Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semi-conductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illus- trates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning

Understanding phase transitions of α\alpha-quartz under dynamic compression conditions by machine-learning driven atomistic simulations

Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural transformations under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and crystallization transitions. Utilizing a newly developed machine-learning interatomic potential, we examine the response of $\alpha$-quartz to shock compression with a peak pressure of 60 GPa over nano-second timescales. We initially observe amorphization before recrystallization into a d-NiAs-structured silica with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions enables us to identify the non-hydrostatic stress and strain states that allow the direct diffusionless formation of rosiaite-structured silica

HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy

BACKGROUND The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level. METHODS Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing. RESULTS The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference. CONCLUSION Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org

Atomistic Modelling of Structure Formation and Phase Transitions in Si-Ox Compounds using Machine-Learning Interatomic Potentials

Silica is used in a wide range of applications from catalysis to construction to microelectronics. The related silicon monoxide is promising for applications as an anode material in lithium batteries. Although these materials have been extensively studied for more than a century, there are still many open questions. For example, the high-pressure transformations of silica are not fully understood. Moreover, in the case of silicon monoxide, there is not even an atomistic structure model that captures the complexity of the structure. In this work, we use atomistic modelling to investigate these problems. For this purpose, we developed several machine learning interatomic potentials (MLIP). First, we developed a Gaussian approximation potential (GAP) model based on a database with focus on bulk silica. Later, we switched to the atomic cluster expansion (ACE) framework. The final ACE potential is fitted to a more comprehensive training database labeled with energies and forces from strongly constrained and appropriately normed (SCAN) exchange-correlation density functional theory (DFT) data. The database covers a wide range of structures, including amorphous and crystalline silica, silica surfaces, high-pressure silica, and silicon-silica interfaces. Several approaches were used to build the database including ‘batch’ learning and active learning. Moreover, we present an active learning technique that extracts DFT feasible small-scale images from large-scale simulations (Chapter 3). The MLIPs are extensively tested in reproducing the thermodynamics of the systems and show excellent behavior, outperforming existing classical models. Nevertheless, to generate realistic amorphous structures of silica, we rely on a ‘hybrid’ protocol using a combination of our MLIP and a classical interatomic potential (Chapter 4). We apply the ACE potential to two cases. First, we study the high-pressure be- havior of amorphous silica and quartz under shock (Chapter 5). We find that there is an intermediate structure between the amorphous state and the crystalline stable state of stishovite. This phase is based on the defective nickel arsenide (d-NiAs) structure. The structure has a disordered silicon sublattice and an ordered hexagonal close-packed (HCP) oxygen sublattice. While the oxygen lattice appears to form fast on the molecular dynamics (MD) time scales, the ordering of the silicon and hence the formation of stishovite takes significantly longer. Moreover, we found that a direct transition between quartz and rosiaite-structured silica is also possible, which seems to require certain strain boundary conditions. Second, we generate structural models of silicon monoxide using melt-quench simulations (Chapter 6). These models show the same nanoscale segregation of silicon and silica as observed in experiment. Moreover, the energetics, grain sizes and X-ray structure factors of these models are in excellent agreement with the experiment. Using 20 ns annealing simulations, we are able to partially crystallize these structures and generate structural models with crystalline silicon in an amorphous silica matrix

Research data for "Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine-learning"

<p>This dataset supports the paper "Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine-learning". The paper is online here:</p> <p>The following files are provided:</p> <ul> <li>Potential files for the complex ACE potential for Si-O and additionally for the linear and Finnis-Sinclair ACE ("potential")</li> <li>Training database in a xyz file format as well as pckl.gzip with weights, which have been used for the fits. Includes also the parameter file for the DFT calculations ("database")</li> <li>Parameters files, which have been used for fitting the potentials ("fitting")</li> <li>Amorphous matrix embedding example script to extract the cells from the large-scale simulations as well as a script for the LAMMPS simulation to amorphize the boundaries ("amorphous_matrix_embedding")</li> <li>Results of various simulations with corresponding input scripts, input structures and analysis scripts ("results")<br> <ul> <li>High pressure simulations: Compression of amorphous silica up to 175 GPa, energy-volume curves, enthalpies</li> <li>Phase diagram: Input files for the calculation of the free energy and scripts to analyse the data to generate a phase diagram</li> <li>SiO: Contains the amorphous and partially crystalline structure files for the SiO models and the small-scale Si-SiO2 interface models. Moreover, files with structure factor data, crystallinity and interface area can be found here. </li> <li> Surfaces: Aerogel structures, which have been shown in the paper and the small-scale amorphous surface models with corresponding DFT data. It also includes the bulk reference data of the small-scale amorphous surface models, which is necessary to calculate the surface energy.</li> <li>Testsets: Include the testsets referenced in Table 1 and Supplementary Table 3.</li> <li>SiO2: Contains SiO2 structural models generated by the hybrid approach and using only the ACE potential.</li> </ul> </li> </ul> <p> </p&gt

Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Abstract Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning

A machine-learned interatomic potential for silica and its relation to empirical models

Silica (SiO₂) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field

A machine-learned interatomic potential for silica and its relation to empirical models

Silica (SiO 2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field

A machine-learned interatomic potential for silica and its relation to empirical models

AbstractSilica (SiO2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field.</jats:p