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Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes

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JMIR Research Protocols 2018年第7期7卷 e10961页

作者： Suominen, Hanna Kelly, Liadh Goeuriot, Lorraine Research School of Computer Science College of Engineering and Computer Science The Australian National University Canberra ACT Australia Machine Learning Research Group Data61 Commonwealth Scientific and Industrial Research Organisation Canberra ACT Australia Faculty of Science and Technology University of Canberra Canberra ACT Australia Department of Future Technologies Faculty of Science and Engineering University of Turku Turku Finland Department of Computer Science Maynooth University Maynooth Co Kildare Ireland Grenoble Informatics Laboratory Université Grenoble Alpes Grenoble France

Background: The eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams’ goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients’ understanding and clinicians’ authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers’ information access. Objectives: This original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations. Methods: A review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar. Results: As many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data gen

关键词： Evaluation studies as topic Health records Information extraction Information storage and retrieval Information visualization Patient education as topic Speech recognition Systematic reviews Test-set generation Text classification

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Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design

arXiv

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arXiv 2024年

作者： Weinreich, Jan Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Hermansson, Kersti Anatole von Lilienfeld, O. LausanneCH-1015 Switzerland University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 Uppsala75121 Sweden Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first one is energy changes associated with charge excitations of molecules, namely ionization potential and electron affinity. They were estimated for 7000 randomly chosen molecules with up to 9 non-hydrogen atoms C, N, O, and F (QM9 dataset) using DH-HF, DF-HF-CABS, PNO-LMP2-F12, and PNO-LCCSD(T)-F12 methods as implemented in Molpro software with aug-cc-pVTZ basis set;additionally, we provide the corresponding atomization energies at these levels of theory, as well as CPU time and disk space used during the calculations. The second one is solvation energies for 39 different solvents, which we estimate for 18361 molecules connected to battery design (Electrolyte Genome Project dataset), 309463 randomly chosen molecules with up to 17 non-hydrogen atoms C, N, O, S, and halogens (GDB17 dataset), as well as 88418 amons of ZINC database of commercially available compounds and 37772 amons of GDB17. For these calculations we used the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) method;we additionally provide estimates of gas-phase atomization energies, as well as information about conformers considered during the COSMO-RS calculations, namely coordinates, energies, and dipole moments. © 2024, CC BY.

关键词： Solvation

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machine learning Force Fields

arXiv

引用

arXiv 2020年

作者： Unke, Oliver T. Chmiela, Stefan Sauceda, Huziel E. Gastegger, Michael Poltavsky, Igor Schütt, Kristof T. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Technische Universität Berlin Berlin10623 Germany BASLEARN BASF-TU joint Lab Technische Universität Berlin Berlin10587 Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany

In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Towards DMC accuracy across chemical space with scalable ∆-QML

arXiv

引用

arXiv 2022年

作者： Huang, Bing von Lilienfeld, O. Anatole Krogel, Jaron T. Benali, Anouar University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to under-gird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for ∆-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer learning in Gridded Geo-Spatial Processes

arXiv

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arXiv 2022年

作者： Eichenberger, Christian Neun, Moritz Martin, Henry Herruzo, Pedro Spanring, Markus Lu, Yichao Choi, Sungbin Konyakhin, Vsevolod Lukashina, Nina Shpilman, Aleksei Wiedemann, Nina Raubal, Martin Wang, Bo Vu, Hai L. Mohajerpoor, Reza Cai, Chen Kim, Inhi Hermes, Luca Melnik, Andrew Velioglu, Riza Vieth, Markus Schilling, Malte Bojesomo, Alabi Al Marzouqi, Hasan Liatsis, Panos Santokhi, Jay Hillier, Dylan Yang, Yiming Sarwar, Joned Jordan, Anna Hewage, Emil Jonietz, David Tang, Fei Gruca, Aleksandra Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland Layer 6 AI Toronto Canada ITMO University Saint Petersburg Russia JetBrains Research Saint Petersburg Russia HSE University Saint Petersburg Russia Institute of Transport Studies Monash University ClaytonVIC Australia CSIRO’s Data61 Eveleigh Australia Institute Civil and Environmental Engineering Department Kongju National University Korea Republic of Machine Learning & Neuroinformatics Group Bielefeld University Germany Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Alchera Data Technologies Ltd Cambridge United Kingdom HERE Technologies Zurich Switzerland Silesian University of Technology Gliwice Poland Machine Learning Institute Johannes Kepler University Linz Austria

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 1012 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input. © 2022, CC BY-NC-SA.

