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Found 51 papers, 33 papers with code
Atom-by-atom protein generation and beyond with language models
However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files
In doing so, we demonstrate that it is not necessary to use simplified molecular representations to train chemical language models -- that they are powerful generative models capable of directly exploring chemical space in three dimensions for very different structures.
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Waveflow: Enforcing boundary conditions in smooth normalizing flows with application to fermionic wave functions
In this paper, we introduce four main novelties: First, we present a new way of handling the topology problem of normalizing flows.
Group SELFIES: A Robust Fragment-Based Molecular String Representation
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees.
Quantum compression with classically simulatable circuits
As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable.
On scientific understanding with artificial intelligence
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Bayesian optimization with known experimental and design constraints for chemistry applications
The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
We introduce a novel RL framework for scalable 3D design that uses a hierarchical agent to build molecules by placing molecular substructures sequentially in 3D space, thus attempting to build on the existing human knowledge in the field of molecular design.
Keeping it Simple: Language Models can learn Complex Molecular Distributions
In this work, we investigate the capacity of simple language models to learn distributions of molecules.
Learning quantum dynamics with latent neural ODEs
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question.
Design of quantum optical experiments with logic artificial intelligence
In this work, we propose the use of logic AI for the design of optical quantum experiments.
Golem: An algorithm for robust experiment and process optimization
Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values.
Assigning Confidence to Molecular Property Prediction
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.
Noisy intermediate-scale quantum (NISQ) algorithms
We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.
Natural Evolutionary Strategies for Variational Quantum Computation
We implement two specific approaches, the exponential and separable natural evolutionary strategies, for parameter optimization of PQCs and compare them against standard gradient descent.
A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches based on the direct application of the parameter-shift-rule come with an associated cost of O(2^(2n)) expectation values.
Quantum Physics Chemical Physics Computational Physics
Tequila: A platform for rapid development of quantum algorithms
As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in high demand for flexible and reliable ways to implement, test, and share new ideas.
Quantum Physics Chemical Physics Computational Physics
Bayesian Variational Optimization for Combinatorial Spaces
This paper focuses on Bayesian Optimization in combinatorial spaces.
Olympus: a benchmarking framework for noisy optimization and experiment planning
Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.
The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian.
Quantum Physics
Reducing qubit requirements while maintaining numerical precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach
We present a basis-set-free approach to the variational quantum eigensolver using an adaptive representation of the spatial part of molecular wavefunctions.
Quantum Physics Chemical Physics Computational Physics
Quantum Computer-Aided design of Quantum Optics Hardware
It is not clear how the full potential of large quantum systems can be exploited.
Quantum Physics Computational Physics Optics
Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise.
Conceptual understanding through efficient inverse-design of quantum optical experiments
Here we present Theseus, an efficient algorithm for the design of quantum experiments, which we use to solve several open questions in experimental quantum optics.
Quantum Physics Optics
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials.
Automated discovery of superconducting circuits and its application to 4-local coupler design
Superconducting circuits have emerged as a promising platform to build quantum processors.
Quantum Physics
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization problems.
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems
Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards multi-component active layer blends.
Applied Physics
An Artificial Spiking Quantum Neuron
Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing.
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.
Quantum Chemistry in the Age of Quantum Computing
Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century.
Quantum Physics
Variational Quantum Factoring
In this work, we revisit the problem of factoring, developing an alternative to Shor's algorithm, which employs established techniques to map the factoring problem to the ground state of an Ising Hamiltonian.
Quantum Physics
PHOENICS: A universal deep Bayesian optimizer
In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.
Quantum Neuron: an elementary building block for machine learning on quantum computers
In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately.
Automatic differentiation in quantum chemistry with an application to fully variational Hartree-Fock
Automatic Differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions.
Chemical Physics
QVECTOR: an algorithm for device-tailored quantum error correction
Current approaches to fault-tolerant quantum computation will not enable useful quantum computation on near-term devices of 50 to 100 qubits.
Quantum Physics
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
These results show that PDTS is a successful solution for large-scale parallel BO.
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics.
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Automatic chemical design using a data-driven continuous representation of molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.
Neural networks for the prediction organic chemistry reactions
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning.
qHiPSTER: The Quantum High Performance Software Testing Environment
We present qHiPSTER, the Quantum High Performance Software Testing Environment.
Quantum Physics Distributed, Parallel, and Cluster Computing
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on graphs.
Ranked #2 on
Drug Discovery
on HIV dataset
Exploiting locality in quantum computation for quantum chemistry
Accurate prediction of chemical and material properties from first principles quantum chemistry is a challenging task on traditional computers.
Quantum Physics Chemical Physics
A variational eigenvalue solver on a quantum processor
Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer.
Quantum Physics Chemical Physics
