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Loriano Storchi

Other affiliations: University of Chieti-Pescara
Bio: Loriano Storchi is an academic researcher from University of Perugia. The author has contributed to research in topics: Detector & Physics. The author has an hindex of 17, co-authored 80 publications receiving 1594 citations. Previous affiliations of Loriano Storchi include University of Chieti-Pescara.


Papers
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Journal ArticleDOI
TL;DR: The advanced interferometer network will herald a new era in observational astronomy, and there is a very strong science case to go beyond the advanced detector network and build detectors that operate in a frequency range from 1 Hz to 10 kHz, with sensitivity a factor 10 better in amplitude as discussed by the authors.
Abstract: The advanced interferometer network will herald a new era in observational astronomy. There is a very strong science case to go beyond the advanced detector network and build detectors that operate in a frequency range from 1 Hz to 10 kHz, with sensitivity a factor 10 better in amplitude. Such detectors will be able to probe a range of topics in nuclear physics, astronomy, cosmology and fundamental physics, providing insights into many unsolved problems in these areas.

441 citations

Journal ArticleDOI
TL;DR: A new original computational method for pKa prediction of organic compounds that was developed, trained, and cross-validated by using a large and diverse data set of pKa values, and demonstrated that the predictive ability of the method is good.
Abstract: One of the most important physicochemical properties of a molecule is pKa. It is known that two parameters imperative in ADME profiling, solubility, and lipophilicity are governed by pKa, and receptor binding can be influenced by pKa. Because most drugs are ionized in physiological conditions, pKa is particularly relevant to medicinal chemistry. Despite the numerous advances in high-throughput measurements, in silico determination is still the fastest and cheapest way of obtaining pKa. This paper presents a new original computational method for pKa prediction of organic compounds. Descriptors were generated using the program GRID, and these descriptors are based on molecular interaction fields precomputed on a set of molecular fragments. The new method was developed, trained, and cross-validated by using a large and diverse data set of 24 617 pKa values. This paper presents the results for a class of 421 acidic nitrogen compounds (RMSE = 0.41, r2 = 0.97, q2 = 0.87) and for a class of 947 six-membered N-he...

275 citations

Journal ArticleDOI
TL;DR: Tautomeric rearrangements affect the results of cheminformatics applications that depend on the knowledge of the 2D or 3D structure of a compound, such as tools for database searches, fingerprint generation, virtual screening, and physical-chemical properties prediction.
Abstract: Tautomeric rearrangements affect the results of cheminformatics applications that depend on the knowledge of the 2D or 3D structure of a compound, such as tools for database searches, fingerprint generation, virtual screening, and physical-chemical properties prediction. In this paper we present TauThor, a tool to enumerate tautomers and predict tautomer stability in the aqueous medium. The enumeration is based on a recursive process that generates tautomers according to the general scheme HX-Y=Z ⇋ X=Y-ZH. The stability of a tautomer is calculated by using a library of 145 fragments associated with experimental tautomeric percentages in water and a pKa based-method that utilizes pKa values predicted by MoKa. Predicted tautomeric ratios based on pKa calculations were benchmarked against literature data for a set of eleven compounds. The FDA approved drugs database, the NCI database and two vendor databases - Specs Screening Library and Asinex Gold Collection - were used to illustrate the impact of tautomer...

113 citations

Journal ArticleDOI
TL;DR: High-throughput pK(a) measurements by using Spectral Gradient Analysis on novel series of compounds selected from vendor databases report the effect of specific chemical groups and steric constraints on the pK (a) of common functionalities in medicinal chemistry, such as amines, sulfonamides, and amides.

107 citations

Journal ArticleDOI
TL;DR: In this article, a quantum mechanical investigation on the nature of electronic trap states in realistic models of individual and sintered anatase TiO2 nanocrystals (NCs) of ca. 3 nm diameter was conducted.
Abstract: We report a quantum mechanical investigation on the nature of electronic trap states in realistic models of individual and sintered anatase TiO2 nanocrystals (NCs) of ca. 3 nm diameter. We find unoccupied electronic states of lowest energy to be localized within the central part of the NCs, and to originate from under-coordinated surface Ti atoms lying mainly at the edges between the (100) and (101) facets. These localized states are found at about 0.3–0.4 eV below the fully delocalized conduction band states, in good agreement with both electrochemical and spectro-electrochemical results. The overall Density-Of-States (DOS) below the conduction band (CB) can be accurately fitted to an exponential distribution of states, in agreement with capacitance data. Water molecules adsorbed on the NC surface raise the energy and reduce the number of localized states, thus modifying the DOS. As a possible origin of additional trap states, we further investigated the oriented attachment of two TiO2 NCs at various possible interfaces. For the considered models, we found only minor differences between the DOS of two interacting NCs and those of the individual constituent NCs. Our results point at the presence of inherent trap states even in perfectly stoichiometric and crystalline TiO2 NCs due to the unavoidable presence of under-coordinated surface Ti(IV) ions at the (100) facets.

86 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal Article
TL;DR: In this paper, the ATLAS experiment is described as installed in i ts experimental cavern at point 1 at CERN and a brief overview of the expec ted performance of the detector is given.
Abstract: This paper describes the ATLAS experiment as installed in i ts experimental cavern at point 1 at CERN. It also presents a brief overview of the expec ted performance of the detector.

2,798 citations

Journal ArticleDOI
01 May 1970

1,935 citations

01 Feb 1995
TL;DR: In this paper, the unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio using DFT, MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set.
Abstract: : The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

1,652 citations