关键词： Probes

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Pushing the boundaries of asteroseismic individual frequency modelling: Unveiling two evolved very low-metallicity red giants

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Astronomy and Astrophysics 2025年 697卷

作者： Larsen, J.R. Rørsted, J.L. Aguirre Børsen-Koch, V. Lundkvist, M.S. Christensen-Dalsgaard, J. Winther, M.L. Stokholm, A. Li, Y. Slumstrup, D. Kjeldsen, H. Corsaro, E. Benomar, O. Dhanpal, S. Weiss, A. Mosser, B. Hekker, S. Stello, D. Korn, A.J. Jendreieck, A. Elsworth, Y. Handberg, R. Kallinger, T. Jiang, C. Ruchti, G. Stellar Astrophysics Centre (SAC) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark Aarhus Astronomy Data Centre (AADC) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 18 Copenhagen 2200 Denmark School of Physics and Astronomy University of Birmingham Edgbaston B15 2TT United Kingdom Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive Honolulu 96822 HI United States GRANTECAN Cuesta de San José s/n La Palma Breña Baja E-38712 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna E-38205 Spain European Southern Observatory Alonso de Cordova 3107 Vitacura Chile Aarhus Space Centre (SpaCe) Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C. 8000 Denmark INAF Osservatorio Astrofisico di Catania Via S. Sofia 78 Catania 95123 Italy Department of Astronomical Science School of Physical Sciences SOKENDAI 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Solar Science Observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Scientific Machine Learning group Rutherford Appleton Laboratory Science and Technology Facilities Council Harwell Campus Didcot OX11 0QX United Kingdom Max-Planck-Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching 85748 Germany LIRA Observatoire de Paris Université PSL CNRS Sorbonne Université Université Paris-Cité Meudon 92195 France Heidelberger Institut für Theoretische Studien Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Center for Astronomy (ZAH/LSW) Heidelberg University Königstuhl 12 Heidelberg 69117 Germany School of Physics University of New South Wales Sydney 2052 NSW Australia Sydney Institute for Astronomy (SIfA) School of Physics University of Sydney Sydney 2006 NSW Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimension

Context. Metal-poor stars play a crucial role in understanding the nature and evolution of the first stellar generation in the Galaxy. Previously, asteroseismic characterisation of red-giant stars has relied on constraints from the global asteroseismic parameters and not the full spectrum of individual oscillation modes. Using the latter, we present for the first time the characterisation of two evolved very metal-poor stars including the detail-rich mixed-mode patterns. Aims. We will demonstrate that incorporating individual frequencies into grid-based modelling of red-giant stars enhances its precision, enabling detailed studies of these ancient stars and allowing us to infer the stellar properties of two very metal-poor [Fe/H] ∼ -2.5 dex Kepler stars: KIC 4671239 and KIC 7693833. Methods. Recent developments in both observational and theoretical asteroseismology have allowed for detailed studies of the complex oscillation pattern of evolved giants. In this work, we employ Kepler timeseries and surface properties from high-resolution spectroscopic data within a grid-based modelling approach to asteroseismically characterise KIC 4671239 and KIC 7693833 using the BAyesian STellar Algorithm, BASTA. Results. Both stars show agreement between constraints from seismic and classical observables, an overlap unrecoverable when purely considering the global asteroseismic parameters. KIC 4671239 and KIC 7693833 were determined to have masses of 0.78_-0.03^+0.04 and 0.83_-0.01^+0.03_-0.01 M_⊙ with ages of 12.1_-1.5^+1.6 and 10.3_-1.4^+0.6 Gyr, respectively. Particularly, for KIC 4671239 the rich spectrum of model frequencies closely matches the observed. Conclusions. A discrepancy between the observed and modelled ν_max of ∼10% was found, indicating a metallicity dependence of the ν_max scaling relation. For metal-poor populations, this results in overestima

关键词： Asteroseismology Stars: evolution Stars: individual: KIC 4671239 Stars: individual: KIC 7693833 Stars: oscillations

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Improving Generative Model-based Unfolding with Schrödinger Bridges

arXiv

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arXiv 2023年

作者： Diefenbacher, Sascha Liu, Guan-Horng Mikuni, Vinicius Nachman, Benjamin Nie, Weili Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Machine Learning Research Group NVIDIA Research United States

machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one based on discriminative models and one based on generative models. The main advantage of discriminative models is that they learn a small correction to a starting simulation while generative models scale better to regions of phase space with little data. We propose to use Schrödinger Bridges and diffusion models to create SBUnfold, an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of SBUnfold is that its generative model maps one set of events into another without having to go through a known probability density as is the case for normalizing flows and standard diffusion models. We show that SBUnfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset. © 2023, CC BY.

