Zero-temperature localization in a sub-Ohmic spin-boson model investigated by an extended hierarchy equation of motion

With a decomposition scheme for the bath correlation function, the hierarchy equation of motion (HEOM) is extended to the zero-temperature sub-Ohmic spin-boson model, providing a numerically accurate prediction of quantum dynamics. As a dynamic approach, the extended HEOM determines the delocalized-localized (DL) phase transition from the extracted rate kernel and the coherent-incoherent dynamic transition from the short-time oscillation. As the bosonic bath approaches from the strong to weak sub-Ohmic regimes, a crossover behavior is identified for the critical Kondo parameter of the DL transition, accompanied by the transition from the coherent to incoherent dynamics in the localization

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here, we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating pairs of structures, i.e., reactant, TS, and product, in an elementary reaction. Provided reactant and product, this model generates a TS structure in seconds instead of the hours required when performing quantum chemistry-based optimizations. The generated TS structures achieve an average error of 0.13 A root mean square deviation compared to true TS. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction rate estimation (2.6 kcal/mol) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision the proposed approach to be useful in constructing and pruning large reaction networks with unknown mechanisms

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from an accuracy and a computational cost perspective, complicating high throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models to predict the excited state properties of photoactive iridium complexes. We use experimental data of 1,380 iridium complexes to train and evaluate the ML models and identify the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional theory tight binding calculations. Using these models, we predict the three excited state properties considered, mean emission energy of phosphorescence, excited state lifetime, and emission spectral integral, with accuracy competitive with or superseding TDDFT. We conduct feature importance analysis to identify which iridium complex attributes govern excited state properties and we validate these trends with explicit examples. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and identify promising ligands for the design of new phosphors

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery

Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets like open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (ca. 1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the periodic table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the effective nuclear charge alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data is limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the periodic table, a property we expect to be broadly useful for other materials domains

M2^2Hub: Unlocking the Potential of Machine Learning for Materials Discovery

We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at https://github.com/yuanqidu/M2Hub

Quantum Chemistry Meets Machine Learning: Autonomous Computational Workflow for Chemical Discovery

Automation has long been revolutionizing our modern society since the first industrial revolution and has the potential to provide sufficient productivity forces for revolution is ongoing in computational sciences. Quantum chemistry software and modern computers have developed to a stage where virtual high throughput screening (VHTS), i.e., running thousands of calculations in parallel, becomes possible. This provides great opportunities for developing automated workflows to utilize the increasing computing power to generate large-scale data sets. Together with machine learning (ML) models trained on these data sets as either surrogate function approximations or generative models, accelerated chemical discovery for functional molecules and materials are achieved. Current automation workflows, however, are far from perfect. Namely, they produce too many unfruitful results and suffer severely from method selection bias, especially on challenging chemical spaces such as transition metal chemistry. These problems limit the automated workflows from providing common prosperity. Similar efficiency and accuracy needed for chemical discovery. In this Thesis, we introduce intelligent ML-based decision-making models in automation workflows. We build the first set of classifiers to predict the likelihood of calculation success that on-the-fly monitors and terminates an already running calculations if they are predicted to fail with high confidence. These classifiers are extremely transferable and stays accurate (i.e.,>95%) during the whole geometry optimization process, saving >1/2 of the computation resources. We develope the first semi-supervised learning classifier to identify strong static correlation in a system, achieving state-of-the-art performance for this task. Therefore, we can pre-determine which systems require more expensive (yet more accurate) correlated wavefunction theory calculations, thus improving overall data accuracy without adding unnecessary computational cost. We also proposed an approach that utilizes the consensus among multiple density functional approximations (DFAs) to discover robust (i.e., DFA- insensitive) candidate compounds, which are in much better agreement with experimentally observed leads. Lastly, we built a DFA recommender that selects the DFA with the lowest expected error to the reference in a system-dependent manner, achieving the accuracy needed for inorganic chemical discovery. All these ML-based decision-making models are integrated in workflows for VHTS. We anticipate these “smart” computational workflows are keys to autonomous chemical discovery.Ph.D

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

<p>Code and model release for OA-ReactDiff, a generalizied Denoising Diffusion Probabilistic Model (DDPM) for generating sets of 3D  structures (i.e., reactant, transition state, and product) in chemical reactions. OA-ReactDiff reduces the transition state search from hours/days to seconds and complemented conventional intuition-based reaction exploration with generative AI solutions that can discover surprising "unintended" reactions. A codebase under continuous development can be found at https://github.com/chenruduan/OAReactDiff. </p&gt