Deep QMC methods use neural networks to solve quantum chemistry problems.
problem Solving the electronic Schrödinger equation from first principles.
method Quantum Monte Carlo with neural network wavefunctions.
result Highly accurate solutions at reduced computational cost.
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule…
Derives symmetric and antisymmetric kernels for quantum physics and chemistry applications.
problem Efficiently handling symmetries and antisymmetries in machine learning for quantum physics and chemistry.
method Symmetrizing and antisymmetrizing conventional kernels, analyzing feature space dimensions, proving kernel properties, proposing Slater determinant representation.
result Efficient evaluation of antisymmetric Gaussian kernels even in high-dimensional state spaces, significant reduction in training data size.
QGAA learns latent quantum states, reducing errors in quantum data generation.
problem Learning latent representations for quantum data generation.
method Quantum Generative Adversarial Autoencoder (QGAA) combining QAE and QGAN.
result Average errors in energies for H2 and LiH are 0.02 Ha and 0.06 Ha respectively, demonstrating QGAA's potential.
New dataset abla2DFT for drug-like molecules benchmarks neural network potentials.
problem Lack of large, diverse datasets for training neural network potentials in quantum chemistry.
method Developed a new dataset abla2DFT containing energies, forces, and molecular properties for drug-like molecules. result First dataset with relaxation trajectories for drug-like molecules.
Deep neural network predicts molecular wave functions in minimal basis.
problem Improving accuracy and efficiency in quantum chemistry calculations.
method Adapted SchNet for Orbitals (SchNOrb) model in quasi-atomic minimal basis.
result Model accurately predicts molecular orbital energies and wavefunctions for large molecules.
A new approach to quantum machine learning circuits reduces training difficulties.
problem Challenges in training deep quantum circuits due to flat training landscapes.
method Variable structure approach (VAns) to build ansatzes, applying rules for gate growth and removal.
result VAns successfully mitigates trainability and noise-related issues, improving performance in various applications.
A new model explains protein interactions via electron delocalization.
problem Understanding how protein interactions affect each other.
method Quantized discrete differential geometry of n-simplices.
result Allosteric regulation follows from the model of interactions.
New model accurately predicts chemical bond breaking.
problem Challenges in describing bond breaking in quantum chemistry.
method Pretrained deep neural network wavefunction model Orbformer.
result Consistently converges to chemical accuracy (1 kcal/mol).
Machine learning aids excited-state molecular dynamics studies.
problem Challenges in studying electronically excited states of molecules.
method Employing machine learning techniques for excited-state molecular dynamics.
result Highlight successes and challenges in machine learning for excited-state processes.
Deep learning improves OFDFT for molecular systems.
problem Limited accuracy of OFDFT for non-periodic molecular systems.
method M-OFDFT using deep learning to approximate kinetic energy density.
result Achieves comparable accuracy to Kohn-Sham DFT on large molecules.
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy…
We propose a hybrid quantum-classical algorithm, originated from quantum chemistry, to price European and Asian options in the Black-Scholes model. Our approach is based on the equivalence between the pricing partial differential equation and the Schrodinger equation in imaginary time. We devise a strategy to build a s…
Quantum machine learning boosts drug discovery efficiency.
problem Enhancing drug discovery through quantum computing.
method Quantum neural networks on gate-based quantum computers.
result Significant advancements in molecular property prediction and generation.
New algorithm efficiently trains machine learning models to atomic forces data.
problem Efficiently training machine learning models to large amounts of force data.
method Developed an efficient algorithm for training machine learning models to all available force data.
result Training to all available force data is only a few times more expensive than training to energies alone.
Machine learning speeds up quantum chemical calculations of excited states.
problem Accurate quantum chemical calculations of excited states are computationally expensive.
method Employing machine learning to speed up and advance excited-state simulations in various fields.
result Machine learning techniques can significantly reduce computational time for excited-state simulations.
We compute Khovanov homology for tangles using TQFT.
problem Khovanov homology for tangles is not well studied or computed.
method Topological Quantum Field Theory (TQFT) construction.
result A comprehensive method for computing Khovanov homology of tangles.
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN). These models rely on local aggregation operations and can therefore miss higher-order graph properties. To remedy this, we propose Path-Augmented Graph Transformer Networks (PAGTN) that are explicitly built…
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of …
A new training method improves MLIPs for faster, lighter simulations.
problem High computational and memory costs of complex MLIPs for large-scale MD simulations.
method Teacher-student training framework using latent atomic energy knowledge.
result Lightweight student MLIPs achieve faster MD speeds and comparable accuracy to teachers.
Q-CurL optimizes quantum learning with a curriculum design.
problem Efficiently training quantum models with limited resources.
method Quantum curriculum learning framework.
result Q-CurL enhances training convergence and generalization.
MPE framework proves universal approximation for quantum data distribution.
problem Challenges in generating quantum data from underlying distributions.
method Many-body Projected Ensemble (MPE) framework for quantum state design.
result MPE can approximate any quantum distribution within 1-Wasserstein distance error.
Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerge…
New method explains GNN predictions using walks.
problem GNNs are black-boxes and hard to explain.
method Nested attribution scheme using relevant walks.
result Extracts meaningful explanations from GNNs.
FlowMM models stable crystal structures efficiently.
problem Predicting and proposing stable crystalline structures.
method Riemannian Flow Matching generalized to crystal symmetries.
result 3x more efficient at finding stable materials.
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the la…
Quantum model discovery uses DQCs to solve equations from data.
problem Discovering differential equations from data using quantum computing.
method Differentiable quantum circuits (DQCs) to solve parameterized equations, regression on data and equations.
result Successful parameter inference and equation discovery on various systems.
Kernel method approximates dynamical operators from data.
problem Estimating eigenfunctions of dynamical operators from data.
method Kernel-based approach in reproducing kernel Hilbert spaces.
result Eigenfunctions estimated via matrix eigenvalue problems.
New method combines deep learning and quantum mechanics for efficient molecular statistics.
problem Computational expense in extracting statistics from molecular systems.
method Adaptive Markov chain Monte Carlo with Normalizing Flow and MLP for quantum accuracy.
result Rapid convergence to Boltzmann distribution and accurate thermodynamic observables.
We establish geometric properties of Stiefel and Grassmann manifolds which arise in relation to Slater type variational spaces in many-particle Hartree-Fock theory and beyond. In particular, we prove that they are analytic homogeneous spaces and submanifolds of the space of bounded operators on the single-particle Hilb…
New algorithm improves efficiency of quantum system modeling.
problem Intractable complexities in quantum Hamiltonian learning and Gibbs sampling.
method Generalized quantum natural gradient descent and Quantum-Probabilistic Mirror Descent.
result Data sample efficiency proven using information geometry and quantum metrology.
Develops an analytic theory for quantum imaginary time evolution.
problem Lack of a first-principle understanding of quantum imaginary time evolution.
method Interprets QITE as a form of VQA trained with QNGD and connects it to the geometric geodesic distance in the quantum Fisher information metric.
result QITE converges faster than vanilla gradient descent-based VQAs, though the advantage is suppressed by Hilbert space dimensionality.
Machine learning models simulate molecular spectra and reactions in solvents.
problem Accurate simulation of molecular spectra and reactions in solvent environments.
method Introduced FieldSchNet, a deep neural network for modeling molecular interactions with external fields.
result Demonstrated significant lowering of Claisen rearrangement reaction activation barrier using FieldSchNet.
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynam…
New method uses adiabatic principles to improve ground-state preparation in quantum computing.
problem Challenges in variational training of complex energy landscapes.
method Iterative Hamiltonian deformation complemented with adiabatic principles.
result Consistent convergence to target ground state through sequence of intermediate problems.
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not …
Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict mat…
GDB bridges geometric states with improved accuracy and generality.
problem Challenges in predicting geometric state evolution in complex systems.
method Geometric Diffusion Bridge (GDB) framework using equivariant diffusion bridges.
result GDB surpasses existing methods in accurately bridging geometric states.
A spin network is a cubic ribbon graph labeled by representations of SU(2). Spin networks are important in various areas of Mathematics (3-dimensional Quantum Topology), Physics (Angular Momentum, Classical and Quantum Gravity) and Chemistry (Atomic Spectroscopy). The evaluation of a spin network is an integ…
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
problem Limited access to computational chemistry tools for large-language models.
method Integrating 18 expert-designed chemistry tools into an LLM (ChemCrow).
result ChemCrow autonomously plans and executes chemical syntheses and discoveries.
Optimized DMD for fast atmospheric chemistry forecasting.
problem Forecasting global atmospheric chemistry dynamics efficiently.
method Optimized Dynamic Mode Decomposition (DMD) for reduced order modeling.
result Significant improvement in computational speed and interpretability.
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
BERT learns molecular substructures for chemistry problems.
problem Predicting chemical properties and synthesizing molecules.
method Transformer-based BERT model on molecule string representations, attention visualization.
result BERT learns to represent functional groups and atoms for various chemical properties.
Study examines how taxes affect wealth inequality in economic models.
problem Reducing economic inequality in models of economic activity.
method Examined Artificial Chemistry models and various tax measures.
result Effective tax measures can reduce economic inequality.
We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of…
FSPA bypasses eigenvalue estimation for quantum PCA, achieving optimal complexity and robustness.
problem Quantum PCA eigenvalue estimation is computationally expensive and prone to errors.
method Filtered Spectral Projection Algorithm (FSPA) that projects onto the dominant spectral subspace directly.
result FSPA achieves optimal complexity and robustness, outperforming classical methods.
[New and updated results were published in Nature Chemistry, doi:10.1038/s41557-020-0544-y.] The electronic Schrödinger equation describes fundamental properties of molecules and materials, but can only be solved analytically for the hydrogen atom. The numerically exact full configuration-interaction method is exponent…
Graphs predict reaction conditions for organic chemistry.
problem Predicting specific reaction conditions in organic chemistry.
method Graph Neural Networks (GNNs) for modeling reaction graphs.
result GNNs can identify specific graph features affecting reaction conditions.