关键词： Phase space methods

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Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository

Neuroscience Applied

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Neuroscience Applied 2025年 4卷

作者： Khuntia, Adyasha Buciuman, Madalina-Octavia Fanning, John Stolicyn, Aleks Vetter, Clara Armio, Reetta-Liina From, Tiina Goffi, Federica Hahn, Lisa Kaufmann, Tobias Laurikainen, Heikki Maggioni, Eleonora Martinez-Zalacain, Ignacio Ruef, Anne Dong, Mark Sen Schwarz, Emanuel Squarcina, Letizia Andreassen, Ole Bellani, Marcella Brambilla, Paolo Haren, Neeltje van Hietala, Jarmo Lawrie, Stephen M. Soriano-Mas, Carles Whalley, Heather Taquet, Maxime Meisenzahl, Eva Falkai, Peter Wiegand, Ariane Koutsouleris, Nikolaos Department of Psychiatry and Psychotherapy University Hospital Ludwig-Maximilian University Munich Germany International Max-Planck School for Translational Psychiatry Germany Max-Planck Institute of Psychiatry Munich Munich Germany Max Planck Institute of Psychiatry Munich Germany German Center for Mental Health Partner Site Munich Augsburg Germany Division of Psychiatry Centre for Clinical Brain Sciences University of Edinburgh Scotland Edinburgh United Kingdom Munich Center of Machine Learning Munich Germany Helmholtz Association - Munich School for Data Science Munich Germany Department of Psychiatry University of Turku Turku Finland Turku PET Centre University of Turku Turku Finland Turku University Hospital Turku Finland Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Neurosciences and Mental Health Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico University of Milan Milan Italy NORMENT Centre Division of Mental Health and Addiction Oslo University Hospital Oslo Norway Department of Psychiatry and Psychotherapy University of Tübingen Germany German Center for Mental Health Partner Site Tübingen Germany Bellvitge Biomedical Research Institute-IDIBELL Psychiatry Department Bellvitge University Hospital CIBERSAM and Department of Psychobiology and Methodology of Health Sciences Universitat Autònoma de Barcelona Spain Department of Radiology Bellvitge University Hospital Barcelona Spain Hector Institute for Artificial Intelligence in Psychiatry Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany Department of Psychiatry and Psychotherapy Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University Mannheim Germany German Center for Mental Health Partner Site Mannheim-Heidelberg-Ulm Germany Department of Pathophysiology and Transplantation University of Milan Milan Italy Section of Psychiatry Dept of Neuroscience

The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible data Repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R²) = 0.77, P < .001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R² = 0.74,P < .001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R² = 0.79, P < .001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, mu

关键词： data repository machine learning Multi-site data Neuroimaging Psychiatry Structural MRI Transdiagnostic

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Accurate global machine learning force fields for molecules with hundreds of atoms

arXiv

引用

arXiv 2022年

作者： Chmiela, Stefan Vassilev-Galindo, Valentin Unke, Oliver T. Kabylda, Adil Sauceda, Huziel E. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data – BIFOLD Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Google Research Brain Team Berlin Germany Departamento de Materia Condensada Instituto de Física Universidad Nacional Autónoma de México Cd. de MéxicoC.P. 04510 Mexico BASLEARN - TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of

Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the model complexity with system size. For larger molecules, locality assumptions are typically introduced, with the consequence that non-local interactions are poorly or not at all described, even if those interactions are contained within the reference ab initio data. Here, we approach this challenge and develop an exact iterative parameter-free approach to train global symmetric gradient domain machine learning (sGDML) force fields for systems with up to several hundred atoms, without resorting to any localization of atomic interactions or other potentially uncontrolled approximations. This means that all atomic degrees of freedom remain fully correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of our MLFFs on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond long path-integral molecular dynamics simulations for the supramolecular complexes in the MD22 dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space

arXiv

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arXiv 2023年

作者： Bragato, Marco Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 1416 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multi-fidelity learning. We introduce baseline labels in the form of a generic coarse-graining Hammett Inspired Product (HIP) Ansatz which generalizes the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, and activation energies for SN2 reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-quantum machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models. © 2023, CC0.

关键词： data acquisition

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