arXiv subjects

Stefan Heinen

Publications and source records attributed to Stefan Heinen.

9 recordsLinked to original sources

arXiv2025

On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots

In gate-defined quantum dot systems, the conductance change of electrostatically coupled sensor dots allows the observation of the quantum dots' charge and spin states. Therefore, the sensor dot must be optimally sensitive to changes in its electrostatic environment. A series of conductance measurements varying the two sensor-dot-forming barrier gate voltages serve to tune the dot into a corresponding operating regime. In this paper, we analyze the noise characteristics of the measured data and define a criterion to identify continuous regions with a sufficient signal-gradient-to-noise ratio. Hence, accurate noise estimation is required when identifying the optimal operating regime. Therefore, we evaluate several existing noise estimators, modify them for 1D data, optimize their parameters, and analyze their quality based on simulated data. The estimator of Chen et al. turns out to be best suited for our application concerning minimally scattering results. Furthermore, using this estimator in an algorithm for flank-of-interest classification in measured data shows the relevance and applicability of our approach.

cond-mat.mes-hall↗

arXiv2024

Combining Hammett $\sigma$ constants for $\Delta$-machine learning and catalyst discovery

We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $\sigma$ and $\rho$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $\sigma$ constants for any ligand pair combination results in a cHIP model that enhances data efficiency in computational ligand tuning. We show its usefulness (i) as a baseline for $\Delta$-machine learning (ML), and (ii) to identify novel catalyst candidates via volcano plots. After testing the combination rule on Hammett constants previously published in the literature, we have generated numerical evidence for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines, or pyridines). When used as a baseline, $\Delta$-ML prediction errors of relative binding decrease systematically with training set size and reach chemical accuracy ($\sim$1 kcal/mol) for 20k training instances. Employing the individual ligand constants obtained from cHIP, we report relative substrate binding for a novel dataset consisting of 720 catalysts (not part of training data), of which 145 fall into the most promising range on the volcano plot accounting for oxidative addition, transmetalation, and reductive elimination steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P($t$-Bu)$_3$, are included among these promising candidates, potentially offering dramatic cost savings in experimental applications.

physics.chem-ph↗

arXiv2023

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to $10^{6}$ QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.

physics.chem-ph↗

arXiv2023

Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZ-F12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of $\sim$ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9$^\mathrm{LCCSD(T)}$, EGP, QM9$^\mathrm{CCSD(T)}_\mathrm{AE}$, and QM9$^\mathrm{CCSD(T)}_\mathrm{EA}$, respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder:~LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and $\tau$-HCTH, B3LYP$\ast$(VWN5), TPSSH.

physics.chem-ph↗

arXiv2023

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials

We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (-0.75 to 3.58 V). After data curation (solvents, reference electrodes, and missed data points), we trained multiple supervised machine learning models reaching prediction errors similar to experimental uncertainty ($\sim$0.2 V). For experimental measurements of identical molecules reported in multiple studies, we identified the most likely value based on out-of-sample machine learning predictions. Using the trained machine learning models, we then estimated oxidation potentials of $\sim$132k small organic molecules from the QM9 data set, with predicted values spanning 0.21 to 3.46 V. Analysis of the QM9 predictions in terms of plausible descriptor-property trends suggests that aliphaticity increases the oxidation potential of an organic molecule on average from $\sim$1.5 V to $\sim$2 V, while an increase in number of heavy atoms lowers it systematically. The pipeline introduced offers significant reductions in human labor otherwise required for conventional manual data collection of experimental results, and exemplifies how to accelerate scientific research through automation.

physics.chem-ph↗

arXiv2023

Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations

The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicable throughout chemical compound space. Unfortunately, the most accurate representations require a high dimensional feature mapping, thereby imposing a considerable computational burden on model training and use. We introduce compact yet accurate, linear scaling QML representations based on atomic Gaussian many-body distribution functionals (MBDF), and their derivatives. Weighted density functions (DF) of MBDF values are used as global representations which are constant in size, i.e.~invariant with respect to the number of atoms. We report predictive performance and training data efficiency that is competitive with state of the art for two diverse datasets of organic molecules, QM9 and QMugs. Generalization capability has been investigated for atomization energies, HOMO-LUMO eigenvalues and gap, internal energies at 0 K, zero point vibrational energies, dipole moment norm, static isotropic polarizability, and heat capacity as encoded in QM9. MBDF based QM9 performance lowers the optimal Pareto front spanned between sampling and training cost to compute node minutes,~effectively sampling chemical compound space with chemical accuracy at a sampling rate of $\sim 48$ molecules per core second.

physics.chem-ph↗

arXiv2021

Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

While sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behaviour remain challenging. We introduce a reactant-to-barrier (R2B) machine learning model that rapidly and accurately infers activation energies and transition state geometries throughout chemical compound space. R2B enjoys improving accuracy as training sets grow, and requires as input solely molecular graph information of the reactant. We provide numerical evidence for the applicability of R2B for two competing text-book reactions relevant to organic synthesis, E2 and SN2, trained and tested on chemically diverse quantum data from literature. After training on 1k to 1.8k examples, R2B predicts activation energies on average within less than 2.5 kcal/mol with respect to Coupled-Cluster Singles Doubles (CCSD) reference within milliseconds. Principal component analysis of kernel matrices reveals the hierarchy of the multiple scales underpinning reactivity in chemical space: Nucleophiles and leaving groups, substituents, and pairwise substituent combinations correspond to systematic lowering of eigenvalues. Analysis of R2B based predictions of ~11.5k E2 and SN2 barriers in gas-phase for previously undocumented reactants indicates that on average E2 is favored in 75% of all cases and that SN2 becomes likely for nucleophile/leaving group corresponding to chlorine, and for substituents consisting of hydrogen or electron-withdrawing groups. Experimental reaction design from first principles is enabled thanks to R2B, which is demonstrated by the construction of decision trees. Numerical R2B based results for interatomic distances and angles of reactant and transition state geometries suggest that Hammond's postulate is applicable to SN2, but not to E2.

physics.comp-ph↗

arXiv2019

Machine learning the computational cost of quantum chemistry

Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For 2D non-linear toy systems, single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evidence for a toy system containing two functions and three commonly used optimizer and for thousands of organic molecular systems including closed and open shell equilibrium structures, as well as transition states. Levels of electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d), local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison to conventional indiscriminate job treatment, QML based wall time predictions significantly improve job scheduling efficiency for all tasks after training on just thousands of molecules. Resulting reductions in CPU time overhead range from 10% to 90%.

physics.chem-ph↗

arXiv2019

Control Electronics For Semiconductor Spin Qubits

Future universal quantum computers solving problems of practical relevance are expected to require at least $10^6$ qubits, which is a massive scale-up from the present numbers of less than 50 qubits operated together. Out of the different types of qubits, solid state qubits are considered to be viable candidates for this scale-up, but interfacing to and controlling such a large number of qubits is a complex challenge that has not been solved yet. One possibility to address this challenge is to use qubit control circuits located close to the qubits at cryogenic temperatures. In this work we evaluate the feasibility of this idea, taking as a reference the physical requirements of a two-electron spin qubit and the specifications of a standard 65 nm complementary metal-oxide-semiconductor (CMOS) process. Using principles and flows from electrical systems engineering we provide realistic estimates of the footprint and of the power consumption of a complete control-circuit architecture. Our results show that with further research it is possible to provide scalable electrical control in the vicinity of the qubit, with our concept.

quant-ph↗