1
Similarity of materials and data-quality assessment by fingerprinting
2
Influence of spin-orbit coupling on chemical bonding
3
FAIR data enabling new horizons for materials research
4
Density-of-states similarity descriptor for unsupervised learning from materials data
5
Bayesian probabilistic assignment of chemical shifts in organic solids
6
An AI-toolkit to develop and share research into new materials
7
Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics
8
A machine learning vibrational spectroscopy protocol for spectrum prediction and spectrum-based structure recognition
9
Understanding creep of a single-crystalline Co-Al-W-Ta superalloy by studying the deformation mechanism, segregation tendency and stacking fault energy
10
Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence.
11
Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
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Learning the exchange-correlation functional from nature with fully differentiable density functional theory
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Predicting stable crystalline compounds using chemical similarity
14
Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
15
Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line
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Physics-Inspired Structural Representations for Molecules and Materials.
17
Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization
18
On-the-fly closed-loop materials discovery via Bayesian active learning
19
Finite-temperature materials modeling from the quantum nuclei to the hot electron regime
20
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
21
AFLOW-XtalFinder: a reliable choice to identify crystalline prototype
22
Supervised learning of few dirty bosons with variable particle number
23
Neural Network Potential Energy Surfaces for Small Molecules and Reactions.
24
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
25
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
26
Quantum machine learning using atom-in-molecule-based fragments selected on the fly
27
Multi-scale approach for the prediction of atomic scale properties
28
Numerical quality control for DFT-based materials databases
29
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces.
30
Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative
31
Cost-effective materials discovery: Bayesian optimization across multiple information sources
32
Teaching a neural network to attach and detach electrons from molecules
33
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening.
34
Interpretations of ground-state symmetry breaking and strong correlation in wavefunction and density functional theories
35
Machine Learning for Electronically Excited States of Molecules
36
Recursive evaluation and iterative contraction of N-body equivariant features.
37
Adaptive machine learning for efficient materials design
38
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
39
Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks.
40
Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics
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Scalable neural networks for the efficient learning of disordered quantum systems.
42
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation.
43
Active Learning a One-dimensional Density Functional Theory
44
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
45
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles.
46
Simulating disordered quantum systems via dense and sparse restricted Boltzmann machines
47
Quantum Chemistry in the Age of Machine Learning.
48
Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens.
49
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
50
A critical examination of compound stability predictions from machine-learned formation energies
51
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules
52
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
53
Exploring chemical compound space with quantum-based machine learning
54
Machine learning accurate exchange and correlation functionals of the electronic density
55
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
56
Machine learning for molecular simulation
58
Predicting materials properties without crystal structure: deep representation learning from stoichiometry
59
InterpretML: A Unified Framework for Machine Learning Interpretability
60
An open-source, end-to-end workflow for multidimensional photoemission spectroscopy
61
Deep-neural-network solution of the electronic Schrödinger equation
62
Tight-binding bond parameters for dimers across the periodic table from density-functional theory
63
Identifying domains of applicability of machine learning models for materials science
64
Electron density learning of non-covalent systems
65
Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
66
Machine Learning Interpretability: A Survey on Methods and Metrics
67
Data‐Driven Materials Science: Status, Challenges, and Perspectives
68
Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver.
69
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
70
Materials informatics for the screening of multi-principal elements and high-entropy alloys
71
Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery
72
Explainable Machine Learning for Scientific Insights and Discoveries
73
The NOMAD laboratory: from data sharing to artificial intelligence
74
Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy
75
Big Data-Driven Materials Science and Its FAIR Data Infrastructure
76
Machine Learning Optimization of the Collocation Point Set for Solving the Kohn-Sham Equation.
77
On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
78
A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery
79
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.
80
Completing density functional theory by machine learning hidden messages from molecules
81
Ab initio vibrational free energies including anharmonicity for multicomponent alloys
82
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
83
Solving the electronic structure problem with machine learning
84
Metallic glasses for biodegradable implants
85
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
86
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.
87
Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams
88
Definitions, methods, and applications in interpretable machine learning
89
Symbolic regression in materials science
90
Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds
91
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
92
Deep learning and density-functional theory
93
Coordination corrected ab initio formation enthalpies
94
Machine learning density functional theory for the Hubbard model
95
High-entropy high-hardness metal carbides discovered by entropy descriptors
96
Electronic structure based descriptor for characterizing local atomic environments
97
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
98
Accurate molecular polarizabilities with coupled cluster theory and machine learning
99
Supervised machine learning of ultracold atoms with speckle disorder
100
Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization
101
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians.
102
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
103
The AFLOW Library of Crystallographic Prototypes: Part 2
104
AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis
105
Explaining Explanations: An Overview of Interpretability of Machine Learning
106
Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis.
107
Machine Learning a General-Purpose Interatomic Potential for Silicon
108
Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning
109
Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon.
110
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
111
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy.
112
Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.
113
Microhartree precision in density functional theory calculations
114
Towards exact molecular dynamics simulations with machine-learned force fields
115
AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals.
116
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network.
117
Less is more: sampling chemical space with active learning
118
SchNet - A deep learning architecture for molecules and materials.
