arXiv subjects

Federico Zipoli

Publications and source records attributed to Federico Zipoli.

9 recordsLinked to original sources

arXiv2025

TBHubbard: tight-binding and extended Hubbard model database for metal-organic frameworks

Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural compositions encoded in crystallographic information files complicates application-oriented, computational screening and design. The existing crystallographic data, therefore, requires augmentation by simulated data so that suitable descriptors for machine-learning and quantum computing tasks become available. Here, we provide extensive simulation data augmentation for MOFs within the QMOF database. We have applied a tight-binding, lattice Hamiltonian and density functional theory to MOFs for performing electronic structure calculations. Specifically, we provide a tight-binding representation of 10,000 MOFs, and an Extended Hubbard model representation for a sub-set of 240 MOFs containing transition metals, where intra-site U and inter-site V parameters are computed self-consistently. The data supports computational workflows for identifying structure-property correlations that are needed for inverse material design. For validation and reuse, we have made the data available at https://dataverse.harvard.edu/dataverse/tbhubbard/.

cond-mat.mtrl-sci↗

arXiv2025

Computing band gaps of periodic materials via sample-based quantum diagonalization

A key objective of computational solid state physics is to predict electronic properties of periodic materials. However, electronic structure simulations based on density functional theory fail to predict experimental results if correlations are not properly accounted for. Here, we report a sample-based quantum diagonalization workflow for simulating electronic states of periodic materials, and for predicting their band gaps. To that end, we devise a general lattice Hamiltonian representation in which material-specific, electronic interaction parameters are obtained self-consistently. Two exemplar, wide-gap materials - hafnium dioxide and zirconium dioxide - are expressed as quantum circuits that leverage the lattice representation with a materials-specific parametrization. We sample the quantum circuits on a state-of-the-art, superconducting quantum processor and diagonalize the lattice Hamiltonian in the reduced configuration subspaces with standard techniques. Our method outperforms select quantum-chemical benchmarks as well as approaches based on density functional theory, the standard reference in materials simulation of solids. Importantly, the quantum-computed band gap predictions for the two dielectrics agree with independent lab experiments. In essence, quantum-classical hybrid simulation workflows on pre-fault tolerant quantum computers produce useful, experimentally verifiable property predictions in applied materials science.

quant-ph↗

arXiv2024

Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry

Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts. Additionally, we provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions. This dataset has the potential automate structure elucidation, streamlining the molecular discovery pipeline from synthesis to structure determination. The dataset and code for the benchmarks can be found at https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset.

physics.chem-ph↗

arXiv2023

Learning a reactive potential for silica-water through uncertainty attribution

The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the $ω$-B97XD def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

cond-mat.mtrl-sci↗

arXiv2023

Accelerating Material Design with the Generative Toolkit for Scientific Discovery

With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on material design.

cs.LG↗

arXiv2022

On the Quality of Uncertainty Estimates from Neural Network Potential Ensembles

Neural network potentials (NNPs) combine the computational efficiency of classical interatomic potentials with the high accuracy and flexibility of the ab initio methods used to create the training set, but can also result in unphysical predictions when employed outside their training set distribution. Estimating the epistemic uncertainty of an NNP is required in active learning or on-the-fly generation of potentials. Inspired from their use in other machine-learning applications, NNP ensembles have been used for uncertainty prediction in several studies, with the caveat that ensembles do not provide a rigorous Bayesian estimate of the uncertainty. To test whether NNP ensembles provide accurate uncertainty estimates, we train such ensembles in four different case studies, and compare the predicted uncertainty with the errors on out-of-distribution validation sets. Our results indicate that NNP ensembles are often overconfident, underestimating the uncertainty of the model, and require to be calibrated for each system and architecture. We also provide evidence that Bayesian NNPs, obtained by sampling the posterior distribution of the model parameters using Monte-Carlo techniques, can provide better uncertainty estimates.

cond-mat.mtrl-sci↗

arXiv2019

The solid-state Li-ion conductor Li$_7$TaO$_6$: A combined computational and experimental study

We study the oxo-hexametallate Li$_7$TaO$_6$ with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of $\sim$0.29 eV and a high ionic conductivity of $5.7 \times 10^{-4}$ S cm$^{-1}$ at room temperature (300 K). We find evidence for a wide electrochemical stability window from both calculations and experiments, suggesting its viable use as a solid-state electrolyte in next-generation solid-state Li-ion batteries. To assess its applicability in an electrochemical energy storage system, we performed electrochemical impedance spectroscopy measurements on multicrystalline pellets, finding substantial ionic conductivity, if below the values predicted from simulation. We further elucidate the relationship between synthesis conditions and the observed ionic conductivity using X-ray diffraction, inductively coupled plasma optical emission spectrometry, and X-ray photoelectron spectroscopy, and study the effects of Zr and Mo doping.

cond-mat.mtrl-sci↗

arXiv2019

Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme

The recently published DeePMD model (https://github.com/deepmodeling/deepmd-kit), based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the application of first principle molecular dynamics to physical systems. With this contribution we assess the performance of the DeePMD potential on a real-life application and model diffusion of ions in solid-state electrolytes. We consider as test cases the well known Li10GeP2S12, Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocol suitable for the computation of diffusion coefficients, which is one of the key properties to be optimized for battery applications, and we find good agreement with previous computations. Our results show that the DeePMD model may be a successful component of a framework to identify novel solid-state electrolytes.

physics.comp-ph↗

arXiv2019

An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries

Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion. Being able to leverage this immense source of technical information helps to drastically reduce costs and time to solution in multiple application fields from food safety to pharmaceutics. We present a scalable document ingestion system that integrates data from databases and publications (in PDF format) in a biochemistry knowledge graph (BCKG). The BCKG is a comprehensive source of knowledge that can be queried to retrieve known biochemical facts and to generate novel insights. After describing the knowledge ingestion framework, we showcase an application of our system in the field of carbohydrate enzymes. The BCKG represents a way to scale knowledge ingestion and automatically exploit prior knowledge to accelerate discovery in biochemical sciences.

cs.IR↗