (anthocyanins).  These  techniques  will  be  mainly  based  on   • • MD  (Molecular  Dynamics) TDDFT  (Time-­‐Dependent  Density-­‐Func9onal  Theory). Develop  approximate  schemes  for  quantum-­‐mechanical  calcula9ons  that   retain  the  accuracy  of  state-­‐of-­‐the-­‐art  techniques,  but  are  tailored  to  an   op9mal  performance  for  the  chroma9c  proper9es  of  anthocyanins.
  91. 91. the  MARISA  way  to  molecular  design • Set  up  and  benchmark  a  mul9scale  modeling  framework  for  simula9ng  the   molecular  structure  and  thermal  fluctua9ons  in  realis9c  solva9on   environments.  This  framework  will  be  based  on  advanced  embedding   techniques,  such  as: • • • • MD  (Molecular  Dynamics) QM/MM  (Quantum  Mechanics/Molecular  Mechanics) PCM  (Polarizable  Con9nuum  Model). Benchmark  state-­‐of-­‐the  art  quantum  mechanical  modeling  techniques   against  specific  a  molecular  proper9es  (color)  of  a  specific  class  of  molecules   (anthocyanins).  These  techniques  will  be  mainly  based  on   • TDDFT  (Time-­‐Dependent  Density-­‐Func9onal  Theory). • Develop  approximate  schemes  for  quantum-­‐mechanical  calcula9ons  that   retain  the  accuracy  of  state-­‐of-­‐the-­‐art  techniques,  but  are  tailored  to  an   op9mal  performance  for  the  chroma9c  proper9es  of  anthocyanins. • Use  those  approximate  schemes  for  the  high-­‐throughput  screening  of  large   numbers  of  candidate  anthocyanins  for  a  desired  property  (blue  color)  in   specific  condi9ons  of  temperature,  acidity,  etc.  
  92. 92. la  squadra  MARISA  @SISSA Alessandro  Biancardi,   chimico   Iurii  Timrov,   fisico Arrigo  Calzolari,   scienziato  dei  materiali,   consulente  (CNR,  Modena) XiaoChuan  Ge,   fisico,   studente  di  PhD grazie di esser qua Stefano  Baroni,   fisico,   PI baroni@sissa.it http://talks.baroni.me

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