119
The AFLOW Fleet for Materials Discovery
120
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
121
The search for high entropy alloys: A high-throughput ab-initio approach
122
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.
123
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning.
124
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
125
Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles.
126
A multimode-like scheme for selecting the centers of Gaussian basis functions when computing vibrational spectra
127
Hierarchical modeling of molecular energies using a deep neural network.
128
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.
129
Applying a Smolyak collocation method to Cl2CO
130
Extensive deep neural networks for transferring small scale learning to large scale systems
131
Planning chemical syntheses with deep neural networks and symbolic AI
132
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
133
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
134
Neural Message Passing for Quantum Chemistry
135
Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
136
Multi-fidelity machine learning models for accurate bandgap predictions of solids
137
Towards A Rigorous Science of Interpretable Machine Learning
138
Deep learning and the Schrödinger equation
139
The Elephant in the Room of Density Functional Theory Calculations.
140
Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery
141
AFLUX: The LUX materials search API for the AFLOW data repositories
142
Uncovering structure-property relationships of materials by subgroup discovery
143
Active learning of linearly parametrized interatomic potentials
144
Perspective: Machine learning potentials for atomistic simulations.
145
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
146
Quantum-chemical insights from deep tensor neural networks
147
Designing Nanostructures for Phonon Transport via Bayesian Optimization
148
Bypassing the Kohn-Sham equations with machine learning
149
The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining
150
Modeling Off-Stoichiometry Materials with a High-Throughput Ab-Initio Approach
151
Universal fragment descriptors for predicting properties of inorganic crystals
152
Understanding Probabilistic Sparse Gaussian Process Approximations
153
Solving the quantum many-body problem with artificial neural networks
154
Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
155
Accurate all-electron G 0 W 0 quasiparticle energies employing the full-potential augmented plane-wave method
156
Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface.
157
Three-Parameter Crystal-Structure Prediction for sp-d-Valent Compounds
158
Reproducibility in density functional theory calculations of solids
159
The FAIR Guiding Principles for scientific data management and stewardship
160
The ReaxFF reactive force-field: development, applications and future directions
161
Automated Selection of Active Orbital Spaces.
162
Adaptive Strategies for Materials Design using Uncertainties
163
Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals.
164
Strongly Constrained and Appropriately Normed Semilocal Density Functional.
165
Crystal structure representations for machine learning models of formation energies
166
Explicitly correlated MRCI-F12 potential energy surfaces for methane fit with several permutation invariant schemes and full-dimensional vibrational calculations
167
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
168
Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
169
Big data of materials science: critical role of the descriptor.
170
Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices.
171
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
172
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
173
Communication: favorable dimensionality scaling of rectangular collocation with adaptable basis functions up to 7 dimensions.
174
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
176
On representing chemical environments
177
AFLOW: An automatic framework for high-throughput materials discovery
178
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
179
Finding Density Functionals with Machine Learning
180
One-dimensional continuum electronic structure with the density-matrix renormalization group and its implications for density-functional theory.
181
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
182
Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
183
Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
184
Crystal structure prediction from first principles.
185
Generalized neural-network representation of high-dimensional potential-energy surfaces.
186
Using neural networks to represent potential surfaces as sums of products.
187
An exploration of aspects of Bayesian multiple testing
188
Bayesian error estimation in density-functional theory.
189
Rapid access to infrared reference spectra of arbitrary organic compounds: scope and limitations of an approach to the simulation of infrared spectra by neural networks
190
Artificial neural network prediction of the band gap and melting point of binary and ternary compound semiconductors
191
Generalized Gradient Approximation Made Simple [Phys. Rev. Lett. 77, 3865 (1996)]
192
Generalized Gradient Approximation Made Simple.
193
Neural network models of potential energy surfaces
194
Interatomic Potentials from First-Principles Calculations: The Force-Matching Method
195
A chemical scale for crystal-structure maps
196
Self-Consistent Equations Including Exchange and Correlation Effects
197
Deep integration of machine learning into computational chemistry and materials science
198
Orbital-Free Density Functional Theory with Small Datasets and Deep Learning
199
Deep learning approach for Raman spectroscopy Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization
200
On representing (anti)symmetric functions (arXiv:2007.15298
201
Reproducibility in G0W0 Calculations for Solids
202
Cormorant: covariant molecular neural networks Advances in Neural Information Processing Systems
203
A and Aspuru-Guzik A 2019 2nd Workshop on Machine Learning and the Physical Sciences NeurIPS (Vancouver, Canada
204
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery
205
The mythos of model interpretability Queue
206
The bitter lesson http://incompleteideas.net/IncIdeas/BitterLesson.html
207
Bayesian optimization for materials design Information Science for Materials Discovery and Design ed
208
A and Aspuru-Guzik A 2020
209
Information Theory, Inference, and Learning Algorithms
210
The Supervised Learning No-Free-Lunch Theorems
211
The multiconfiguration time-dependent Hartree (MCTDH) method: A highly efficient algorithm for propa
212
Reinforcement Learning: An Introduction
213
Regression Shrinkage and Selection via the Lasso
214
The calculation of atomic fields Math
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Data from the NOMAD Laboratory [187]; exciting data
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References (separate from the two page limit)
